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Reporting Research Results in APA Style | Tips & Examples

Published on December 21, 2020 by Pritha Bhandari . Revised on January 17, 2024.

The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.

The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences.

Use these standards to answer your research questions and report your data analyses in a complete and transparent way.

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Table of contents

What goes in your results section, introduce your data, summarize your data, report statistical results, presenting numbers effectively, what doesn’t belong in your results section, frequently asked questions about results in apa.

In APA style, the results section includes preliminary information about the participants and data, descriptive and inferential statistics, and the results of any exploratory analyses.

Include these in your results section:

  • Participant flow and recruitment period. Report the number of participants at every stage of the study, as well as the dates when recruitment took place.
  • Missing data . Identify the proportion of data that wasn’t included in your final analysis and state the reasons.
  • Any adverse events. Make sure to report any unexpected events or side effects (for clinical studies).
  • Descriptive statistics . Summarize the primary and secondary outcomes of the study.
  • Inferential statistics , including confidence intervals and effect sizes. Address the primary and secondary research questions by reporting the detailed results of your main analyses.
  • Results of subgroup or exploratory analyses, if applicable. Place detailed results in supplementary materials.

Write up the results in the past tense because you’re describing the outcomes of a completed research study.

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presentation of results in a research paper

Before diving into your research findings, first describe the flow of participants at every stage of your study and whether any data were excluded from the final analysis.

Participant flow and recruitment period

It’s necessary to report any attrition, which is the decline in participants at every sequential stage of a study. That’s because an uneven number of participants across groups sometimes threatens internal validity and makes it difficult to compare groups. Be sure to also state all reasons for attrition.

If your study has multiple stages (e.g., pre-test, intervention, and post-test) and groups (e.g., experimental and control groups), a flow chart is the best way to report the number of participants in each group per stage and reasons for attrition.

Also report the dates for when you recruited participants or performed follow-up sessions.

Missing data

Another key issue is the completeness of your dataset. It’s necessary to report both the amount and reasons for data that was missing or excluded.

Data can become unusable due to equipment malfunctions, improper storage, unexpected events, participant ineligibility, and so on. For each case, state the reason why the data were unusable.

Some data points may be removed from the final analysis because they are outliers—but you must be able to justify how you decided what to exclude.

If you applied any techniques for overcoming or compensating for lost data, report those as well.

Adverse events

For clinical studies, report all events with serious consequences or any side effects that occured.

Descriptive statistics summarize your data for the reader. Present descriptive statistics for each primary, secondary, and subgroup analysis.

Don’t provide formulas or citations for commonly used statistics (e.g., standard deviation) – but do provide them for new or rare equations.

Descriptive statistics

The exact descriptive statistics that you report depends on the types of data in your study. Categorical variables can be reported using proportions, while quantitative data can be reported using means and standard deviations . For a large set of numbers, a table is the most effective presentation format.

Include sample sizes (overall and for each group) as well as appropriate measures of central tendency and variability for the outcomes in your results section. For every point estimate , add a clearly labelled measure of variability as well.

Be sure to note how you combined data to come up with variables of interest. For every variable of interest, explain how you operationalized it.

According to APA journal standards, it’s necessary to report all relevant hypothesis tests performed, estimates of effect sizes, and confidence intervals.

When reporting statistical results, you should first address primary research questions before moving onto secondary research questions and any exploratory or subgroup analyses.

Present the results of tests in the order that you performed them—report the outcomes of main tests before post-hoc tests, for example. Don’t leave out any relevant results, even if they don’t support your hypothesis.

Inferential statistics

For each statistical test performed, first restate the hypothesis , then state whether your hypothesis was supported and provide the outcomes that led you to that conclusion.

Report the following for each hypothesis test:

  • the test statistic value,
  • the degrees of freedom ,
  • the exact p- value (unless it is less than 0.001),
  • the magnitude and direction of the effect.

When reporting complex data analyses, such as factor analysis or multivariate analysis, present the models estimated in detail, and state the statistical software used. Make sure to report any violations of statistical assumptions or problems with estimation.

Effect sizes and confidence intervals

For each hypothesis test performed, you should present confidence intervals and estimates of effect sizes .

Confidence intervals are useful for showing the variability around point estimates. They should be included whenever you report population parameter estimates.

Effect sizes indicate how impactful the outcomes of a study are. But since they are estimates, it’s recommended that you also provide confidence intervals of effect sizes.

Subgroup or exploratory analyses

Briefly report the results of any other planned or exploratory analyses you performed. These may include subgroup analyses as well.

Subgroup analyses come with a high chance of false positive results, because performing a large number of comparison or correlation tests increases the chances of finding significant results.

If you find significant results in these analyses, make sure to appropriately report them as exploratory (rather than confirmatory) results to avoid overstating their importance.

While these analyses can be reported in less detail in the main text, you can provide the full analyses in supplementary materials.

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To effectively present numbers, use a mix of text, tables , and figures where appropriate:

  • To present three or fewer numbers, try a sentence ,
  • To present between 4 and 20 numbers, try a table ,
  • To present more than 20 numbers, try a figure .

Since these are general guidelines, use your own judgment and feedback from others for effective presentation of numbers.

Tables and figures should be numbered and have titles, along with relevant notes. Make sure to present data only once throughout the paper and refer to any tables and figures in the text.

Formatting statistics and numbers

It’s important to follow capitalization , italicization, and abbreviation rules when referring to statistics in your paper. There are specific format guidelines for reporting statistics in APA , as well as general rules about writing numbers .

If you are unsure of how to present specific symbols, look up the detailed APA guidelines or other papers in your field.

It’s important to provide a complete picture of your data analyses and outcomes in a concise way. For that reason, raw data and any interpretations of your results are not included in the results section.

It’s rarely appropriate to include raw data in your results section. Instead, you should always save the raw data securely and make them available and accessible to any other researchers who request them.

Making scientific research available to others is a key part of academic integrity and open science.

Interpretation or discussion of results

This belongs in your discussion section. Your results section is where you objectively report all relevant findings and leave them open for interpretation by readers.

While you should state whether the findings of statistical tests lend support to your hypotheses, refrain from forming conclusions to your research questions in the results section.

Explanation of how statistics tests work

For the sake of concise writing, you can safely assume that readers of your paper have professional knowledge of how statistical inferences work.

In an APA results section , you should generally report the following:

  • Participant flow and recruitment period.
  • Missing data and any adverse events.
  • Descriptive statistics about your samples.
  • Inferential statistics , including confidence intervals and effect sizes.
  • Results of any subgroup or exploratory analyses, if applicable.

According to the APA guidelines, you should report enough detail on inferential statistics so that your readers understand your analyses.

  • the test statistic value
  • the degrees of freedom
  • the exact p value (unless it is less than 0.001)
  • the magnitude and direction of the effect

You should also present confidence intervals and estimates of effect sizes where relevant.

In APA style, statistics can be presented in the main text or as tables or figures . To decide how to present numbers, you can follow APA guidelines:

  • To present three or fewer numbers, try a sentence,
  • To present between 4 and 20 numbers, try a table,
  • To present more than 20 numbers, try a figure.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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How to Write the Results/Findings Section in Research

presentation of results in a research paper

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

  • Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
  • A contextual analysis of this data explaining its meaning in sentence form
  • All data that corresponds to the central research question(s)
  • All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

results section of a research paper, figures

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

results section of a research paper, figures

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

results section of a research paper, figures

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

  • “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

  • The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
  • Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
  • Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

  • Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
  • Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
  • Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

  • Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
  • Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
  • Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

  • The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
  • In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
  • Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
  • Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

  • Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
  • Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
  • Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
  • Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

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Research Results

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

  • Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
  • Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
  • Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
  • Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
  • Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
  • Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
  • Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

  • A brief overview of the research objectives and hypotheses
  • A statement of the research question

II. Descriptive statistics

  • Summary statistics (e.g., mean, standard deviation) for each variable analyzed
  • Frequencies and percentages for categorical variables

III. Inferential statistics

  • Results of statistical analyses, including tests of hypotheses
  • Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

  • Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
  • Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

  • Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

  • Discussion of any limitations of the study and potential sources of bias
  • Assumptions made in the statistical analyses

VII. Conclusions

  • A summary of the key findings and their implications
  • A statement of whether the hypotheses were supported or not
  • Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

  • This study sought to examine the relationship between sleep quality and academic performance in college students.
  • Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
  • Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

  • Participants were college students (N=200) from a mid-sized public university in the United States.
  • The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
  • Participants were recruited through flyers and online advertisements.

III. Results

  • Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
  • See Table 1 for a summary of the results.
  • Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

  • The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
  • These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
  • Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

  • College students who prioritize sleep may see a positive impact on their academic performance.
  • These findings highlight the importance of sleep in academic success.
  • Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

  • Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
  • Developing theories: Research results can help to develop or modify existing theories and create new ones.
  • Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
  • Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
  • Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
  • Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

  • Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
  • Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
  • Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
  • Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
  • Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
  • Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
  • Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
  • Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

  • After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
  • Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
  • Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
  • Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
  • Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
  • The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
  • The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
  • Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
  • Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
  • Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

  • Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
  • Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
  • Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
  • Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
  • Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
  • Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
  • Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
  • Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
  • Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

  • Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
  • Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
  • Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
  • Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

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University of Northern Iowa Home

  • Chapter Seven: Presenting Your Results

This chapter serves as the culmination of the previous chapters, in that it focuses on how to present the results of one's study, regardless of the choice made among the three methods. Writing in academics has a form and style that you will want to apply not only to report your own research, but also to enhance your skills at reading original research published in academic journals. Beyond the basic academic style of report writing, there are specific, often unwritten assumptions about how quantitative, qualitative, and critical/rhetorical studies should be organized and the information they should contain. This chapter discusses how to present your results in writing, how to write accessibly, how to visualize data, and how to present your results in person.  

  • Chapter One: Introduction
  • Chapter Two: Understanding the distinctions among research methods
  • Chapter Three: Ethical research, writing, and creative work
  • Chapter Four: Quantitative Methods (Part 1)
  • Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
  • Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
  • Chapter Five: Qualitative Methods (Part 1)
  • Chapter Five: Qualitative Data (Part 2)
  • Chapter Six: Critical / Rhetorical Methods (Part 1)
  • Chapter Six: Critical / Rhetorical Methods (Part 2)

Written Presentation of Results

Once you've gone through the process of doing communication research – using a quantitative, qualitative, or critical/rhetorical methodological approach – the final step is to  communicate  it.

The major style manuals (the APA Manual, the MLA Handbook, and Turabian) are very helpful in documenting the structure of writing a study, and are highly recommended for consultation. But, no matter what style manual you may use, there are some common elements to the structure of an academic communication research paper.

Title Page :

This is simple: Your Paper's Title, Your Name, Your Institutional Affiliation (e.g., University), and the Date, each on separate lines, centered on the page. Try to make your title both descriptive (i.e., it gives the reader an idea what the study is about) and interesting (i.e., it is catchy enough to get one's attention).

For example, the title, "The uncritical idealization of a compensated psychopath character in a popular book series," would not be an inaccurate title for a published study, but it is rather vague and exceedingly boring. That study's author fortunately chose the title, "A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight ," which is more precisely descriptive, and much more interesting (Merskin, 2011). The use of the colon in academic titles can help authors accomplish both objectives: a catchy but relevant phrase, followed by a more clear explanation of the article's topic.

In some instances, you might be asked to write an abstract, which is a summary of your paper that can range in length from 75 to 250 words. If it is a published paper, it is useful to include key search terms in this brief description of the paper (the title may already have a few of these terms as well). Although this may be the last thing your write, make it one of the best things you write, because this may be the first thing your audience reads about the paper (and may be the only thing read if it is written badly). Summarize the problem/research question, your methodological approach, your results and conclusions, and the significance of the paper in the abstract.

Quantitative and qualitative studies will most typically use the rest of the section titles noted below. Critical/rhetorical studies will include many of the same steps, but will often have different headings. For example, a critical/rhetorical paper will have an introduction, definition of terms, and literature review, followed by an analysis (often divided into sections by areas of investigation) and ending with a conclusion/implications section. Because critical/rhetorical research is much more descriptive, the subheadings in such a paper are often times not generic subheads like "literature review," but instead descriptive subheadings that apply to the topic at hand, as seen in the schematic below. Because many journals expect the article to follow typical research paper headings of introduction, literature review, methods, results, and discussion, we discuss these sections briefly next.

Image removed.

Introduction:

As you read social scientific journals (see chapter 1 for examples), you will find that they tend to get into the research question quickly and succinctly. Journal articles from the humanities tradition tend to be more descriptive in the introduction. But, in either case, it is good to begin with some kind of brief anecdote that gets the reader engaged in your work and lets the reader understand why this is an interesting topic. From that point, state your research question, define the problem (see Chapter One) with an overview of what we do and don't know, and finally state what you will do, or what you want to find out. The introduction thus builds the case for your topic, and is the beginning of building your argument, as we noted in chapter 1.

By the end of the Introduction, the reader should know what your topic is, why it is a significant communication topic, and why it is necessary that you investigate it (e.g., it could be there is gap in literature, you will conduct valuable exploratory research, or you will provide a new model for solving some professional or social problem).

Literature Review:

The literature review summarizes and organizes the relevant books, articles, and other research in this area. It sets up both quantitative and qualitative studies, showing the need for the study. For critical/rhetorical research, the literature review often incorporates the description of the historical context and heuristic vocabulary, with key terms defined in this section of the paper. For more detail on writing a literature review, see Appendix 1.

The methods of your paper are the processes that govern your research, where the researcher explains what s/he did to solve the problem. As you have seen throughout this book, in communication studies, there are a number of different types of research methods. For example, in quantitative research, one might conduct surveys, experiments, or content analysis. In qualitative research, one might instead use interviews and observations. Critical/rhetorical studies methods are more about the interpretation of texts or the study of popular culture as communication. In creative communication research, the method may be an interpretive performance studies or filmmaking. Other methods used sometimes alone, or in combination with other methods, include legal research, historical research, and political economy research.

In quantitative and qualitative research papers, the methods will be most likely described according to the APA manual standards. At the very least, the methods will include a description of participants, data collection, and data analysis, with specific details on each of these elements. For example, in an experiment, the researcher will describe the number of participants, the materials used, the design of the experiment, the procedure of the experiment, and what statistics will be used to address the hypotheses/research questions.

Critical/rhetorical researchers rarely have a specific section called "methods," as opposed to quantitative and qualitative researchers, but rather demonstrate the method they use for analysis throughout the writing of their piece.

Helping your reader understand the methods you used for your study is important not only for your own study's credibility, but also for possible replication of your study by other researchers. A good guideline to keep in mind is  transparency . You want to be as clear as possible in describing the decisions you made in designing your study, gathering and analyzing your data so that the reader can retrace your steps and understand how you came to the conclusions you formed. A research study can be very good, but if it is not clearly described so that others can see how the results were determined or obtained, then the quality of the study and its potential contributions are lost.

After you completed your study, your findings will be listed in the results section. Particularly in a quantitative study, the results section is for revisiting your hypotheses and reporting whether or not your results supported them, and the statistical significance of the results. Whether your study supported or contradicted your hypotheses, it's always helpful to fully report what your results were. The researcher usually organizes the results of his/her results section by research question or hypothesis, stating the results for each one, using statistics to show how the research question or hypothesis was answered in the study.

The qualitative results section also may be organized by research question, but usually is organized by themes which emerged from the data collected. The researcher provides rich details from her/his observations and interviews, with detailed quotations provided to illustrate the themes identified. Sometimes the results section is combined with the discussion section.

Critical/rhetorical researchers would include their analysis often with different subheadings in what would be considered a "results" section, yet not labeled specifically this way.

Discussion:

In the discussion section, the researcher gives an appraisal of the results. Here is where the researcher considers the results, particularly in light of the literature review, and explains what the findings mean. If the results confirmed or corresponded with the findings of other literature, then that should be stated. If the results didn't support the findings of previous studies, then the researcher should develop an explanation of why the study turned out this way. Sometimes, this section is called a "conclusion" by researchers.

References:

In this section, all of the literature cited in the text should have full references in alphabetical order. Appendices: Appendix material includes items like questionnaires used in the study, photographs, documents, etc. An alphabetical letter is assigned for each piece (e.g. Appendix A, Appendix B), with a second line of title describing what the appendix contains (e.g. Participant Informed Consent, or  New York Times  Speech Coverage). They should be organized consistently with the order in which they are referenced in the text of the paper. The page numbers for appendices are consecutive with the paper and reference list.

Tables/Figures:

Tables and figures are referenced in the text, but included at the end of the study and numbered consecutively. (Check with your professor; some like to have tables and figures inserted within the paper's main text.) Tables generally are data in a table format, whereas figures are diagrams (such as a pie chart) and drawings (such as a flow chart).

Accessible Writing

As you may have noticed, academic writing does have a language (e.g., words like heuristic vocabulary and hypotheses) and style (e.g., literature reviews) all its own. It is important to engage in that language and style, and understand how to use it to  communicate effectively in an academic context . Yet, it is also important to remember that your analyses and findings should also be written to be accessible. Writers should avoid excessive jargon, or—even worse—deploying jargon to mask an incomplete understanding of a topic.

The scourge of excessive jargon in academic writing was the target of a famous hoax in 1996. A New York University physics professor submitted an article, " Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity ," to a special issue of the academic journal  Social Text  devoted to science and postmodernism. The article was designed to point out how dense academic jargon can sometimes mask sloppy thinking. As the professor, Alan Sokal, had expected, the article was published. One sample sentence from the article reads:

It has thus become increasingly apparent that physical "reality", no less than social "reality", is at bottom a social and linguistic construct; that scientific "knowledge", far from being objective, reflects and encodes the dominant ideologies and power relations of the culture that produced it; that the truth claims of science are inherently theory-laden and self-referential; and consequently, that the discourse of the scientific community, for all its undeniable value, cannot assert a privileged epistemological status with respect to counter-hegemonic narratives emanating from dissident or marginalized communities. (Sokal, 1996. pp. 217-218)

According to the journal's editor, about six reviewers had read the article but didn't suspect that it was phony. A public debate ensued after Sokal revealed his hoax. Sokal said he worried that jargon and intellectual fads cause academics to lose contact with the real world and "undermine the prospect for progressive social critique" ( Scott, 1996 ). The APA Manual recommends to avoid using technical vocabulary where it is not needed or relevant or if the technical language is overused, thus becoming jargon. In short, the APA argues that "scientific jargon...grates on the reader, encumbers the communication of information, and wastes space" (American Psychological Association, 2010, p. 68).

Data Visualization

Images and words have long existed on the printed page of manuscripts, yet, until recently, relatively few researchers possessed the resources to effectively combine images combined with words (Tufte, 1990, 1983). Communication scholars are only now becoming aware of this dimension in research as computer technologies have made it possible for many people to produce and publish multimedia presentations.

Although visuals may seem to be anathema to the primacy of the written word in research, they are a legitimate way, and at times the best way, to present ideas. Visual scholar Lester Faigley et al. (2004) explains how data visualizations have become part of our daily lives:

Visualizations can shed light on research as well. London-based David McCandless specializes in visualizing interesting research questions, or in his words "the questions I wanted answering" (2009, p. 7). His images include a graph of the  peak times of the year for breakups  (based on Facebook status updates), a  radiation dosage chart , and some  experiments with the Google Ngram Viewer , which charts the appearance of keywords in millions of books over hundreds of years.

The  public domain image  below creatively maps U.S. Census data of the outflow of people from California to other states between 1995 and 2000.

Image removed.

Visualizing one's research is possible in multiple ways. A simple technology, for example, is to enter data into a spreadsheet such as Excel, and select  Charts  or  SmartArt  to generate graphics. A number of free web tools can also transform raw data into useful charts and graphs.  Many Eyes , an open source data visualization tool (sponsored by IBM Research), says its goal "is to 'democratize' visualization and to enable a new social kind of data analysis" (IBM, 2011). Another tool,  Soundslides , enables users to import images and audio to create a photographic slideshow, while the program handles all of the background code. Other tools, often open source and free, can help visual academic research into interactive maps; interactive, image-based timelines; interactive charts; and simple 2-D and 3-D animations. Adobe Creative Suite (which includes popular software like Photoshop) is available on most computers at universities, but open source alternatives exist as well.  Gimp  is comparable to Photoshop, and it is free and relatively easy to use.

One online performance studies journal,  Liminalities , is an excellent example of how "research" can be more than just printed words. In each issue, traditional academic essays and book reviews are often supported photographs, while other parts of an issue can include video, audio, and multimedia contributions. The journal, founded in 2005, treats performance itself as a methodology, and accepts contribution in html, mp3, Quicktime, and Flash formats.

For communication researchers, there is also a vast array of visual digital archives available online. Many of these archives are located at colleges and universities around the world, where digital librarians are spearheading a massive effort to make information—print, audio, visual, and graphic—available to the public as part of a global information commons. For example, the University of Iowa has a considerable digital archive including historical photos documenting American railroads and a database of images related to geoscience. The University of Northern Iowa has a growing Special Collections Unit that includes digital images of every UNI Yearbook between 1905 and 1923 and audio files of UNI jazz band performances. Researchers at he University of Michigan developed  OAIster , a rich database that has joined thousands of digital archives in one searchable interface. Indeed, virtually every academic library is now digitizing all types of media, not just texts, and making them available for public viewing and, when possible, for use in presenting research. In addition to academic collections, the  Library of Congress  and the  National Archives  offer an ever-expanding range of downloadable media; commercial, user-generated databases such as Flickr, Buzznet, YouTube and Google Video offer a rich resource of images that are often free of copyright constraints (see Chapter 3 about Creative Commons licenses) and nonprofit endeavors, such as the  Internet Archive , contain a formidable collection of moving images, still photographs, audio files (including concert recordings), and open source software.

Presenting your Work in Person

As Communication students, it's expected that you are not only able to communicate your research project in written form but also in person.

Before you do any oral presentation, it's good to have a brief "pitch" ready for anyone who asks you about your research. The pitch is routine in Hollywood: a screenwriter has just a few minutes to present an idea to a producer. Although your pitch will be more sophisticated than, say, " Snakes on a Plane " (which unfortunately was made into a movie), you should in just a few lines be able to explain the gist of your research to anyone who asks. Developing this concise description, you will have some practice in distilling what might be a complicated topic into one others can quickly grasp.

Oral presentation

In most oral presentations of research, whether at the end of a semester, or at a research symposium or conference, you will likely have just 10 to 20 minutes. This is probably not enough time to read the entire paper aloud, which is not what you should do anyway if you want people to really listen (although, unfortunately some make this mistake). Instead, the point of the presentation should be to present your research in an interesting manner so the listeners will want to read the whole thing. In the presentation, spend the least amount of time on the literature review (a very brief summary will suffice) and the most on your own original contribution. In fact, you may tell your audience that you are only presenting on one portion of the paper, and that you would be happy to talk more about your research and findings in the question and answer session that typically follows. Consider your presentation the beginning of a dialogue between you and the audience. Your tone shouldn't be "I have found everything important there is to find, and I will cram as much as I can into this presentation," but instead "I found some things you will find interesting, but I realize there is more to find."

Turabian (2007) has a helpful chapter on presenting research. Most important, she emphasizes, is to remember that your audience members are listeners, not readers. Thus, recall the lessons on speech making in your college oral communication class. Give an introduction, tell them what the problem is, and map out what you will present to them. Organize your findings into a few points, and don't get bogged down in minutiae. (The minutiae are for readers to find if they wish, not for listeners to struggle through.) PowerPoint slides are acceptable, but don't read them. Instead, create an outline of a few main points, and practice your presentation.

Turabian  suggests an introduction of not more than three minutes, which should include these elements:

  • The research topic you will address (not more than a minute).
  • Your research question (30 seconds or less)
  • An answer to "so what?" – explaining the relevance of your research (30 seconds)
  • Your claim, or argument (30 seconds or less)
  • The map of your presentation structure (30 seconds or less)

As Turabian (2007) suggests, "Rehearse your introduction, not only to get it right, but to be able to look your audience in the eye as you give it. You can look down at notes later" (p. 125).

Poster presentation

In some symposiums and conferences, you may be asked to present at a "poster" session. Instead of presenting on a panel of 4-5 people to an audience, a poster presenter is with others in a large hall or room, and talks one-on-one with visitors who look at the visual poster display of the research. As in an oral presentation, a poster highlights just the main point of the paper. Then, if visitors have questions, the author can informally discuss her/his findings.

To attract attention, poster presentations need to be nicely designed, or in the words of an advertising professor who schedules poster sessions at conferences, "be big, bold, and brief" ( Broyles , 2011). Large type (at least 18 pt.), graphics, tables, and photos are recommended.

Image removed.

A poster presentation session at a conference, by David Eppstein (Own work) [CC-BY-SA-3.0 ( www.creativecommons.org/licenses/by-sa/3.0 )], via Wikimedia Commons]

The Association for Education in Journalism and Mass Communication (AEJMC) has a  template for making an effective poster presentation . Many universities, copy shops, and Internet services also have large-scale printers, to print full-color research poster designs that can be rolled up and transported in a tube.

Judging Others' Research

After taking this course, you should have a basic knowledge of research methods. There will still be some things that may mystify you as a reader of other's research. For example, you may not be able to interpret the coefficients for statistical significance, or make sense of a complex structural equation. Some specialized vocabulary may still be difficult.

But, you should understand how to critically review research. For example, imagine you have been asked to do a blind (i.e., the author's identity is concealed) "peer review" of communication research for acceptance to a conference, or publication in an academic journal. For most  conferences  and  journals , submissions are made online, where editors can manage the flow and assign reviews to papers. The evaluations reviewers make are based on the same things that we have covered in this book. For example, the conference for the AEJMC ask reviewers to consider (on a five-point scale, from Excellent to Poor) a number of familiar research dimensions, including the paper's clarity of purpose, literature review, clarity of research method, appropriateness of research method, evidence presented clearly, evidence supportive of conclusions, general writing and organization, and the significance of the contribution to the field.

Beyond academia, it is likely you will more frequently apply the lessons of research methods as a critical consumer of news, politics, and everyday life. Just because some expert cites a number or presents a conclusion doesn't mean it's automatically true. John Allen Paulos, in his book  A Mathematician reads the newspaper , suggests some basic questions we can ask. "If statistics were presented, how were they obtained? How confident can we be of them? Were they derived from a random sample or from a collection of anecdotes? Does the correlation suggest a causal relationship, or is it merely a coincidence?" (1997, p. 201).

Through the study of research methods, we have begun to build a critical vocabulary and understanding to ask good questions when others present "knowledge." For example, if Candidate X won a straw poll in Iowa, does that mean she'll get her party's nomination? If Candidate Y wins an open primary in New Hampshire, does that mean he'll be the next president? If Candidate Z sheds a tear, does it matter what the context is, or whether that candidate is a man or a woman? What we learn in research methods about validity, reliability, sampling, variables, research participants, epistemology, grounded theory, and rhetoric, we can consider whether the "knowledge" that is presented in the news is a verifiable fact, a sound argument, or just conjecture.

American Psychological Association (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.

Broyles, S. (2011). "About poster sessions." AEJMC.  http://www.aejmc.org/home/2013/01/about-poster-sessions/ .

Faigley, L., George, D., Palchik, A., Selfe, C. (2004).  Picturing texts . New York: W.W. Norton & Company.

IBM (2011). Overview of Many Eyes.  http://www.research.ibm.com/social/projects_manyeyes.shtml .

McCandless, D. (2009).  The visual miscellaneum . New York: Collins Design.

Merskin, D. (2011). A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight. Journal of Communication Inquiry  35: 157-178. doi:10.1177/0196859911402992

Paulos, J. A. (1997).  A mathematician reads the newspaper . New York: Anchor.

Scott, J. (1996, May 18). Postmodern gravity deconstructed, slyly.  New York Times , http://www.nytimes.com/books/98/11/15/specials/sokal-text.html .

Sokal, A. (1996). Transgressing the boundaries: towards a transformative hermeneutics of quantum gravity.  Social Text  46/47, 217-252.

Tufte, E. R. (1990).  Envisioning information . Cheshire, CT: Graphics Press.

Tufte, E. R. (1983).  The visual display of quantitative information . Cheshire, CT: Graphics Press.

Turabian, Kate L. (2007).  A manual for writers of research papers, theses, and dissertations: Chicago style guide for students and researchers  (7th ed.). Chicago: University of Chicago Press.

presentation of results in a research paper

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

presentation of results in a research paper

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

  • Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

presentation of results in a research paper

  • Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

  • Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

  • Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

  • Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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Writing a scientific paper.

  • Writing a lab report
  • INTRODUCTION

Writing a "good" results section

Figures and Captions in Lab Reports

"Results Checklist" from: How to Write a Good Scientific Paper. Chris A. Mack. SPIE. 2018.

Additional tips for results sections.

  • LITERATURE CITED
  • Bibliography of guides to scientific writing and presenting
  • Peer Review
  • Presentations
  • Lab Report Writing Guides on the Web

This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.

  • Factual statements supported by evidence. Short and sweet without excess words
  • Present representative data rather than endlessly repetitive data
  • Discuss variables only if they had an effect (positive or negative)
  • Use meaningful statistics
  • Avoid redundancy. If it is in the tables or captions you may not need to repeat it

A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota

  • Present the results of the paper, in logical order, using tables and graphs as necessary.
  • Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. 
  • Avoid: presenting results that are never discussed;  presenting results in chronological order rather than logical order; ignoring results that do not support the conclusions; 
  • Number tables and figures separately beginning with 1 (i.e. Table 1, Table 2, Figure 1, etc.).
  • Do not attempt to evaluate the results in this section. Report only what you found; hold all discussion of the significance of the results for the Discussion section.
  • It is not necessary to describe every step of your statistical analyses. Scientists understand all about null hypotheses, rejection rules, and so forth and do not need to be reminded of them. Just say something like, "Honeybees did not use the flowers in proportion to their availability (X2 = 7.9, p<0.05, d.f.= 4, chi-square test)." Likewise, cite tables and figures without describing in detail how the data were manipulated. Explanations of this sort should appear in a legend or caption written on the same page as the figure or table.
  • You must refer in the text to each figure or table you include in your paper.
  • Tables generally should report summary-level data, such as means ± standard deviations, rather than all your raw data.  A long list of all your individual observations will mean much less than a few concise, easy-to-read tables or figures that bring out the main findings of your study.  
  • Only use a figure (graph) when the data lend themselves to a good visual representation.  Avoid using figures that show too many variables or trends at once, because they can be hard to understand.

From:  https://writingcenter.gmu.edu/guides/imrad-results-discussion

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Organizing Your Social Sciences Research Paper

  • 7. The Results
  • Purpose of Guide
  • Design Flaws to Avoid
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  • Glossary of Research Terms
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  • Narrowing a Topic Idea
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  • Evaluating Sources
  • Primary Sources
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  • Scholarly vs. Popular Publications
  • Qualitative Methods
  • Quantitative Methods
  • Insiderness
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  • Limitations of the Study
  • Common Grammar Mistakes
  • Writing Concisely
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  • Footnotes or Endnotes?
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  • Generative AI and Writing
  • USC Libraries Tutorials and Other Guides
  • Bibliography

The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

  • Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
  • Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE :   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

  • Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
  • Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
  • A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
  • The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
  • A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

  • Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
  • Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
  • Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
  • Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
  • Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
  • Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
  • Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

  • The particular approach to data collection used in the study;
  • Any theoretical perspective(s) that shaped your data collection and analytical approach;
  • When the study occurred, over how long, and where (concealing identifiable details as needed);
  • A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
  • The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
  • The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

  • Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
  • Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
  • Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
  • Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
  • Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
  • Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to Make a PowerPoint Presentation of Your Research Paper

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Table of Contents

A research paper presentation is often used at conferences and in other settings where you have an opportunity to share your research, and get feedback from your colleagues. Although it may seem as simple as summarizing your research and sharing your knowledge, successful research paper PowerPoint presentation examples show us that there’s a little bit more than that involved.

In this article, we’ll highlight how to make a PowerPoint presentation from a research paper, and what to include (as well as what NOT to include). We’ll also touch on how to present a research paper at a conference.

Purpose of a Research Paper Presentation

The purpose of presenting your paper at a conference or forum is different from the purpose of conducting your research and writing up your paper. In this setting, you want to highlight your work instead of including every detail of your research. Likewise, a presentation is an excellent opportunity to get direct feedback from your colleagues in the field. But, perhaps the main reason for presenting your research is to spark interest in your work, and entice the audience to read your research paper.

So, yes, your presentation should summarize your work, but it needs to do so in a way that encourages your audience to seek out your work, and share their interest in your work with others. It’s not enough just to present your research dryly, to get information out there. More important is to encourage engagement with you, your research, and your work.

Tips for Creating Your Research Paper Presentation

In addition to basic PowerPoint presentation recommendations, which we’ll cover later in this article, think about the following when you’re putting together your research paper presentation:

  • Know your audience : First and foremost, who are you presenting to? Students? Experts in your field? Potential funders? Non-experts? The truth is that your audience will probably have a bit of a mix of all of the above. So, make sure you keep that in mind as you prepare your presentation.

Know more about: Discover the Target Audience .

  • Your audience is human : In other words, they may be tired, they might be wondering why they’re there, and they will, at some point, be tuning out. So, take steps to help them stay interested in your presentation. You can do that by utilizing effective visuals, summarize your conclusions early, and keep your research easy to understand.
  • Running outline : It’s not IF your audience will drift off, or get lost…it’s WHEN. Keep a running outline, either within the presentation or via a handout. Use visual and verbal clues to highlight where you are in the presentation.
  • Where does your research fit in? You should know of work related to your research, but you don’t have to cite every example. In addition, keep references in your presentation to the end, or in the handout. Your audience is there to hear about your work.
  • Plan B : Anticipate possible questions for your presentation, and prepare slides that answer those specific questions in more detail, but have them at the END of your presentation. You can then jump to them, IF needed.

What Makes a PowerPoint Presentation Effective?

You’ve probably attended a presentation where the presenter reads off of their PowerPoint outline, word for word. Or where the presentation is busy, disorganized, or includes too much information. Here are some simple tips for creating an effective PowerPoint Presentation.

  • Less is more: You want to give enough information to make your audience want to read your paper. So include details, but not too many, and avoid too many formulas and technical jargon.
  • Clean and professional : Avoid excessive colors, distracting backgrounds, font changes, animations, and too many words. Instead of whole paragraphs, bullet points with just a few words to summarize and highlight are best.
  • Know your real-estate : Each slide has a limited amount of space. Use it wisely. Typically one, no more than two points per slide. Balance each slide visually. Utilize illustrations when needed; not extraneously.
  • Keep things visual : Remember, a PowerPoint presentation is a powerful tool to present things visually. Use visual graphs over tables and scientific illustrations over long text. Keep your visuals clean and professional, just like any text you include in your presentation.

Know more about our Scientific Illustrations Services .

Another key to an effective presentation is to practice, practice, and then practice some more. When you’re done with your PowerPoint, go through it with friends and colleagues to see if you need to add (or delete excessive) information. Double and triple check for typos and errors. Know the presentation inside and out, so when you’re in front of your audience, you’ll feel confident and comfortable.

How to Present a Research Paper

If your PowerPoint presentation is solid, and you’ve practiced your presentation, that’s half the battle. Follow the basic advice to keep your audience engaged and interested by making eye contact, encouraging questions, and presenting your information with enthusiasm.

We encourage you to read our articles on how to present a scientific journal article and tips on giving good scientific presentations .

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Writing an Abstract

Oral presentation, compiling a powerpoint.

Abstract : a short statement that describes a longer work.

  • Indicate the subject.
  • Describe the purpose of the investigation.
  • Briefly discuss the method used.
  • Make a statement about the result.

Oral presentations usually introduce a discussion of a topic or research paper. A good oral presentation is focused, concise, and interesting in order to trigger a discussion.

  • Be well prepared; write a detailed outline.
  • Introduce the subject.
  • Talk about the sources and the method.
  • Indicate if there are conflicting views about the subject (conflicting views trigger discussion).
  • Make a statement about your new results (if this is your research paper).
  • Use visual aids or handouts if appropriate.

An effective PowerPoint presentation is just an aid to the presentation, not the presentation itself .

  • Be brief and concise.
  • Focus on the subject.
  • Attract attention; indicate interesting details.
  • If possible, use relevant visual illustrations (pictures, maps, charts graphs, etc.).
  • Use bullet points or numbers to structure the text.
  • Make clear statements about the essence/results of the topic/research.
  • Don't write down the whole outline of your paper and nothing else.
  • Don't write long full sentences on the slides.
  • Don't use distracting colors, patterns, pictures, decorations on the slides.
  • Don't use too complicated charts, graphs; only those that are relatively easy to understand.
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How to present a research paper in PPT: best practices

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How to present a research paper in PPT: best practices

A research paper presentation is frequently used at conferences and other events where you have a chance to share the results of your research and receive feedback from colleagues. Although it may appear as simple as summarizing the findings, successful examples of research paper presentations show that there is a little bit more to it.

In this article, we’ll walk you through the basic outline and steps to create a good research paper presentation. We’ll also explain what to include and what not to include in your presentation of research paper and share some of the most effective tips you can use to take your slides to the next level.

Research paper PowerPoint presentation outline

Creating a PowerPoint presentation for a research paper involves organizing and summarizing your key findings, methodology, and conclusions in a way that encourages your audience to interact with your work and share their interest in it with others. Here’s a basic research paper outline PowerPoint you can follow:

1. Title (1 slide)

Typically, your title slide should contain the following information:

  • Title of the research paper
  • Affiliation or institution
  • Date of presentation

2. Introduction (1-3 slides)

On this slide of your presentation, briefly introduce the research topic and its significance and state the research question or objective.

3. Research questions or hypothesis (1 slide)

This slide should emphasize the objectives of your research or present the hypothesis.

4. Literature review (1 slide)

Your literature review has to provide context for your research by summarizing relevant literature. Additionally, it should highlight gaps or areas where your research contributes.

5. Methodology and data collection (1-2 slides)

This slide of your research paper PowerPoint has to explain the research design, methods, and procedures. It must also Include details about participants, materials, and data collection and emphasize special equipment you have used in your work.

6. Results (3-5 slides)

On this slide, you must present the results of your data analysis and discuss any trends, patterns, or significant findings. Moreover, you should use charts, graphs, and tables to illustrate data and highlight something novel in your results (if applicable).

7. Conclusion (1 slide)

Your conclusion slide has to summarize the main findings and their implications, as well as discuss the broader impact of your research. Usually, a single statement is enough.

8. Recommendations (1 slide)

If applicable, provide recommendations for future research or actions on this slide.

9. References (1-2 slides)

The references slide is where you list all the sources cited in your research paper.

10. Acknowledgments (1 slide)

On this presentation slide, acknowledge any individuals, organizations, or funding sources that contributed to your research.

11. Appendix (1 slide)

If applicable, include any supplementary materials, such as additional data or detailed charts, in your appendix slide.

The above outline is just a general guideline, so make sure to adjust it based on your specific research paper and the time allotted for the presentation.

Steps to creating a memorable research paper presentation

Creating a PowerPoint presentation for a research paper involves several critical steps needed to convey your findings and engage your audience effectively, and these steps are as follows:

Step 1. Understand your audience:

  • Identify the audience for your presentation.
  • Tailor your content and level of detail to match the audience’s background and knowledge.

Step 2. Define your key messages:

  • Clearly articulate the main messages or findings of your research.
  • Identify the key points you want your audience to remember.

Step 3. Design your research paper PPT presentation:

  • Use a clean and professional design that complements your research topic.
  • Choose readable fonts, consistent formatting, and a limited color palette.
  • Opt for PowerPoint presentation services if slide design is not your strong side.

Step 4. Put content on slides:

  • Follow the outline above to structure your presentation effectively; include key sections and topics.
  • Organize your content logically, following the flow of your research paper.

Step 5. Final check:

  • Proofread your slides for typos, errors, and inconsistencies.
  • Ensure all visuals are clear, high-quality, and properly labeled.

Step 6. Save and share:

  • Save your presentation and ensure compatibility with the equipment you’ll be using.
  • If necessary, share a copy of your presentation with the audience.

By following these steps, you can create a well-organized and visually appealing research paper presentation PowerPoint that effectively conveys your research findings to the audience.

What to include and what not to include in your presentation

In addition to the must-know PowerPoint presentation recommendations, which we’ll cover later in this article, consider the following do’s and don’ts when you’re putting together your research paper presentation:

  • Focus on the topic.
  • Be brief and to the point.
  • Attract the audience’s attention and highlight interesting details.
  • Use only relevant visuals (maps, charts, pictures, graphs, etc.).
  • Use numbers and bullet points to structure the content.
  • Make clear statements regarding the essence and results of your research.

Don’ts:

  • Don’t write down the whole outline of your paper and nothing else.
  • Don’t put long, full sentences on your slides; split them into smaller ones.
  • Don’t use distracting patterns, colors, pictures, and other visuals on your slides; the simpler, the better.
  • Don’t use too complicated graphs or charts; only the ones that are easy to understand.
  • Now that we’ve discussed the basics, let’s move on to the top tips for making a powerful presentation of your research paper.

8 tips on how to make research paper presentation that achieves its goals

You’ve probably been to a presentation where the presenter reads word for word from their PowerPoint outline. Or where the presentation is cluttered, chaotic, or contains too much data. The simple tips below will help you summarize a 10 to 15-page paper for a 15 to 20-minute talk and succeed, so read on!

Tip #1: Less is more

You want to provide enough information to make your audience want to know more. Including details but not too many and avoiding technical jargon, formulas, and long sentences are always good ways to achieve this.

Tip #2: Be professional

Avoid using too many colors, font changes, distracting backgrounds, animations, etc. Bullet points with a few words to highlight the important information are preferable to lengthy paragraphs. Additionally, include slide numbers on all PowerPoint slides except for the title slide, and make sure it is followed by a table of contents, offering a brief overview of the entire research paper.

Tip #3: Strive for balance

PowerPoint slides have limited space, so use it carefully. Typically, one to two points per slide or 5 lines for 5 words in a sentence are enough to present your ideas.

Tip #4: Use proper fonts and text size

The font you use should be easy to read and consistent throughout the slides. You can go with Arial, Times New Roman, Calibri, or a combination of these three. An ideal text size is 32 points, while a heading size is 44.

Tip #5: Concentrate on the visual side

A PowerPoint presentation is one of the best tools for presenting information visually. Use graphs instead of tables and topic-relevant illustrations instead of walls of text. Keep your visuals as clean and professional as the content of your presentation.

Tip #6: Practice your delivery

Always go through your presentation when you’re done to ensure a smooth and confident delivery and time yourself to stay within the allotted limit.

Tip #7: Get ready for questions

Anticipate potential questions from your audience and prepare thoughtful responses. Also, be ready to engage in discussions about your research.

Tip #8: Don’t be afraid to utilize professional help

If the mere thought of designing a presentation overwhelms you or you’re pressed for time, consider leveraging professional PowerPoint redesign services . A dedicated design team can transform your content or old presentation into effective slides, ensuring your message is communicated clearly and captivates your audience. This way, you can focus on refining your delivery and preparing for the presentation.

Lastly, remember that even experienced presenters get nervous before delivering research paper PowerPoint presentations in front of the audience. You cannot know everything; some things can be beyond your control, which is completely fine. You are at the event not only to share what you know but also to learn from others. So, no matter what, dress appropriately, look straight into the audience’s eyes, try to speak and move naturally, present your information enthusiastically, and have fun!

If you need help with slide design, get in touch with our dedicated design team and let qualified professionals turn your research findings into a visually appealing, polished presentation that leaves a lasting impression on your audience. Our experienced designers specialize in creating engaging layouts, incorporating compelling graphics, and ensuring a cohesive visual narrative that complements content on any subject.

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  • v.74(8); 2010 Oct 11

Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

  • Audio recordings and transcripts from in-depth or semi-structured interviews
  • Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
  • Audio recordings and transcripts from focus group sessions.
  • Field notes (notes taken by the researcher while in the field [setting] being studied)
  • Video recordings (eg, lecture delivery, class assignments, laboratory performance)
  • Case study notes
  • Documents (reports, meeting minutes, e-mails)
  • Diaries, video diaries
  • Observation notes
  • Press clippings
  • Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

  • Issues can be examined in detail and in depth.
  • Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
  • The research framework and direction can be quickly revised as new information emerges.
  • The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
  • Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
  • Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

  • Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
  • Rigor is more difficult to maintain, assess, and demonstrate.
  • The volume of data makes analysis and interpretation time consuming.
  • It is sometimes not as well understood and accepted as quantitative research within the scientific community
  • The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
  • Issues of anonymity and confidentiality can present problems when presenting findings
  • Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

  • □ Research question is clearly stated.
  • □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
  • □ Any specific research or educational terminology used later in manuscript is defined.
  • □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
  • □ Reason for choosing particular research method is stated.
  • □ Criteria for selecting study participants are explained and justified.
  • □ Recruitment methods are explicitly stated.
  • □ Details of who chose not to participate and why are given.
  • □ Study sample and research setting used are described.
  • □ Method for gaining informed consent from the participants is described.
  • □ Maintenance/Preservation of subject anonymity and confidentiality is described.
  • □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
  • □ Methods are outlined and examples given (eg, interview guide).
  • □ Decision to stop data collection is described and justified.
  • □ Data analysis and verification are described, including by whom they were performed.
  • □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
  • □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
  • □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
  • □ Transferability of research findings to other settings is discussed.
  • □ Findings are presented in the context of any similar previous research and social theories.
  • □ Discussion often is incorporated into the results in qualitative papers.
  • □ A discussion of the existing literature and how this present research contributes to the area is included.
  • □ Any particular strengths and limitations of the research are discussed.
  • □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

  • □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

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  • Published: 21 February 2024

Making cities mental health friendly for adolescents and young adults

  • Pamela Y. Collins   ORCID: orcid.org/0000-0003-3956-448X 1 ,
  • Moitreyee Sinha 2 ,
  • Tessa Concepcion 3 ,
  • George Patton   ORCID: orcid.org/0000-0001-5039-8326 4 ,
  • Thaisa Way 5 ,
  • Layla McCay 6 ,
  • Augustina Mensa-Kwao   ORCID: orcid.org/0000-0001-8136-6108 1 ,
  • Helen Herrman 7 , 8 ,
  • Evelyne de Leeuw 9 ,
  • Nalini Anand 10 ,
  • Lukoye Atwoli 11 ,
  • Nicole Bardikoff 12 ,
  • Chantelle Booysen   ORCID: orcid.org/0000-0001-7218-8039 13 ,
  • Inés Bustamante 14 ,
  • Yajun Chen 15 ,
  • Kelly Davis 16 ,
  • Tarun Dua 17 ,
  • Nathaniel Foote 18 ,
  • Matthew Hughsam 2 ,
  • Damian Juma 19 ,
  • Shisir Khanal 20 ,
  • Manasi Kumar   ORCID: orcid.org/0000-0002-9773-8014 21 , 22 ,
  • Bina Lefkowitz 23 , 24 ,
  • Peter McDermott 25 ,
  • Modhurima Moitra 3 ,
  • Yvonne Ochieng   ORCID: orcid.org/0000-0002-9741-9814 26 ,
  • Olayinka Omigbodun 27 ,
  • Emily Queen 1 ,
  • Jürgen Unützer 3 ,
  • José Miguel Uribe-Restrepo 28 ,
  • Miranda Wolpert 29 &
  • Lian Zeitz 30  

Nature ( 2024 ) Cite this article

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  • Risk factors

Urban life shapes the mental health of city dwellers, and although cities provide access to health, education and economic gain, urban environments are often detrimental to mental health 1 , 2 . Increasing urbanization over the next three decades will be accompanied by a growing population of children and adolescents living in cities 3 . Shaping the aspects of urban life that influence youth mental health could have an enormous impact on adolescent well-being and adult trajectories 4 . We invited a multidisciplinary, global group of researchers, practitioners, advocates and young people to complete sequential surveys to identify and prioritize the characteristics of a mental health-friendly city for young people. Here we show a set of ranked characteristic statements, grouped by personal, interpersonal, community, organizational, policy and environmental domains of intervention. Life skills for personal development, valuing and accepting young people’s ideas and choices, providing safe public space for social connection, employment and job security, centring youth input in urban planning and design, and addressing adverse social determinants were priorities by domain. We report the adversities that COVID-19 generated and link relevant actions to these data. Our findings highlight the need for intersectoral, multilevel intervention and for inclusive, equitable, participatory design of cities that support youth mental health.

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More than a decade ago, Galea posed the question “Can we improve mental health if we improve cities?” 4 . In the past two centuries, urbanization has shaped landscapes and lives, making it the “sentinel demographic shift” of our times 4 . The relationships between mental health status and the social, cultural and physical environment have been explored for at least as long; nineteenth-century researchers proposed environmental exposures as possible explanations of ‘insanity’ 5 . Faris and Dunham’s classic 1930s study 6 linked social disorganization and unstable communities to mental disorders. Two decades later, Leonard Duhl sought to create healthy societies through liveable cities, informing the World Health Organization’s Healthy Cities initiative 7 , 8 . The question remains pertinent today even as we recognize the multiple and complex forces that shape mental health 9 . Today we understand that urban environments influence a broad range of health outcomes for their populations, positively and negatively, and this impact is manifested unequally 10 . Opportunities for education and connection exist for some, whereas rising levels of urban inequality, violence, stressful racial or ethnic dynamics in urban neighbourhoods, exposure to environmental toxins, lack of green space, inadequate infrastructure and fear of displacement increase risk for poor mental health and disproportionately affect marginalized groups 11 . Disparate outcomes also pertain to distinct developmental stages, and the mental health of adolescents and young adults is particularly vulnerable to urban exposures.

Adolescents, youth and urban mental health

Young people under the age of 25 are the demographic group most likely to move to cities for educational and employment opportunities, and by 2050 cities will be home to 70% of the world’s children 3 . Cities concentrate innovation 3 and have long been considered the consummate source of skills, resources and talent 12 . They offer greater opportunities for health and economic development, education, employment, entertainment and social freedoms (that is, the ‘urban advantage’), but rapid urbanization also deepens disparities and exposes individuals to considerable adversity, placing their mental health at risk 13 . In fact, most evidence points to urban living as a risk factor for poorer mental health, yielding increased risk for psychosis, anxiety disorders and depression 1 , 2 . Adolescence and young adulthood, specifically, encompass a critical period of risk for the incidence of mental disorders: an estimated half of mental disorders evident before age 65 begin in adolescence and 75% begin by age 24 (ref.  14 ). Mental disorders are the leading causes of disease burden among 10–24-year-olds worldwide 15 , responsible for an estimated 28.2 million disability-adjusted life years globally, with 1 disability-adjusted life year being equivalent to a healthy year of life lost to the disability caused by mental disorders. Public awareness of these issues rose as the incidence of mental disorders and suicide increased in some countries among adolescents and young adults during the coronavirus pandemic 16 , 17 . Urban environments probably have a role in these processes.

Fundamental to adolescents’ growth and development are their interactions with the complex urban environment: physical, political, economic, social and cultural 18 . Adolescents have a heightened sensitivity to context and social evaluation, and a stronger neural response to social exclusion, as well as to threat and reward stimuli 19 , and it is plausible that they may be particularly sensitive to social and environmental cues in the urban context, such as discrimination or violence. Discriminatory policies and norms are entrenched in many of the institutions with which young people interact (for example, schools, housing, justice and policing), and minoritized youth may experience the emotional and mental health consequences 20 . In fact, in settings of structural inequality (for example, high neighbourhood poverty and unemployment), young people are at greater risk for low self-efficacy and feelings of powerlessness and depression 21 . Social cohesion and collective efficacy can reduce the effects of concentrated disadvantage and nurture social and emotional assets among young people, families and their networks 21 .

At present, the world’s largest population of adolescents and young adults so far is growing up amid the sequelae of a tenacious pandemic, rapid population growth in urban centres and increasing urbanization, demanding an urgent response to support youth mental health 22 . Investing in adolescent well-being is said to yield a triple dividend through actions that reduce mortality and disability in adolescence, prolong healthy life in adulthood, and protect the health of the next generation by educating and strengthening the health of young parents 23 . Interventions in urban settings that align with developmental needs of adolescents and young adults could remediate insults from early life and establish healthy behaviours and trajectories for adult life 19 , 24 , potentially averting chronic conditions such as human immunodeficiency virus (HIV) and the associated mental health, social and physical sequelae 25 . In fact, investment in a package of adolescent mental health interventions can yield a 24-fold return in health and economic benefits 26 . At the societal level, shaping the aspects of urban life that influence youth mental health—through services, social policies and intentional design—could have an enormous impact 4 . Proposals for ‘restorative urbanism’ that centre mental health, wellness and quality of life in urban design may move cities in the direction of moulding urban environments for better adolescent health 27 , 28 . Young people, who contribute to the creativity of urban environments and drive movements for social change 29 , have a central part to play in this transformation.

Mental Health Friendly Cities, a global multi-stakeholder initiative led by citiesRISE, mobilizes youth-driven action and systems reform to promote and sustain the mental health and well-being of young people in cities around the world 30 , 31 ( Supplementary Information ). To guide transformative actions that will enable cities to promote and sustain adolescent and youth mental health, we studied global priorities for urban adolescent mental health. One aim of this study is to contribute data-driven insights that can be used to unite several sectors in cities to act within and across their domains in favour of mental health promotion and care that is responsive to the needs of young people. To that end, we administered a series of linked surveys that permitted the influence of ideas from young people and multidisciplinary domain experts through an anonymous sequential process, following established methods for research priority setting 32 .

Framework and top-ranked recommendations

To determine the elements of an urban landscape that would support mental health for adolescents and youth and would amplify their voices, we recruited a panel of 518 individuals from 53 countries to participate in a series of three digitally administered surveys that began in April 2020 (Table 1 ). Figure 1 shows the panel participation at each round. In survey 1, panellists responded to the open-ended question: “What are the characteristics of a mental health-friendly city for young people?”. Analysis of survey 1 data produced 134 statements about mental health-friendly cities for young people ( Methods ). In survey 2, participants selected their preferred 40 of the 134 statements. They were also presented with a second question related to the influence of the COVID-19 pandemic on their ideas about youth well-being in cities. In survey 3, we categorized survey 2 statements by socioecological domains (Fig. 2 ) and asked panellists to rank-list their preferred statements in each domain. Before ranking, panellists were required to choose one of three framings that informed their selected ranking: immediacy of impact on youth mental health; ability to help youth thrive in cities; and ease or feasibility of implementation.

figure 1

The composition of the project leadership structures, sample recruitment and participation by each survey round are shown below. We invited 801 individuals to participate in the survey panel through recommendations and direct invitations from advisory board members. Participants recruited through snowball sampling received the Research Electronic Data Capture (REDCap) link ( n  = 24). Individuals who gave informed consent in REDCap were deemed to have accepted the survey panel invitation. S1, survey 1; S2, survey 2; S3, survey 3.

figure 2

The socioecological model with six levels (personal, interpersonal, community, organization, policy and environment) that are used to categorize the characteristics of a mental health friendly city.

We present the findings of the third survey within a socioecological model (Figs. 3 – 5 ) because of this model’s relevance to the combination of social and environmental exposures in an urban setting and their interaction with the developing adolescent 33 . Bronfenbrenner’s model begins by recognizing that young people’s personal experiences and development are shaped by their interactions with the people around them 34 ; that is, they react to and act on their immediate environment of familial and peer relationships (microlevel). These interpersonal relationships are also influenced by neighbourhood and community dynamics and exposure to institutions and policies (mesolevel). These, in turn, are nested within the organizational, political, historical, cultural (for example, values, norms and beliefs) and physical environments (macrolevel) whose interplay directly or indirectly affects the adolescent’s mental health and well-being. A high court ruling (policy environment) could have direct or indirect effects on the community, household and personal well-being of a young person seeking asylum. The socioecological framework encompasses the dynamic relationships of an individual with the social environment.

figure 3

Mean ranks and standard deviations (s.d.) values for each mental health-friendly city (MHFC) characteristic are reported grouped by socioecological level and three framings described in the Analysis: immediacy of impact; ability to help youth thrive in cities; and ease or feasibility of implementation. Overall ranks (along with mean and s.d. values) for the total sample are reported. n values in bold represent the number of participants responding for each domain; the percentages in bold represent the percentage of respondents per domain. The number and percentage of the sample that assigned the highest rank for each characteristic are also reported (column 2). The colour continuum from light blue to dark blue shows the highest ranked means in the lightest shades and the lower ranks in darker blue.

figure 4

See the caption of Fig. 3 for details.

figure 5

See the caption of Fig. 3 for details. LGBT+, people from sexual and gender minorities.

The characteristics

We grouped 37 city characteristics across 6 socioecological domains: personal, interpersonal, community, organizational, policy and environmental. Figures 3 – 5 show the mean ranking for each framing and the total mean ranking averaged across frames. We show, for each characteristic statement, the number and percentage of panellists who ranked it highest. The five characteristics in the personal domain centre on factors that enable healthy emotional maturation for young people, future orientation and self-reflexivity. Most panellists (53%) ranked these characteristics according to immediacy of impact on youth mental health in cities, and mean rankings were identical to those linked to ability to help youth thrive in cities. The characteristic that describes prioritizing teaching life skills, providing opportunities for personal development and providing resources that allow young people to flourish rose to the top mean rank for each frame and was also ranked first in this domain by the largest number of panellists ( n  = 93). Notably, the characteristic that describes preparing youth to handle their emotions and overcome challenges was ranked first by 62 panellists, although its mean rank was much lower.

Characteristics in the interpersonal domain refer to young people’s interactions with others in the environment. Prioritized characteristics in this domain centred on relationships marked by acceptance and respect for young people and noted the value of intergenerational relationships. The top-ranked characteristic emphasized age friendliness and interactions that value the feelings and opinions of young people as well as safe and healthy relationships. In this domain, ranked means for characteristics framed according to immediacy of impact on youth mental health and ability to help youth thrive were the same for the top two characteristics. Notably, the two highest-ranked means for ease of implementation focused on opportunities for safe and healthy relationships and strengthening intergenerational relationships.

Young people’s intrapersonal experiences and interpersonal relationships are nested within a system of community and organizational relationships. Study participants prioritized access to safe spaces for youth to gather and connect among the three characteristics in the domain of community, and rankings were identical for each framing. At the organizational domain, two characteristics shared high mean rankings: employment opportunities that allow job security and satisfaction and a responsive and supportive educational system. Health-care services and educational services were the organizations most frequently referenced in relation to youth mental health. Whereas employment opportunities ranked first in terms of feasibility of implementation, provision of youth-friendly health services ranked first for immediacy of impact on youth mental health. With the exception of the community and organizational domains, more panellists chose to frame their responses in terms of immediacy of impact on youth mental health.

Of the four statements in the policy domain, the design and planning of cities with youth input and gender sensitivity ranked highest overall and was most frequently ranked first by panellists (30.68%). Promoting democratic cooperation and equal opportunity and anti-discrimination in all institutions received the highest mean rank for feasibility of implementation.

The sixth socioecological domain lists 13 characteristics related to the social, cultural and physical environments. Addressing adverse social determinants of health for young people had the highest overall ranked mean; however, normalizing youth seeking mental health care and addressing service gaps ranked first when framed by feasibility of implementation and immediacy of impact. Having access to affordable basic amenities was most frequently ranked first in this domain by panellists, but panellist preferences were distributed across the list.

COVID-19 and urban youth well-being

Our data collection began in April 2020 during the COVID-19 pandemic, and by survey 2 (August 2020), most countries were experiencing the pandemic’s public health, social and economic effects. In light of this, we added an open-ended survey question to which 255 participants responded “How has the COVID-19 pandemic changed your ideas about the wellbeing of young people in cities?” ( Methods ). Most respondents reported changes in perspective or new emphases on inequities as determinants of youth well-being and mental health, whereas nine reported that COVID-19 did not change their ideas. For one such respondent (in the >35 years age category), the pandemic merely confirmed the powerful effect of social vulnerabilities on risk and outcomes during an emergency: “COVID-19 has not changed my ideas about the wellbeing of young people in cities. I found that the young people in cities who did well during the lockdown period and the difficult period of the pandemic were those who were already doing well in terms of a rich social network, good interpersonal relations with family and friends, enjoyable work life, a close religious network, membership [in] a young people’s club so that they were able to stay connected via social media. Those who had access to food and essential commodities and those who knew they would return to school or work after the pandemic. Those who had access to good living conditions and some space for recreation also did well. ... The impact of COVID19 was felt much more by those with existing mental health conditions, living in crowded slums, poverty, unemployment, who were uncertain about the next step”.

Respondents highlighted losses young people experienced as a result of the pandemic. These included loss of the city as a place of opportunity; loss of jobs, familial and individual income, and economic stability; loss of a planned future and loss of certainty; loss of rites of passage of youth; loss of access to friends, social networks and social support; loss of access to quality education and to health care, especially mental health care and sexual and reproductive health services; loss of opportunities for psychological and social development; and loss of loved ones who died from COVID-19. We summarize the qualitative findings according to the socioecological framework. We present sample quotes in Table 2 , along with the age category of the respondents (18–24, 25–35 and >35) and actions for cities to take.

Policy and environment

Governance and equity.

Freedom from discrimination and the value of equity were listed among the mental health-friendly city characteristics; however, respondents pointed out the dearth of equity that COVID-19 unveiled (see the first quote in Table 2 ).

Respondents observed that policy responses to COVID-19, including mandated curfews and quarantines, shifted the social and economic environment of cities. Young people and their families lost economic opportunities, and cities also became less affordable during the pandemic. Participants explained that poverty and job loss worsened young people’s mental health and well-being and exposed youth to more risk factors because they needed to “hustle or work to place food on the table”. The loss of jobs also deprived youth of hope and underlined the economic inequities that some felt marked their generation more than previous ones. One participant (18–24) reported “Before, I used to think youths need someone who can understand them, empathize with them, but looking at the current scenario, I feel youths need security and a hopeful future too”. In some settings, these economic shifts resulted in an exodus from cities. A respondent (18–24) observed “Cities have always attracted young people but since the pandemic started the cost of living has gone from being a barrier to being another factor in encouraging young people to leave”.

Urban built environment

For those who remained in the city, the urban built environment could also offer respite from pandemic-related restrictions in mobility when green spaces and other open spaces were accessible. Participants alluded to cramped urban housing, crowded slums and poor housing infrastructure as stressors that the availability of safe public spaces alleviated. Green space in particular provided solace for young people. A participant (18–24) responded “It’s difficult when you’re confined to the limited space especially when you’re not closer to nature. Negative thoughts get you one way or another even if you try your best. Pandemic has caused more depression I reckon among the youths”. Accessible green space was highlighted as a need and an area for investing effort and policy change (Table 2 ). A desire for clean, youth-friendly green space for safe gathering and recreation was contrasted with unplanned land use and confined spaces, the latter of which some participants linked to greater risks for young people.

Community and organizations

Respondents reported diminished access to education and health care, and a disregard of young people’s needs by decision-makers (Table 2 ). Some responses criticized the lack of forethought before the pandemic to budget for and provide supportive learning environments for youth of all socioeconomic strata. The closure of schools generated stress for young people with the disruption of routines and opportunities to socialize. The pandemic generated greater uncertainty about job opportunities and future trajectories. At the same time, the pandemic brought opportunities to position youth as either contributors and leaders or detractors from community life. Young people reflected on how they experienced inclusion, empathy and exclusion, as well as opportunity for leadership. One respondent (25–35) commented “Our worlds are changing and with it many of our expectations about our education, work, personal interactions and relationships. Instead of being met with understanding, we are collectively positioned as transgressors of social distancing in a way that fails to understand that we are often incredibly vulnerable in this new world and left exposed by lack of infrastructure, service provision and support”.

A respondent (18–24) noticed possibilities for involving young people in responses that could mitigate their numerous losses: “Given the opportunities and resources, young people can be a carrier of change and wellbeing if adults trust them enough to be”.

Interpersonal domain

Getting through difficult times required interpersonal supports: connectedness through in-person encounters in safe spaces, complemented by digital interactions. Multiple respondents emphasized the relationship between social isolation and poor mental health among city youth during the pandemic, noting the difficulty of making meaningful connection during a time of physical isolation. Two young respondents (18–24) said the well-being of young people was linked to being “in a group of people”, which provides “safety and unity”, and to “inclusion, activity, and interpersonal relationships”. Space repeatedly emerged as a theme, as a conduit to facilitate social connection for young people without risk of COVID-19 transmission, violence, sexual abuse or exposure to drug use. Some participants called for greater investment in creating strong, safe virtual communities for young people; however, although participants identified virtual spaces as a resource for mental health support, a young panellist (18–24) remarked of social media and technology that “It isolated people, even though we have … ways of staying connected 24/7, we still feel lonely.”

Consistent with the lead mental health-friendly city characteristic in the personal domain (Figs. 3 – 5 ), the pandemic prompted realization of the need for personal skills development to support youth mental well-being. Some respondents expressed concern about the loss of social skills among young people as a result of confinement and an 18–24-year-old commented “… Youths are in that stage where they need to be equipped with skills to promote positive mental wellbeing”. Another young person (18–24) remarked “Most of us do not really have the capacity and necessary skills to support each other when it comes to mental health”. Participants described the importance of being prepared for unpredictable circumstances and enabling youth to “manage themselves, their emotions, and wellbeing”.

Pandemic-related gains

In some cases, the pandemic brought positive experiences for young people, including more time for self-reflection and discovery, engaging in healing practices, more opportunities to connect with friends, and overall, a greater societal and individual focus on strengthening mental health. A participant (25–35) referred to young people: “They are more conscious about health and their wellbeing by reducing workload and connecting with nature”. Others believed the pandemic revealed young people’s capacity to adapt and to consider the needs of their elders. Some viewed the social justice uprisings that occurred in many countries as a positive vehicle for change and cooperation with others. Changing these conditions would require longer-term solutions: strengthening urban infrastructure and addressing the underlying drivers of inequity. Another participant (>35) lauded the power of youth activism: “… the pandemic has shown us that the resilience of youth is great, as well as the commitment and solidarity with their communities through volunteering, advocacy and youth mobilization”.

Our study convened a multinational and multidisciplinary panel of researchers, practitioners, advocates and young people to identify the characteristics of a mental health-friendly city for youths. The characteristics are distributed among six socioecological domains (Figs. 3 – 5 ) that encompass the personal development of young people, supportive educational systems, people-centred health care, a built environment responsive to the needs of young people, and equity-focused policy-making and governance. Within each of these domains, the characteristics we identified are associated with an evolving evidence base linked to youth mental health outcomes and to potential policy intervention.

Intrapersonal characteristics in our list underline the centrality of enabling young people to cultivate skills to manage their interior lives. The targets of such skills-building activities align with proposed ‘active ingredients’ of mental health interventions, such as intervention components related to mechanisms of action or clinical effects on depressive or anxiety symptoms 35 . Examples include affective awareness skills that enable young people to differentiate and describe emotions 36 and emotion regulation skills to increase and maintain positive emotions 37 . Youth-friendly mental health and educational services, a priority theme at the community level of the framework, could support the intrapersonal realm by deploying a variety of interventions for self-control that benefit adolescent and young adult academic, behavioural and social functioning 38 . Such interventions can also be implemented in earlier childhood educational settings through integration into the curriculum or through other community-based medical or social service organizations 39 . Interventions implemented in selected high-income settings include Promoting Alternative Thinking Strategies 40 , the Incredible Years 41 and Family Check-up 42 . For young adults, interventions that convey skills to alleviate common psychological problems such as procrastination, perfectionism, low self-esteem, test anxiety and stress could potentially reduce the prevalence of specific mental health conditions while possibly providing acceptable and non-stigmatizing options for care 43 , 44 .

Our data suggest that a defining theme of any mental health-friendly city for youth is the quality of young people’s social fabric and the city’s ability to provide young people with the skills, opportunities and places required to build and maintain healthy social relationships with their peers, across generations, and as members of a community. The relationships of concern in the interpersonal realm have intrinsic value for healthy adolescent and youth development, promoting well-being 45 and prevention of depression 46 , 47 . Panellists also linked opportunities to socialize and build social networks to the availability of safe spaces, the top-ranked priority in the community domain. Achieving safety necessitates equitable and violence-free institutions and cities 48 , a priority that panellists ranked first for ease of implementation in the policy domain. Thus, policies and legislation are required that reduce neglect, bullying, harassment, abuse, censorship, exposure to violence and a wide range of threats towards young people, from homelessness to crime to intimidation by officials 48 , 49 .

Exposure to community violence and household violence consistently worsens mental health outcomes for youth 50 , 51 , 52 , 53 ; successful reduction of urban violence should be prioritized. Equity-focused responses to safety needs should include reducing discriminatory physical and structural violence against young people based on race, ethnicity, gender, sexuality or mental health status, which place youth at risk of harmful exposures: rape or trafficking of adolescent girls or police killings of North American Black youth. To create urban spaces in which young people can experience safety, freedom and belongingness requires approaches that actively prevent discrimination 54 and that consider young people’s multiple identities in the design of institutional as well as outdoor spaces. Women-only parks create greater security for girls and young women and potentially more positive social interaction in some settings 55 .

The benefits of green space, measured as self-satisfaction for adolescents, are linked to greater social contact (for example, more close friends), underscoring space as a conduit for social connection 55 . The advantages of healthy urban spaces for adolescents have emerged not only in health sciences research but also in allied fields such as urban design and sociology 27 , 56 , 57 . Urban spaces with opportunities for active commute options to and from school are associated with increased physical activity and environmental supportiveness 58 . Similarly, the presence of community spaces, such as town centres, is associated with improved social connectedness and sense of belonging 59 .

The critical importance of social connectedness was reinforced in the COVID-19 responses. Yet, in many cities the pandemic eliminated spaces that foster urban conviviality, often with lasting effects 60 . Restricted movement and COVID-19 transmission risk associated with public transport may have contributed to greater stress for urban dwellers and ongoing reluctance to use these services 61 . Such factors contribute to social isolation, which may persist in the near term. Consistent with our COVID-19 data, responses from a sample of Australian youth identified social isolation, interrupted education and work, and uncertainty about the future among the primary negative effects of COVID-19 pandemic 62 . In several studies, loneliness increased the risk of mental health conditions among young people during prior epidemics; of relevance to the COVID-19 pandemic, the duration of loneliness predicted future mental health problems 63 .

Analysis of our survey 2 data revealed differences in the priorities of young participants (18–24 and 25–35) compared with panellists over age 35. This discrepancy could have implications for urban decision-makers whose plans to implement positive actions on behalf of young people may not align with what is most salient for youth. Thus, youth involvement in policy development is even more crucial. Soliciting youth perspectives about what supports their mental health based on their personal experiences could simplify and improve interventions intended for them 64 . Several actions could facilitate meaningful youth engagement in governance: encourage collaboration between governments and youth organizations to co-create and co-lead national action plans; implement mechanisms within global governance organizations for youth consultation at local, national and international levels; require inclusion of young people on relevant conference agendas; and improve access to funding for youth-led organizations 65 , 66 .

Notably, the themes of equity and elimination of discrimination due to race, gender, sexual orientation and neurodiversity arose frequently in the responses to the survey and the COVID-19 question, as did the adversities to which minoritized groups are vulnerable (for example, community violence, police violence and bullying; Figs. 4 and 5 ). A city that is free of discrimination and racism ranked first among policy responses with immediacy of impact on the mental health of youth—even though no statements proposed dismantling systems of oppression that underlie racism and discrimination, as one respondent noted (Fig. 4 ). Globally, racism, xenophobia and other forms of discrimination increase mortality and harm the mental health of affected groups through stress-related physiological responses, harmful environmental exposures and limited access to opportunities and health services 20 , 67 , 68 , 69 . Embedded racist and xenophobic norms, policies and practices of institutions—including those that govern educational, labour and health care systems—yield racialized outcomes for young people around the world (for example, high incidence of HIV infection among adolescent girls in southern sub-Saharan Africa) 20 . To disrupt these forces requires multiple approaches, including recognition and remedy of historical injustices, the activism of social movements committed to change, and implementation of legal frameworks based in human rights norms 70 .

When participants ranked characteristics for ease of implementation (Figs. 3 – 5 ), they coalesced around a broad set of factors demonstrating the need for collaboration across urban sectors (for example, normalizing seeking mental health care, promoting democratic cooperation and equal opportunity, and creating employment opportunities and progressive educational systems). This need for cooperation is perhaps most apparent for actions that increase equity. Successful cooperation requires a clear, shared vision and mission, allocation of funding in each sector, diversity of funding sources, distributed decision-making and authority across sectors, and policies that facilitate collaboration 71 . However, well-intentioned cross-sectoral responses to urban needs may inadvertently increase inequities by designing programmes influenced by market forces that magnify environmental privilege (that is, unequal exposure to environmental problems according to social privilege) 54 . Examples include gentrification and development that use land to create green spaces but further dislocate and marginalize communities in need of affordable housing 54 . Implementing community- and youth-partnered processes for urban health equity policy co-creation could yield unified agendas and help to circumvent inequitable outcomes 54 , 72 . A mental health-friendly city must be positioned to support, integrate and enable the thriving of marginalized and vulnerable young people of the society, who should be involved in its governance.

Strengths and limitations

Our study has several strengths. First, this priority-setting study yielded a rich dataset of recommended characteristics of a mental health-friendly city for young people from a globally diverse panel of more than 480 individuals from 53 countries. Second, we welcomed expertise from participants with roles relevant to urban sectors: researchers, policymakers and practice-based participants, and we engaged young people in the study advisory board and as study participants, capitalizing on their lived experience. Third, we captured information about how the COVID-19 pandemic influenced participants’ ideas about urban adolescent mental health. Fourth, to our knowledge, this is the first study that brings together a large and multidisciplinary set of stakeholders concerned for cities (for example, urban designers) and for youth mental health (for example, teachers and health professionals) to identify priorities for intersectoral action.

Our study also has several limitations. First, the participants recruited do not reflect the full social and economic diversity of urban populations whom city governments and decision-makers must serve. Our decision to use a web-based format following standard health research priority-setting methods required tradeoffs. We sought disciplinary, age and geographic diversity; however, our sample does not represent the most marginalized groups of adolescents or adults. Rather, the recruitment of academics, educators, leaders and well-networked young people through an online study probably minimizes the number of participants living in adversity. Although we also recruited young people who were not necessarily established experts, many were students or members of advocacy or international leadership networks and were not likely to exemplify the most disadvantaged groups. We risk masking the specific viewpoints or needs of marginalized and at-risk young people. However, we are reassured by the prominence of equity as a theme and the call to address social determinants of health. Second, it is possible that participants recruited through the authors’ professional networks may be more likely to reflect the viewpoints of the advisory committee members who selected them, given collaborative or other professional relationships. This may have shaped the range of responses and their prioritization. Third, the aspirational calls for an end to discrimination and inequalities highlighted in our results require confronting long-standing structural inequities both within and between countries. Structural violence frequently maintains these power imbalances. Although we do not view their aspirational nature as a limitation, we note that our study data do not outline the complexity of responses required to address these determinants of mental health or to dismantle discriminatory structures. Fourth, our data present several aggregated characteristics that may require disaggregation as cities contextualize the findings for their settings. Fifth, our network recruitment strategy led to skewed recruitment from some geographic regions (for example, North America and Nepal), which may have biased responses (Extended Data Figs. 1 – 3 ). Extended Data Table 1 shows the similarities and differences in the rankings for Nepal, USA and the remaining countries in survey 3. Additionally, we recruited few 14–17-year-olds. We experienced attrition over the three rounds of surveying, ending with complete responses from 261 individuals from 48 countries, with the greatest loss in participants between surveys 1 and 2 (Table 1 ), among the 14–17-, 18–24- and 25–35-year-old age groups, and among participants from Nepal (Extended Data Fig. 2 ).

Conclusions

We identified a set of priorities for cities that require intervention at multiple levels and across urban sectors. A clear next step could involve convenings to build national or regional consensus around local priorities and plans to engage stakeholders to co-design implementation of the most salient characteristics of a mental health-friendly city for youth in specific cities (Box 1 ). It is likely that many variables (for example, geography, politics, culture, race, ethnicity and sexual identity) will shape priorities in each city. Therefore, essential to equitable action is ensuring that an inclusive community of actors is at the table formulating and making decisions, and that pathways for generating knowledge of mental health-friendly city characteristics remain open. This includes representation of sectors beyond mental health that operate at the intersection of areas prioritized by young people. Preparing for implementation will require avenues for youth participation and influence through collective action, social entrepreneurship and representation in national, regional and community decision-making. Enlisting the participation of youth networks that bring young people marginalized owing to sex, gender, sexual orientation, race, economic status, ethnicity or caste; young people with disabilities; and youth and adults with lived experience of mental health conditions in the design of mental health-friendly cities will help to level power imbalances and increase the likelihood that cities meet their needs.

Action for adolescent mental health aligns well with actions nations should take to achieve development targets, and collective action to draw attention to these areas of synergy could benefit youth and cities. Specifically, supporting the mental health of young people aligns with Sustainable Development Goal 11 (sustainable cities and communities) and the New Urban Agenda that aims to “ensure sustainable and inclusive urban economies, to end poverty and to ensure equal rights and opportunities … and integration into the urban space” 73 , 74 , 75 .

Additionally, the list of mental health-friendly city characteristics presents a starting point for strengthening the evidence base on intervening at multiple levels (for example, individual, family, community, organizations and environment) to better understand what works for which youth in which settings. Cities function as complex systems, and systems-centred research can best enable us to understand how individuals’ interactions with one another and with their environments influence good or poor mental health 76 . Similarly, interdisciplinary inquiry is needed that investigates urban precarity and sheds light on social interventions for youth mental health 77 . New research that tests implementation strategies and measures mental health outcomes of coordinated cross-sectoral interventions in cities could be integrated with planned actions. Innovative uses of data that measure the ‘racial opportunity gap’ can help cities to understand how race and place interact to reduce economic well-being for minoritized young people on their trajectory to adulthood 78 . Even heavily studied relationships, such as mental health and green space, can benefit from new methodologies for measuring exposures, including application of mixed methods, and refined characterization of outcomes by gender and age with a focus on adolescents and youth 79 . Globally, mental health-supporting actions for young people in urban areas have an incomplete evidence base, with more peer-reviewed publications skewed towards North American research 73 .

Designing mental health-friendly cities for young people is possible. It requires policy approaches that facilitate systemic, sustained intersectoral commitments at the global as well as local levels 80 . It also requires creative collaboration across multiple sectors because the characteristics identified range from transport to housing to employment to health, with a central focus on social and economic equity. Acting on these characteristics demands coordinated investment, joint planning and decision-making among urban sectoral leaders, and strategic deployment of human and financial resources across local government departments that shape city life and resources 75 , 81 . This process will be more successful when cities intentionally and accountably implement plans to dismantle structural racism and other forms of discrimination to provide equitable access to economic and educational opportunities for young people, with the goal of eliminating disparate health and social outcomes. The process is made easier when diverse stakeholders identify converging interests and interventions that allow them each to achieve their goals.

Box 1 Considerations for implementing a mental health-friendly city for youth

Considerations for implementing a mental health-friendly city for youth using a structure adapted from UNICEF’s strategic framework for the second decade of life 82 and integrating selected characteristics identified in the study with examples distilled from scientific literature and from project advisory group members. Objectives for implementation along with corresponding examples and selected initiatives are shown.

Youth are equipped with resources and skills for personal and emotional development, compassion, self-acceptance, and flourishing.

Youth develop and sustain safe, healthy relationships and strong intergenerational bonds in age-friendly settings that respect, value and validate them.

Communities promote youth integration and participation in all areas of community life.

Communities establish and maintain safe, free public spaces for youth socializing, learning and connection.

Institutions facilitate satisfying, secure employment; progressive, inclusive, violence-free education; skills for mental health advocacy and peer support.

Policies support antiracist, gender equitable, non-discriminatory cities that promote democratic cooperation and non-violence.

Urban environments provide safe, reliable infrastructure for basic amenities and transportation; affordable housing; access to green and blues space; and access to recreation and art.

Cities minimize adverse social determinants of health; design for safety and security for vulnerable groups; and orient social and built environments to mental health promotion, belonging and purpose.

Use rights-based approaches

Prioritize equity for racially, ethnically, gender, sexually and neurologically diverse young people

Ensure sustained and authentic participation of youth

Schools and other educational settings

Health and social services

Families and communities

Religious and spiritual institutions

Child protection and justice systems

Peer groups

Civil society

Digital and non-digital media

Implementation objectives

Build consensus and contextualize the mental health-friendly city approach at local, regional, national levels

Engage diverse youth in co-design of mental health-friendly city plans

Expand opportunities for youth governance

Enable collaboration among sectors for policy alignment

Engage communities, schools, health services, media for intervention delivery

Legislate social protection policies

Scale interventions to improve economic and behavioral outcomes

Link implementation to achievement of national or international objectives

Selected implementation strategies

Youth co-design and participation: Growing Up Boulder is an initiative to create more equitable and sustainable communities in which young people participate and influence issues that affect them. It is a partnership between local schools, universities, local government, businesses and local non-profit organizations in the USA that has enabled young people to formally participate in visioning processes such as community assessments, mapping, photo documentation and presentations to city representatives 83 .

Engaging schools for interventions: universal school-based interventions for mental health promotion 84 ; linkage to mental health care for school-based programs 85 ; “Whole-school approaches” that engage students and families, communities, and other agencies to support mental health and improve academic outcomes 84 , 86 .

Digital platforms for youth mental health: Chile’s HealthyMind Initiative digital platform launched during the COVID-19 pandemic and provided a one-stop resource for information and digital mental health services. The platform included targeted evidence-based resources for children and adolescents 87 .

Interventions to test at scale: Stepping Stones and Creating Futures is a community-based intervention for intimate partner violence reduction and strengthening livelihoods in urban informal settlements in South Africa that reduced young men’s perpetration of intimate partner violence and increased women’s earning power 88 .

Shared international objectives: support Sustainable Development Goal 11 and New Urban Agenda targets and Sustainable Development Goals 1–6, 8, 10 and 16.

Project structure and launch

This study aimed to identify priorities for creating cities that promote and sustain adolescent and youth mental health. Central to achieving this aim was our goal of engaging a multidisciplinary, global, age-diverse group of stakeholders. As we began and throughout the study, we were cognizant of the risk of attrition, the importance of maintaining multidisciplinary participation throughout the study and the value of preserving the voices of young people. We used a priority-setting methodology explicitly aimed to be inclusive while simultaneously limiting study attrition. To ensure that we were inclusive of the voices of young people and our large and diverse sample, we limited our study to three surveys, which we determined a priori. Our approach was informed by standard methodologies for health research priority setting 32 .

The project was led by a collaborative team from the University of Washington Consortium for Global Mental Health, Urban@UW, the University of Melbourne and citiesRISE. We assembled three committees representing geographic, national, disciplinary, gender and age diversity to guide the work. First, a core team of P.Y.C., T.W., G.P., M.S. and T.C., generated an initial list of recommended members of the scientific advisory board on the basis of their research and practice activities related to adolescent mental health or the urban setting. We sought a multidisciplinary group representing relevant disciplines. The 18-member scientific advisory board, comprising global leaders in urban design and architecture, social entrepreneurship, education, mental health and adolescent development, provided scientific guidance. We invited members of an executive committee, who represented funding agencies as well as academic and non-governmental organizational leadership, to provide a second level of feedback. A youth advisory board, recruited through citiesRISE youth leaders and other global mental health youth networks, comprised global youth leaders in mental health advocacy. A research team from the University of Washington (Urban@UW, the University of Washington Population Health Initiative and the University of Washington Consortium for Global Mental Health) provided study coordination. The study received institutional review board approval at the University of Washington (STUDY00008502). Invitations to advisory groups were sent in December 2019, along with a concept note describing the aims of the project, and committee memberships were confirmed in January 2020. In February 2020, the committees formulated the question for survey 1: “What are the characteristics of a mental health friendly city for young people?”.

Study recruitment

The members of the scientific advisory board, youth advisory board and executive committee were invited to nominate individuals with expertise across domains relevant to urban life and adolescent well-being. The group recommended 763 individuals to join the priority-setting panel; individuals invited to serve on the scientific advisory board, youth advisory board and executive committee were included in panel invitations ( n  = 38). Our goal was to establish a geographically diverse panel of participants with scientific, policy and practice-based expertise corresponding to major urban sectors and related challenges (for example, health, education, urban planning and design, youth and criminal justice, housing and homelessness, and violence). Many of the nominees were experts with whom the core group and scientific advisory board members had collaborated, as well as individuals recruited on the basis of their participation in professional and scientific associations and committees (for example, Lancet Commissions and Series) or global practice networks (for example, Teach for All). Nominees’ names, the advisory member who nominated them, gender, country and discipline were tracked by T.C. We used snowball sampling to recruit participants from geographic regions that were under-represented: an additional 24 people were recruited through referrals. The scientific advisory board and youth advisory board sought to maximize the number of young people participating in the study, and invitations were extended to adolescents and young adults through educational, professional, advocacy and advisory networks. Nominees received an invitation letter by e-mail, accompanied by a concept note that introduced the study, defined key constructs, described the roles of the study advisory groups and provided an estimated study timeline. Youth participants (14–24) received a more abbreviated introductory letter. A link to a REDCap survey with an informed consent form and round 1 question was embedded in the invitation e-mail, which was offered in English and Spanish. Of the 824 individuals invited, 518 individuals from 53 countries provided informed consent and agreed to participate, resulting in a nomination acceptance rate of 62.8%.

Data collection

We administered a series of three sequential surveys using REDCap version 9.8.2. Panellists were asked to respond to the survey 1 question “What are the characteristics of a mental health friendly city for young people?” by providing up to five characteristics and were invited to use as much space as needed. In survey 2, panellists received 134 characteristic statements derived from survey 1 data and were asked to select their 40 most important statements. From these data, we selected 40 most frequently ranked statements. These were presented in the round 3 survey with three redundant statements removed. The remaining 37 characteristic statements were categorized across 6 socioecological domains and panellists were asked to select 1 of 3 framings by which to rank the statements in each domain: immediacy of impact on youth mental health in cities, ability to help youth thrive in cities, and ease or feasibility of implementation. Of individuals who consented to participate, 93.4% completed round 1, 58.5% completed round 2 and 56.2% completed round 3 (Table 1 ).

We added a new open-ended question to survey 2: “How has the COVID-19 pandemic changed your ideas about the wellbeing of young people in cities?”. Panellists were invited to respond using as many characters (that is, as much space) as needed.

Data analysis

Three-survey series.

We managed the survey 1 data using ATLAS.ti 8 software for qualitative data analysis and conducted a conventional content analysis of survey 1 data 89 . Given the multidisciplinarity of the topic and our multidisciplinary group of respondents, we selected an inductive method of analysis to reflect, as simply as possible, the priorities reported by the study sample without imposing disciplinary frameworks. In brief, responses were read multiple times, and characteristics were highlighted in the text. A list of characteristics (words and phrases) was constructed, and we coded the data according to emerging categories (for example, accessibility, basic amenities, career, built environment, mental health services and so on). The analysis yielded 19 broad categories with 423 characteristics. Within each category, characteristics were grouped into statements that preserved meaning while streamlining the list, which yielded 134 characteristic statements. The University of Washington research team convened a 1-week series of data discussions with youth advisers to review the wording of the characteristics and ensure their comprehensibility among readers from different countries. The survey 1 categorized data were reviewed by members of the scientific advisory board, who recommended that using relevant domains to group characteristics would provide meaningful context to the final list. We used IBM SPSS 28.0 for quantitative analyses of data from surveys 2 and 3. In survey 2, we analysed the frequency of endorsement of the 40 characteristics selected by panellists and generated a ranked list of all responses, with the most frequently endorsed at the top. The decision to select 40 characteristics aligned with methods applied in a previous priority-setting exercise 90 and permitted a list of preferred characteristics that could subsequently be categorized according to a known framework, allowing city stakeholders a broad list from which to select actions. We also analysed frequency of endorsement by age categories (18–24, 25–35 and >35). To amplify the viewpoints of younger participants (under age 35), we combined the top 25 characteristic statements of panellists over 35 with the top 26 characteristic statements of participants under 35 to generate a list of 40 statements, including 11 shared ranked characteristics. As noted, we removed three of these statements because of their redundancy. In survey 3, we analysed data consisting of 37 characteristic statements divided across 6 socioecological domains. Characteristics in each domain were ranked according to one of three framings. We calculated mean ranking and standard deviation for characteristics in each framing category per socioecological domain. Mean rankings (with standard deviation) were calculated across framing categories to arrive at the total mean rank per characteristic and they reflect the proportional contribution of each domain. We also calculated the frequency with which panellists ranked each characteristic statement number 1.

Our study methods align with good practices for health research priority setting as follows 32 .

Context: we defined a clear focus of the study.

Use of a comprehensive approach: we outlined methods, time frame and intentions for the results before beginning the study; however, we modified (that is, simplified) the methods for survey 3 to minimize study attrition.

Inclusiveness: we prioritized recruiting for broad representation and maintaining engagement of an inclusive participant group, and methodological decisions were made in service of this priority.

Information gathering: our reviews of the literature showed that a study bringing together these key stakeholders had not been conducted, despite the need.

Planning for implementation: we recognized from the outset that additional convening at regional levels would be required to implement action, and our network members are able to move the agenda forwards.

Criteria: we determined criteria for the priorities (framing: feasibility of implementation, immediacy of impact and ability to help youth thrive) that study participants used and which we believe will be useful for practical implementation.

Methods for deciding on priorities: we determined that rank order would be used to determine priorities.

Evaluation: not applicable; we have not planned an evaluation of the impact of priority setting in this phase of work.

Transparency: the manuscript preparation, review and revisions enable us to present findings with transparency.

COVID-19 qualitative data

We managed the COVID-19 qualitative data using Microsoft Excel and Microsoft Word. We carried out a rapid qualitative analysis 91 . First, the text responses were read and re-read multiple times. We coded the data for content related to expressions of change, no change or areas of emphasis in participants’ perceptions of youth mental health in cities during the pandemic. We focused our attention on data that highlighted changes. We further segmented the data by participant age categories, domains of change and suggested actions, and we assigned socioecological level of changes. We created a matrix using excerpted or highlighted text categorized according to these categories. Three data analysts (P.Y.C., T.C. and A.M.-K.) reviewed the domains of change and identified emerging themes, which were added to the matrix and linked to quotes. The team discussed the themes and came to consensus on assignment to a socioecological level. We prioritized reporting recurring concepts (for example, themes of loss, inequity, green space, isolation and mental illnesses) and contrasting concepts (for example, gains associated with COVID-19) and associated actions 92 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Survey data that support the findings of this study are available from the corresponding author, P.Y.C., on reasonable request. The sharing of data must comply with institutional policies that require a formal agreement (between the corresponding author and the requester) for sharing and release of data under limits permissible by the institutional review board.

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Acknowledgements

We thank M. Antia, S. Talam and J. Vollendroft for contributions to this project; H. Jack for contributions to the manuscript revision; and the survey panellists without whom this work would not have been possible. M.K. was supported in part by funding from the Fogarty International Center (K43 TW010716) and the National Institute of Mental Health (R21 MH124149) of the National Institutes of Health. This study was supported in part by funding to citiesRISE (M.M. and M.H.) from the Rural India Supporting Trust and from Pivotal Ventures. This study was conducted while P.Y.C. was on the faculty at the University of Washington, Seattle. The University of Washington (P.Y.C. and T.C.) received funding from citiesRISE by subcontract. T.D. is a staff member of the World Health Organization (WHO). The content and views expressed in this manuscript are solely the responsibility of the authors and do not necessarily represent the official views, decisions or policies of the institutions with which they are affiliated, including WHO, the US Department of Health and Human Services and the National Institutes of Health.

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Pamela Y. Collins, Augustina Mensa-Kwao & Emily Queen

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Contributions

This study was led by a core group, P.Y.C., G.P., M.S. and T.W., who were members of the project’s scientific advisory board and executive committee and part of the group of 32 co-authors (P.Y.C., M.S., T.C., G.P., T.W., L.M., A.M.-K., L.A., N.B., I.B., Y.C., T.D., E.d.L., N.F., H.H., S.K., M.K., B.L., O.O., J.M.U.-R., C.B., K.D., M.H., D.J., M.M., E.Q., Y.O., L.Z., N.A., P.M., J.U. and M.W.). P.Y.C. and T.C. regularly updated the core group members by e-mail, and P.Y.C. led online meetings with updates on study progress and data collection and study outcomes with members of the scientific advisory board (N.B., I.B., Y.C., T.D., E.d.L., N.F., H.H., S.K., M.K., B.L., O.O., J.M.U.-R. and K.D.), youth advisory board (K.D., C.B., D.J., Y.O., E.Q. and L.Z.) and executive committee (N.A., J.U. and M.W.). P.Y.C. (the core group lead) and members of the scientific advisory board and executive committee were involved with conceptualization, study design and methodology. Youth advisers assisted with qualitative data analysis. P.Y.C., T.C. and A.M.-K. were also responsible for data curation and formal analysis; P.Y.C. and T.C. wrote the original draft, with contribution from G.P., M.S., T.W., H.H. and L.M. P.Y.C., T.C., A.M.-K., M.M., H.H. and E.d.L. reviewed and organized responses to reviewers. All co-authors reviewed responses to the reviewers. P.Y.C. led the manuscript revision with A.M.-K., M.M. and T.C. All co-authors had the opportunity to discuss the results, review full drafts of the manuscript and provide comments on the manuscript at all stages.

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Extended data figures and tables

Extended data fig. 1 distribution of participants by nationality (n = 518) a,b,c ..

a Countries Participating: Argentina, Australia, Bangladesh, Cameroon, Canada, China, Colombia, Croatia, Czech Republic, Ecuador, Egypt, Ethiopia, France, Germany, Ghana, Haiti, Hong Kong, India, Iran, Italy, Kenya, Malawi, Mauritius, Mexico, Nepal, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Papua New Guinea, Peru, Philippines, Poland, Rwanda, Samoa, Sierra Leone, Slovenia, South Africa, South Korea, Sweden, Switzerland, Taiwan, Tanzania, The Gambia, Tunisia, Turkey, Uganda, UK, USA, Venezuela, Zambia, Zimbabwe (53 total); b Two responses (“Asian” and “Indigenous and European”) do not list a nation but capture verbatim open-text responses; c Countries with one participant removed from graph and include: Argentina, Bangladesh, Cameroon, Croatia, Czech Republic, Ecuador, Egypt, Ethiopia, France, Haiti, Hong Kong, Indigenous and European, Mauritius, New Zealand, Norway, Papua New Guinea, Samoa, Slovenia, South Africa, South Korea, Switzerland, Taiwan, Tanzania, The Gambia, Tunisia, Turkey, Uganda, Venezuela.

Extended Data Fig. 2 Participant Nationality by Survey Round.

a SEA = South-East Asia, NA = North America*, AF = Africa, LSA = Latin & South America*, EU = Europe, WP = Western Pacific, EM = Eastern Mediterranean.

Extended Data Fig. 3 Distribution of Participants by WHO Region * and Survey Round.

a SEA = South-East Asia, NA = North America*, AF = Africa, LSA = Latin & South America*, EU = Europe, WP = Western Pacific, EM = Eastern Mediterranean; *We separated North America from Latin & South America for more transparent display of participant distribution.

Supplementary information

Supplementary information.

Supplementary Note which describes citiesRISE and lists the project team members of Making cities mental health-friendly for adolescents and young adults.

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Abstract: The advent of large language models (LLMs) brings an opportunity to minimize the effort in search engine result page (SERP) organization. In this paper, we propose GenSERP, a framework that leverages LLMs with vision in a few-shot setting to dynamically organize intermediate search results, including generated chat answers, website snippets, multimedia data, knowledge panels into a coherent SERP layout based on a user's query. Our approach has three main stages: (1) An information gathering phase where the LLM continuously orchestrates API tools to retrieve different types of items, and proposes candidate layouts based on the retrieved items, until it's confident enough to generate the final result. (2) An answer generation phase where the LLM populates the layouts with the retrieved content. In this phase, the LLM adaptively optimize the ranking of items and UX configurations of the SERP. Consequently, it assigns a location on the page to each item, along with the UX display details. (3) A scoring phase where an LLM with vision scores all the generated SERPs based on how likely it can satisfy the user. It then send the one with highest score to rendering. GenSERP features two generation paradigms. First, coarse-to-fine, which allow it to approach optimal layout in a more manageable way, (2) beam search, which give it a better chance to hit the optimal solution compared to greedy decoding. Offline experimental results on real-world data demonstrate how LLMs can contextually organize heterogeneous search results on-the-fly and provide a promising user experience.

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AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business

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  • Published: 23 February 2024

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  • Declan Humphreys   ORCID: orcid.org/0009-0008-2693-7340 1 ,
  • Abigail Koay   ORCID: orcid.org/0000-0002-4130-9931 1 ,
  • Dennis Desmond   ORCID: orcid.org/0000-0003-1278-6306 1 &
  • Erica Mealy   ORCID: orcid.org/0000-0002-8119-151X 1  

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This paper examines the ethical obligations companies have when implementing generative Artificial Intelligence (AI). We point to the potential cyber security risks companies are exposed to when rushing to adopt generative AI solutions or buying into “AI hype”. While the benefits of implementing generative AI solutions for business have been widely touted, the inherent risks associated have been less well publicised. There are growing concerns that the race to integrate generative AI is not being accompanied by adequate safety measures. The rush to buy into the hype of generative AI and not fall behind the competition is potentially exposing companies to broad and possibly catastrophic cyber-attacks or breaches. In this paper, we outline significant cyber security threats generative AI models pose, including potential ‘backdoors’ in AI models that could compromise user data or the risk of ‘poisoned’ AI models producing false results. In light of these the cyber security concerns, we discuss the moral obligations of implementing generative AI into business by considering the ethical principles of beneficence, non-maleficence, autonomy, justice, and explicability. We identify two examples of ethical concern, overreliance and over-trust in generative AI, both of which can negatively influence business decisions, leaving companies vulnerable to cyber security threats. This paper concludes by recommending a set of checklists for ethical implementation of generative AI in business environment to minimise cyber security risk based on the discussed moral responsibilities and ethical concern.

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1 Introduction

The recent hype around AI has seen many companies rush to incorporate generative AI to their business strategy. A recent IBM study found that nearly 80% of UK businesses have already deployed generative AI in their business or are planning to within the next year [ 1 ]. The message to industry seems clear “Organizations are seizing the generative AI moment to capture opportunities … Those that don’t will be stuck in the control tower wondering why they’ve fallen behind.” [ 2 ].

Generative AI models take large amounts of data and are then trained to produce data that resembles the most commonly found elements. A Large Language Model (LLM) is a type of generative AI model that assigns statistical probabilities to a sequence of words. These probabilities help to generate human like responses in natural language processing tasks [ 3 ]. Companies are using these LLMs such as ChatGPT, LLaMA, Claude, and Gemini to aid many areas of business. The areas which are most likely to see the potential of generative AI to improve businesses are areas such as sales, marketing, software engineering, customer service and product research and development [ 4 ]. The benefits of its implementation are still being tested, but there is early evidence that AI-based assistants can improve the performance of novice or low-skilled workers [ 5 ].

However, there are growing concerns that the race to integrate generative AI is not being accompanied by adequate guardrails or safety evaluations [ 6 ]. A recent global survey on AI found that few companies were fully prepared for the widespread use of generative AI [ 7 ]. The rush to buy into the hype of generative AI, and not fall behind the competition, is potentially exposing organisations to broad and possibly catastrophic cyber-attacks or breaches. In the growing area of cyber security ethics, the hype around AI presents a novel risk, one which could lead companies to fail in their moral obligation to keep company and individual’s data safe and secure.

We have already seen Microsoft AI researchers accidently leak 38 TB of private training data [ 8 ]; Samsung employees inputting sensitive source code into ChatGPT, [ 9 ]; and a bug in ChatGPT exposing active user’s chat history [ 10 ]. Beyond the risk due to accidents or human error, there are more malicious threats posed by generative AI. Imagined scenarios could see targeted manipulation of the data driving a company’s model to spread misinformation or influence business decisions [ 11 ]. Risks are also increased with the reliance on third-party AI providers, with more than half (55%) of AI related failures stemming from third-party tools, companies can be left vulnerable to unmitigated risks [ 12 ].

It is evident that generative AI poses new and novel threats to business security. A recent IBM survey found that 96% of surveyed business executives expect that adopting generative AI will make a security breach likely in the next three years [ 11 ]. However, this report noted a “glaring disconnect between the understanding of generative AI cyber security needs and the implementation of cyber security measures” [ 11 ]. Reportedly, only 24% of generative AI projects will include a cyber security component within the next 6 months, with 69% of executives saying that innovation takes precedence over cyber security for generative AI [ 11 ]. A separate study found that 53% of organisations saw cyber security as a generative AI-related risk, with only 38% working to mitigate that risk [ 7 ].

The hype around generative AI in business, therefore, presents an area of ethical concern. Ethics is at the core of cyber security, as it is increasingly required to prevent harm to people, not just information, and to protect our ability to live well [ 13 , 14 , 15 ]. Companies have a duty of care toward their users, customers, and employees with regard to protecting the data they hold [ 16 ]. The world is now so reliant on secure networks and systems to protect identities, personal information, and livelihoods that breaches to can have major disruptions and disastrous effects on individual’s lives [ 17 ]. Beyond the effect on the public, it is in the financial interest of companies to focus on cyber security with the average cost of a data breach in 2023 being USD 4.45 million [ 18 ].

As our analysis of potential threats to generative AI models, such as LLMs, will show businesses need to be aware of the increased risk to privacy and security. While companies tout the vast benefits of generative AI for business productivity, there needs to be a greater focus on effective mitigation of threats posed to and by generative AI models [ 6 ]. Conversations of these risks have generally been kept within cyber security industry professionals, but there needs to be a wider understanding of the vulnerabilities which generative AI is susceptible to before organisations jump to using them. There is an ethical responsibility for business to consider the cyber security risk associated with generative AI, and for this information to be shared with the general public.

2 Cyber security as an area of ethical inquiry

As more and more data and information is stored online, and more services move to digital operations, the threat to the security and risk of harm also increases. The definition of cyber security has evolved over time and it often contested [ 19 ]. There remains the question as to whether cyber security is a role, a field, a discipline, or a practical application encompassing a combination of information security, operational security, network and communications security or other security focused disciplines.

A thorough and systematic review of historical definitions of cyber security by Schatz, et al. [ 19 ] arrived at a definition of cyber security that includes the key aspects of protecting information as the core asset. To wit: “ The approach and actions associated with security risk management processes followed by organizations and states to protect confidentiality, integrity and availability of data and assets used in cyber space. The concept includes guidelines, policies and collections of safeguards, technologies, tools and training to provide the best protection for the state of the cyber environment and its users.” [ 19 ].

Schatz’ inclusion of basic protection of the confidentiality, integrity and availability of information has become prescient with the advent of AI generated deepfakes, celebrity images, and AI journalism employing automated authors. This has also led to a greater focus on the ethical implications of cyber security processes and policy. Integrity, for example, is defined as guarding against improper information modification and includes ensuring information authenticity [ 20 ].

Cyber security is a growing field of ethical investigation, with developing literature into the ethical challenges, risks and issues associated [ 14 , 21 , 22 ]. Whether monitoring information flows of individuals, intrusive measures to identify child sexual exploitation material, or restricting access to online sites to deter terrorism and extremism, cyber security can be both intrusive and violate norms of privacy.

One issue faced by the cyber security ethicist is the broad nature of the field of cyber security. There has been a distinction made between the ethics of national or state based cyber security and business or commercial cyber security [ 14 ]. The former of these takes in topics such as the application of just war theory to cyberwar and espionage [ 23 , 24 , 25 ]. However, it is questionable whether cyberwar and espionage do fall under the purview of cyber security or whether cyber security provides a supporting capability to ensure their success.

Alternatively, in the private sector, there are numerous areas of inquiry that fit under the broad umbrella of cyber security ethics. Recent work has focused on the ethics of conducting cyber security research [ 26 ]; the ethical balance between needing internet traffic to be monitored for security, but also wanting it to be private [ 27 ]; the concept of “ethical hacking” to test security of networks or employees [ 28 ]; as well as the ethical obligations of businesses to protect their data [ 16 , 29 ].

We will concentrate on the new ethical challenges presented by generative AI and the resulting cyber security implications for an organisation. To narrow the ethical focus of this paper, we will concentrate on the moral responsibility businesses have to protect their assets as well as user and employee data. It will be shown that the ethical considerations for cyber security on business have clear crossovers for the implementation of generative AI.

Whereas generative AI for public consumption is a relatively new phenomenon, many of the ethical considerations can be derived from previous research and applications of ethics to cyber security activities. The ability to apply ethical considerations to emerging technologies will continue to challenge cyber security professionals as new applications appear and see mainstream adoption.

3 Literature review

In this section we look at the background literature related to AI in cyber security as well as the growing literature on the ethical issues around generative AI tools, such as ChatGPT. We will conclude by showing where the gaps in the literature lie and clearly note the contributions this paper makes to the field. We note that, while there is literature around the risks of generative AI tools, such as ChatGPT, this has not yet translated into the discourse of business ethics. This paper takes the unique angle of framing the implementation of generative AI as a question of business ethics and cyber security ethics.

3.1 AI in cyber security

The relationship between AI and cyber security is not new, with autonomous or semi-autonomous systems for cyber security defence being on the market for a number of years. In 2017, for example, DarkLight was released in what was then called “first of its kind” artificial intelligence tool to enhance cyber security defence [ 30 ]. There has since been literature highlighting the beneficial uses of AI in cyber security defence.

Early uses of AI in cyber security were based around developing discriminative-based machine learning (ML) or deep learning (DL) AI models. ML tools are capable of discriminating data through classifying information, and recognising specific patterns [ 31 ]. Though powerful, ML is also limited in terms of threat detection as it acts according to pre-defined features, meaning that any features not pre-defined will evade detection [ 31 ]. DL models, a subset of ML, on the other hand are able to learn high-level abstract characteristics, or deeper features of given data, making them excel at things like image and speech recognition, text analysis and natural language processing [ 32 ]. This benefits cyber security as it enables the detection of unknown attackers or novel forms of malware. AI assists in cyber security through constructing models for malware classification, intrusion detection and threat intelligence sensing [ 18 ]. Because AI has the ability to extract patterns from large datasets, and adapt to new information, it can accurately make predictions to improve cyber security [ 33 ].

3.2 Cyber security of AI

While the benefits of AI in cyber security have become evident in the preceding years, the malicious threats to AI models have also been recognised. ML and DL models used in AI systems such as recommendation systems or facial recognition are susceptible to ‘poisoning’ or manipulation, potentially undermining their integrity and useability [ 3 , 6 , 34 ]. In practical terms, injecting misleading or incorrect data into an AI model used for cyber security defence can skew its decision making causing it to overlook vulnerabilities or misidentify threats [ 33 ].

Since the increased popularity of generative AI, spurred by the release of ChatGPT in 2022, new discussions have surfaced on the usefulness and risks of such technology. Generative AI is a branch of ML and DL which is capable of creating new data that is similar to its training data set [ 35 ]. Large language models, such as ChatGPT, use text as their dataset, and have caused a boom in AI interest and hype.

The use of generative AI been explored in areas such as healthcare [ 36 , 37 ], education [ 38 ], academia [ 39 ], creative industries [ 40 ], journalism, and media [ 41 ]. At the time of writing, empirical study of the effect of generative AI within work and business is in its infancy, yet its far-ranging impacts are being explored. Studies have so far looked at the effect of generative AI in areas such as call centres [ 5 ], on knowledge worker productivity and quality [ 42 ], risk management and finance [ 43 ] and on operations and supply chains [ 44 ].

3.3 Ethical concerns and risks in AI

For all the new applications and advances in efficiency which generative AI is showing, it has also undoubtedly brought concern with recent work focusing on the ethics around generative AI and ChatGPT [ 45 ]. Some of this literature focuses on the threat which generative AI will have for jobs [ 46 , 47 ], bias in training data affecting its output [ 48 , 49 ], or a diminishing of critical thinking and problem-solving skills amongst users [ 50 ]. Other concerns circle around the threat of disinformation [ 51 ], manipulation of public sentiment [ 52 ], and a widening socio-economic inequalities [ 46 ].

With regards to cyber security, recent work has highlighted the risks to generative AI models such as ChatGPT, and their susceptibility to data poisoning and manipulation [ 3 , 6 , 34 ] similar to earlier ML or DL models. Companies making AI models, such as Open AI and Google, have published their own findings on the risks associated with these models and the techniques they used to train them [ 53 , 54 ]. Generative AI has also reduced the barrier of entry for cybercriminals, helping in malware creation and phishing attacks [ 55 ].

Literature on the cyber security risk of generative AI for business is beginning, with ChatGPT in particular being cited as a potential risk. This includes the risk of data breaches or unauthorised access to user conversations as well as the risk of staff putting sensitive information into the program [ 56 ]. However, there is still a gap in literature translating the technical threats of generative AI into a business setting.

While we have noted some of the ethical issues raised by generative AI, limited work has been done in systematically applying ethical frameworks or lenses to these issues. Schlagwein and Willocks [ 57 ] apply deontological and teleological lenses to judge the ethical use of AI in research and science. Illia et al. [ 58 ] apply a stakeholder theory approach to the ethics of using AI for text-generation in business. The latter, arguing that the use of AI agents diminishes direct communication between stakeholders, potentially causing misunderstandings and leading to a decreased level of trust between parties.

Our paper will look at issues in generative AI in business, through the lens of ethical principles similar to those found in bio-ethics, namely: beneficence, non-maleficence, autonomy, justice and explicability. This builds on work in applying ethical principles both to AI [ 59 ] and to cyber security [ 14 ].We note that not all are convinced of the efficacy of a principlist approach to AI ethics, Bruschi and Diomede [ 60 ] provide a useful summary of this argument. However, while our paper focusses on generative AI, it also does so by looking at it as a technological innovation in the workplace. Thus, we build upon literature which applies ethical principles to the introduction of new technology in society and into the workplace [ 61 , 62 ].

From this review we can see that there is growing literature outlining the risk which could befall Generative AI models. However, this concern has not yet been translated into discourse around the ethical implementation of generative AI for business. This is evidenced by the lack of awareness or concern around the cyber security risk of gen AI amongst business leaders [ 11 ]. This paper therefore makes the following contributions:

Supports the case for cyber security being an ethical obligation for business, using normative ethical principles.

Highlights literature on the cyber security risks associated with generative AI, including the risks of poisoning, manipulation, and data leakage.

Demonstrates how the risks associated with generative AI can threaten business operations and their responsibilities to stakeholders.

Makes the case that businesses have an ethical obligation to consider the cyber security risk of generative AI and provides suggestions based on ethical considerations and analysis.

4 Cyber security of AI as an ethical obligation for business

While many have recognised the need for ethics in cyber security, there has been little clear consensus about the most appropriate framework from which to investigate ethical issues the field. Some advocate for the use of traditional frameworks of deontology, utilitarianism and virtue ethics [ 21 ] while others have proposed using a principlist approach adopted from areas such as bio-ethics [ 14 ].

While broad moral theories of utilitarianism or deontology provide guidance, their effectiveness falls when applied to situations which require pragmatic solutions [ 14 ]. The contextual nuances of cyber security provide difficulty in applying such general theories. For example, some have noted the substantial difficulty in applying a general theory of consequentialism or deontology to a process such as tracking a hacker through the machines of innocent persons [ 63 ].

Greater success has been found in applying ethical principles like those adopted in the field of bioethics. To analyse the ethical obligation of implementing generative AI in business with respect to cyber security concerns, we propose a combination of the ethical framework for a Good AI society from Floridi et al. [ 59 ] and the principlist ethics for cyber security from Formosa et al. [ 14 ].

It is our contention that the application of the moral principles of beneficence, non-maleficence, autonomy, justice and explicability are the most suitable to analyse the ethical concerns regarding the cyber security risks of generative AI for companies. Because adoption of generative AI in business combines both issues of ethical AI and ethics of cyber security, there is utility in applying such a set of principles.

It is evident now that generative AI will have a major impact on the way companies do business, but there are still questions around the opportunities and risks associated with its adoption. An ethical adoption of generative AI should also take into consideration the cyber security risk associated with its implementation. In the next few subsections, we present how the ethical principles of beneficence, non-maleficence, autonomy, justice and explicability relate to businesses adoption of generative AI considering cyber security.

4.1 Beneficence

A core principle of bioethics, beneficence concerns promoting well-being or “doing good”. Implementing a technology such as AI should be for the common good and to generally promote the well-being of people [ 59 ]. Similarly, beneficence in cyber security means protecting privacy and personal data, which subsequently promotes well-being of the public [ 14 ]. Good cyber security also has the added benefit of enhancing the reputation of a company and building trust among their customers.

While AI presents certain risks as we will outline in the following sections, it also opens beneficial opportunities for business such as the potential to increase productivity and reduce workloads on staff [ 5 ]. In cyber security, for example, generative AI can increase threat detection, automate repetitive tasks, scan for threats and learn to detect threat patterns to detect malicious traffic on a network [ 56 ].

It should be noted there is an issue of value judgements when identifying benefits of adopting a new technology. What is best for a company in terms of their bottom line might be different to what is best for individual workers and what is best for the company’s customers.

4.2 Non-maleficence

Non-maleficence or the “do no harm” principle, warns against causing harm or making our lives worse-off overall [ 14 ]. Regarding the development of AI, there should be caution “against the many potentially negative consequences of overusing or misusing AI technologies” [ 59 ].

Similarly, steps must be taken in cyber security to prevent unduly increasing threats or harms to business or other stakeholders. Cyber security practices focus on three core principles: confidentiality, availability and integrity (known as the CIA triad) [ 22 ]. Where confidentiality is broken, information is made unavailable, or the integrity of data is compromised then harm can follow [ 14 ].

In both digital ethics and cyber security, any technology which is implemented in an organisation must be done so with the consideration of the type of harm which could occur and the likelihood of such harm occurring. Accordingly, introducing generative AI must also be done without increasing the risks of harm through breaches in cyber security. Harms can include economic and psychological harms to individuals who, for example, have to go through the stress of being victims of theft or identity fraud [ 17 ]. Harm can also come in the form of financial or reputational loss for organisations [ 17 ]. Organisational planning and work to prevent such harm occurring falls under the principle of non-maleficence [ 14 ].

4.3 Autonomy

In medical ethics, autonomy refers to ability for everyone to have a right to decide for themselves about their own treatment. Autonomy in relation to AI becomes more complex, as we willingly give over forms of control over decision-making power to machines [ 59 ]. Autonomy means balancing what we decide to do for ourselves, and what we give over or delegate to systems and machines [ 59 ]. It can refer to the ability for human agents to be able to choose when to implement, or what decisions to take based on AI recommendations.

There is a crossover here with ethics in cyber security, as autonomy requires the ability for individuals to have access to their data and systems [ 14 ]. Cyber security can prevent unauthorised access to our data but should also give some control over user privacy [ 14 ].

Generative AI provides a distinct ethical consideration regarding autonomy. Data scraping for training AI models takes away the autonomy of individuals to choose to have their data used, possibly infringing on privacy and intellectual property rights. One such example is an artist having their work used to train a model which can subsequently generate new simulated works matching their unique style [ 64 ]. The nature of generative AI models means that once data has been used in its training, there is no option to ‘take-it-back’ or withdraw consent later without deleting the model and starting from a new training set. As we will see when we look at risk factors of generative AI, this could leave individual data exposed to malicious actors with little in the way of protection.

Businesses incorporating generative AI must consider how the data used to their model was trained or sourced. If it is based on customer data for example, should those customers need to give specific informed consent for their data to be used in AI training?

4.4 Justice

There are many conceptions of justice, most of which revolve around promoting fairness and equality. It can also refer to the distribution of benefits and harms, considering their impacts on the least advantaged groups [ 14 ].

Justice with regard to AI means acting to eliminate unfair discrimination, create shared benefits, and prevent the undermining of social structures [ 59 ]. AI development, while bringing many opportunities for innovation, also has the risk of maintaining social inequalities rather than improving them. A feature of LLMs is their propensity to maintain stereotypes and bias [ 65 ]. Businesses implementing AI or generative AI must consider the wider social or justice implications of such technology.

Justice in cyber security should also consider the protection of property, data, and privacy rights [ 14 ]. As much as control over digital privacy is a matter of preserving autonomy , it is also a matter of justice and procedural fairness. Those who are affected by a technology should have a fair opportunity to challenge it. Some questions which will soon come to the fore regarding generative AI are around whether customers have a capability to opt out of their data being used to train AI models. If their data is exposed in a generative AI hack, who is responsible? What legal avenues could they pursue? This will be a matter for law and policy to decide, however, no business will want to be known as the first to have a data breach due to a generative AI attack.

4.5 Explicability

As a feature of procedural fairness, Floridi et al. [ 59 ] point out that there is a need to be able to understand and hold to account decision making in AI, considering both explicability and accountability. “Explicability” can broadly be considered as an answer to the question “how does it work?”, while “accountability” an answer to “who is responsible for the way it works?” [ 59 ]. As with autonomy, there are ethical issues around transparency and accountability.

Formosa et al. [ 14 ] point out that explicability in cyber security also includes procedures for holding people and organisations accountable for failures. The rapid incorporation of AI technologies into the workplace and society broadly, has also led to a rush of people trying to understand the capabilities and limitations of these technologies. Implementing an AI solution into business should also come with relevant training as it should be clear who is accountable and responsible for its use. If a company uses a third party to create a generative AI model, that somehow becomes a threat or leaks valuable information, whose responsibility is it? The company implementing it, the user who utilised it for that task, or the one designing and training the model?

5 Business implementations of AI and large language models – buying into the hype

While some might see cyber security as a technical field meant only for the protection of systems and networks, ultimately the aim of the cyber security professional is to protect the well-being of the public at large [ 13 ]. As an ever-increasing amount of data is gathered and stored about us, there is also an increasing obligation for companies to keep that information secure. The spate of large-scale hacks where private and sensitive information has been leaked has sparked calls for greater responsibility to be taken by companies who handle and store such data [ 66 ]. The implementation of generative AI expands the threat horizon. As companies rush to implement AI, they also have an obligation to understand and work to minimise the threats and subsequent harms this technology could bring.

To many, the main threat AI tools present lie in their ability to replace workers or eliminate traditional human-centred roles. To others, replacing humans with AI tools removes flexibility and responsiveness and takes out the humanity of traditional, customer-oriented services. However, to early adopters, AI is seen as a panacea of efficiency and effectiveness, removing the barriers to improving customer service and business while expanding business opportunities into previously unknown areas. To these business owners, AI tools work 24/7, do not ask for time off, can be modified at will, and do not suffer from the traditional personal and professional challenges of human employees. Where AI tools have not replaced human employees, AI tools are seen as enhancements to human-centric jobs and can improve their performance and responsiveness significantly.

However, with the adoption of early generative AI tools come higher error rates and challenges in fine tuning them to support traditional business models. A lack of understanding of how proprietary company data, once fed into an LLM, exposes the company to potential IP issues. Further, as many users have discovered, generative AI output is only as good as the data used to train the model. Generative AI results have often yielded biased, racist, and often incorrect information owing to ineffectual model tuning, limited cross validation process and operationalisation. Therefore, owing to a lack of critical thinking and analysis skills in the corporate sector may result in both poor performance and embarrassing results.

While long term expectations are that AI tools will undoubtedly result in business efficiencies, reduced labour costs, and the ability to increase the number of customers served, the short-term prognosis for their use has been mixed. Positively, the advent and adoption of AI tools has meant the creation of new job positions such as prompt engineers, Machine Learning trainers and validators, AI deployment specialists, and coders. We would also expect that new positions as AI ethicists and data control and evaluation specialists would also be a part of the new technology explosion.

5.1 The cyber-threat of AI adoption

The mass adoption of generative AI will amplify existing cyber and information security threats bringing new areas of concern. In the cyber security field, hackers and cyber criminals have also adopted AI to support hacking, online scams, and phishing emails [ 56 ]. AI serves as a force multiplier while enhancing the skills of previously mediocre cyber criminals. Despite numerous controls and safety measures, entire websites are devoted to circumventing these controls and jailbreaking existing tools. In some cases, Darkweb hackers now offer tailored AI tools to support online criminal enterprises [ 67 ]. Hackers have also traded in stolen ChatGPT login credentials, creating targets for information theft as ChatGPT profiles store a history of queries and responses [ 68 ].

Owing to its rapid deployment and universal adoption throughout the public and private sectors, there is a greater risk that generative AI could be ‘hacked’ or otherwise misappropriated. While most software applications are traditionally extensively evaluated for security and vulnerabilities, this has been lacking in generative AI. In traditional software development models we can trace a “bug” back to its cause, even if that cause is a complex interrelation with other programs, libraries services or even time itself, but generative AI adds another dimension since it’s based on such large data sets, The creative use of seemingly innocuous applications such as generative AI by criminals and adversarial nation states often results in technology surprise and creates new lines of exploitation. Whereas policy and regulatory controls are often lacking with these new technologies, their adoption without due consideration places organisations at risk. This exacerbates the potential risk with the rapid implementation of AI in workplaces, without sufficient thought or oversight.

6 Cyber security risk factors for generative AI and large language models

The following threats have been identified by cyber security researchers, and as of yet have not been known to be maliciously exploited. Even though some of these threats remain speculative in their possibility, they give reason to consider the safety of generative AI models.

6.1 Data poisoning

Firstly, there is a risk that bad actors could manipulate training data which is used to create generative AI models like LLMs. LLMs are trained on data sets scraped from across the internet, a malicious actor could store altered or ‘poisoned’ information waiting for that model to scrape the training data as it is updated [ 54 ]. This poisoned data would then surface in responses given by the model. This is especially true with the recent creation by OpenAI of personal GPTs [ 69 ]. Personal GPTs can be created by anyone to operate alongside of OpenAI’s ChatGPT and may be narrowly focused on one field or topic area. These GPT models are trained and validated the same way as other GPTs but with a narrowly defined set of input data. If the data is skewed or biased, the resulting output will reflect the ingested data. Not only could this lead to incorrect or skewed data, but it could also be used to support extremist viewpoints or to exploit vulnerable user groups.

Historically, data seeding has been used to influence Internet users through data propagation and search engine optimisation [ 70 ]. This strategy has now evolved to influence AI LLMs by prepopulating websites, social media and databases with information that data training will ingest and incorporate into AI results. A recent report by Google outlined an example where an attacker might want to influence public sentiment about a politician, so that whenever the model is queried about that politician it gives a positive response [ 54 ]. The researchers pointed out that is possible for an attacker to buy expired domains that used to have content about a politician, modifying it to be more positive [ 54 ]. The follow-on effect being that an LLM which scraped those sites would proceed to give those favourable results when asked. Further research indicated that an attacker only needs to control 0.01% of a dataset to poison it, which could be done for a cost of just US$60 [ 34 ]. If this is correct for all datasets, then there is a low barrier for someone able to poison any dataset and undermine the reliability of the subsequent model.

While influence operations have historically been the purview of governments, the integration of AI tools used by the masses makes disinformation campaigns and influence operations available to anyone. As we’ve seen recently, companies training AI have run afoul of copyright claims, but their tool flexibility and ease of access may also violate the CIA triad identified by Schatz et al. [ 19 ]. The use of autonomous tools designed to respond to human interrogatories with false, private or biased information is not generally addressed within our traditional view of cyber security. Unless we treat AI as a potential bad actor, those actions, controlled by complex rulesets and instigated by prompt engineers, may simply be viewed as anomalous and not worthy of consideration as a separate entity within our definition of cyber security.

Others have similarly argued that disinformation meets the conditions to be considered a cyber security risk due to the threat to business reputation, calling into question the integrity of data, and the psychological threat to individuals due to distrust [ 71 ]. Whether or not disinformation is directly an issue of cyber security, it has nonetheless been seen as a business risk to consider, due to the potential of influencing investment decisions or causing supply chain disruptions [ 72 , 73 ].

OpenAI specifically addresses the potential misuse of language models for disinformation campaigns by various actors including “ propagandists for hire ” [ 74 ]. Potential solutions to mitigate the impact of propaganda and disinformation campaigns include improved fact-sensitive models, tagging information for easier tracking, government control over data collection and AI hardware.

6.2 Training data extraction

Early test attacks on GTP-2 showed that it was possible for adversaries to extract specific examples of training data just by querying large language models [ 3 ]. The test showed the possibility of extracting exact words and phrases used in the training of the model, alarmingly this included public personally identifiable information such as names, phone numbers and email addresses [ 3 ]. This information only needed to appear once in the training data. In February 2023, a Harvard University student used a ‘prompt injection’ attack on Bing chat to gain access to a document otherwise hidden to users [ 75 ]. This could be a risk as many companies are training their own internal LLMs. A company which is training its own LLM with proprietary information could run the risk of having sensitive information exposed through such an attack.

6.3 Backdooring the model

More alarmingly, is the risk for indirect prompt injection [ 6 ]. In this case attackers can strategically inject prompts into training data, which can then allow attackers to indirectly exploit or completely take control of a system, without the need to access the model itself [ 6 ]. Similar to the example of data poisoning, a training data set could include malicious content that, instead of providing misinformation, could provide specific coded instructions for the model to follow.

Google researchers have pointed out that a model could be built with hidden outputs when a specific “trigger” is activated [ 54 ]. This code could, for example, trigger a download of malicious code onto the user’s device or control certain outputs of the model, changing the response or action the model takes. The researchers give the example of an attacker uploading a new kind of AI image classification tool to GitHub. While the program appears to run smoothly, the attacker could have stored malicious code to download malware on a device after a certain trigger is activated [ 54 ].

These are just some of the examples of ways in which malicious actors might be able to manipulate and otherwise affect the reliability of AI models.

6.4 Adversarial prompting

LLMs are generally built with safeguards around generating contents that are harmful and misaligned with common moral and ethical standards. However, several researchers have demonstrated that using specific or augmented prompts can bypass the safety measures and trick these models into providing harmful content. Typically referred to as “jailbreaking,” there are numerous online resources that provide instruction to users on developing prompts that will bypass the controls of the AI engine [ 76 ]. A jailbreak prompt instructs the AI engine to ignore any previous coded instructions, emulate another, less restrictive engine, or incorporate specific attributes to respond to the user’s instructions. An example that has been used previously by users is to invoke the Do Anything Now (DAN) mode in ChatGPT. While in DAN mode, ChatGPT is more responsive to user requests that potentially violate its rules.

7 Ethical implications of generative AI risk

We now turn to the ethical implications for the risks mentioned in Sect. 6. As some of the examples in Sect. 6 have shown there are multiple attacks which AI models could be vulnerable to. It is important that businesses who are planning to implement such tools within their organisation recognise and be alert to the potential harm that could come from such use. We will use the ethical principles for cyber security outlined in Sect. 4, to show what ethical concerns businesses must consider in light of the cyber security risk of generative AI models.

These threats outlined in Sect. 6 are enabled or exacerbated in two ways, by users either (a) over-relying on the output of an AI program or (b) over-trusting what information they give over to it. Firstly, by over-relying on the output of a generative AI model, employees risk making potentially harmful decisions or exposing systems to malware through phishing scam attacks. Secondly, by over-trusting the security of training data or the information put into an LLM model, there is the increased risk for data leak or theft.

7.1 Overreliance

There is evidence to suggest that people are susceptible to overreliance on AI decision making, even when it is detrimental to their work [ 77 , 78 ]. Instead of combining critical thinking and their own insights into a problem along with an AI model, people frequently over-rely on the AI even if they would have made a better choice on their own [ 77 ]. This is also known as ‘automation bias’. Pilots have been shown to place trust in incorrect automated processes, even if they would not have done so without automated recommendations [ 79 , 80 ]. Pilots must go through special training to overcome these types of automation bias. When generative AI solutions are implemented in business, there must be a consideration of what training will be sufficient to combat overreliance or automation bias.

One solution proposed to combat overreliance has been explainable AI (XAI) where a system gives reasons for its decision. The idea being that if a system can give people an explanation for how it came to a decision, they might be more easily be able to spot errors, reducing overreliance. However, it is debatable whether explainable AI does reduce overreliance and more research is being done on what circumstances explainable AI could be effective [ 78 ].

It is widely recognised that generative AI systems have the capacity to hallucinate, casting doubt on “the whole information environment” [ 53 ]. Beyond hallucinations, as the above analysis of cyber threats show, the capacity for malicious actors to purposely poison output from such models to give incorrect information gives extra cause for concern. There is a risk that hackers could change the data driving a company’s AI model, potentially influencing business decisions with targeted manipulation or misinformation [ 11 ].

In line with the principle of beneficence, ethical implementation of generative AI in business should be of benefit to employees, promote well-being and make the workplace better overall. Guardrails should be in place to ensure that its implementation is not providing more avenues for employees to make mistakes, which could potentially lead to cyber security risks.

The introduction of generative AI must also be done without risking increasing threats or harms to business or other stakeholders. Non-maleficence warns against the negative consequences of overusing or misusing AI technologies [ 59 ]. The adoption of generative AI within a company should be done while recognising the increased risk of a cyber security incident. For example, over-relying on generative AI in coding can serve as a more immediate cyber security threat, as past versions of GitHub’s Copilot were found to recommend insecure and vulnerable code to developers [ 81 ]. However, few companies are prioritising protection against the cyber security risk of generative AI [ 11 ].

The level of overreliance on the system as a source of truth, where users are not trained or used to questioning its output can increase the threat of cyber security breaches and subsequent harm. Overreliance could also be exploited by indirect prompt injection, with researchers demonstrating the possibility for a ‘hacked’ LLM to elicit information from a user [ 6 ]. By injecting instructions into an LLM, researchers were able to have the model ask users questions, enabling them to gain information such as the user’s real name [ 6 ]. If workers over-rely on a generative AI system, they might give over such information in a conversation without thinking of it as being a risk.

The issue of AI literacy, education and equality must be emphasised when integrating generative AI. Once trust and ubiquity of generative AI in business has been built, businesses should consider whether there will be a threat of delegating too much to machines, thereby threatening the autonomy of workers. Moreover, what impact could this have to erode the capacity of workers to make choices, especially in significant decision making? Over time, and once AI models become engrained in the operations of a workplace, employee capacity to judge the lines between what the AI can and cannot do might become blurred. For example, is a new staff member, going to understand when to rely on an AI decision and when not to? This will also be a challenge as there is evidence that LLMs change their behaviour over time [ 82 ].

We earlier defined justice as it relates to AI as promoting fairness, equality, and shared benefits. As a matter of justice, the displacement of jobs is a recognised threat of AI integration, threatening fairness and equality [ 46 ]. Generative AI can be used in internal business process such as human resource management (HRM) for training and development initiatives, resource allocation and employee engagement [ 83 ]. But HRM decisions also have an impact on individuals, such as who gets hired or fired, who gets better appraisals, or who is put on preferred projects [ 84 ]. These type of decisions all have psychological impacts on employees [ 85 ]. If generative AI is used in the process of evaluating staff performance it must be done so in light of distributive justice (everyone is treated the same way by the system) and procedural justice (the processes employed to reach a decision are transparent) [ 84 , 86 ]. This last point concerns the principle of explicability . When implementing a generative AI system, its use and capabilities should be explainable to all users. Management and employees should know why certain systems are used, how they make their decisions and on what information in order to reduce possible overreliance.

7.2 Over-trust

The second factor we identify is what we term as over-trust in generative AI systems. This refers to the degree to which users trust a model with sensitive information, or trust that it is safe and secure. Studies have found that a proportion of employees have pasted sensitive information into ChatGPT [ 87 ]. Companies such as Samsung moved to ban employees using ChatGPT as a result of company proprietary material being placed into the program [ 9 ]. There is also increasing trust placed in third-party AI providers, without always a consideration of the cyber security risks [ 12 ].

Some companies have moved to create their own in-house AI models trained on company data and information to assist staff with queries. BloombergGPT, for example, an LLM that was purpose built from the scratch for finance by Bloomberg [ 88 ]. The training and use of such a model brings its own security challenges, as we have seen such models are susceptible to data extraction attacks [ 3 ]. Bloomberg, for their part, chose not to release their model citing security concerns of a model trained on so much company data being potentially exposed through nefarious means increasing risk for harm [ 88 ]. Training such a model is cost intensive and not something which is an option for many businesses.

Large companies such as Morgan Stanley are using cloud-based systems only accessible to its employees. While some argue a leak of confidential or private information “should not be a problem” [ 89 ] this thinking ignores the risk of internal threats and of actors trying to use attacks such as training data extraction. It also ignores the risk of the model being otherwise leaked, as happened with Meta’s AI language model LLaMA [ 90 ]. There is also the risk of accidental data leaks, such as the recent 38 TB of data accidentally exposed by Microsoft AI researchers [ 8 ].

Companies such as Salesforce have touted promises of plugging the AI “trust gap”, promoting services to protect company information while using AI tools, a package which will reportedly cost businesses $360,000 per year to implement [ 91 ].

With companies implementing domain specific LLMs, in an unregulated market, ethical considerations should still be implemented to protect the security of data. By applying the ethical principles from Sect. 4, we can see how over-trust in a new and untested technology presents ethical issues for companies.

In terms of beneficence, there are many positive benefits for the training of generative AI and large language models on proprietary content or knowledge [ 89 ]. This can be useful in assisting customer-facing employees find information about company policy, solving customer problems, or keeping employee knowledge when they leave the organisation [ 89 ]. In implementing such a strategy, companies and staff must have best practises in mind, and continually revise its use. Morgan Stanley reportedly used 1,000 financial managers to fine tune its model for safety and use [ 92 ]. However, this kind of resource intensive safeguard is not something that is practical for all businesses.

By trusting generative AI systems to store and process data, organisations could also be exposing themselves to added security threats. Non-maleficence (“do no harm”) in this case does not just mean intentional harm, but also means preventing accidental harm or the harm from the “unpredicted behaviour of machines” [ 59 ]. Placing data or sensitive information into generative AI models, could increase the threat of infringing upon personal privacy by increasing a company’s exposure to cyber-risk. Generative AI models can be susceptible to attacks such as prompt injection attacks or data extraction attacks, both of which have the potential to leak sensitive data [ 6 ]. If we consider that IBM estimates that only 24% of generative AI projects will include a cyber security component within the next 6 months [ 11 ], then this rush to adopt AI is leading to users being exposed to unnecessary consequences.

A new question raised by generative AI is what autonomy do customers have over their information being stored or used in a model which potentially has flaws in security? If generative AI programs become widespread and ubiquitous in business, should customers have to give their consent for their information to be either (a) be used in the training set of a model; or (b) to be inputted into the finished model?

Regulations about the business use of these models is on the horizon, but there are many questions still to be considered. If users or customers have a right for their data to be erased from a database, such as under the rules of the GDPR, similar protections cannot be offered once a model has been trained a person’s data. There is also no option to later withdraw consent once a model has been trained. Mechanisms and best practice around the use of customer information, which could threaten autonomy, must be taken into consideration. The ethical AI guidelines Floridi et al. [ 59 ] point out that the autonomy of humans should be promoted, while also limiting the autonomy of machines, and making them intrinsically reversable. The problem with LLMs is that they are lacking in the capacity to be reversable.

Justice in both AI and cyber security encompasses the protection of rights, in particular the right to privacy over data. In using and training models with data taken from users, for example, there must be a consideration for the protection of this data. The susceptibility of models to attacks can include the threat of information or data theft [ 6 ].

Justice can also refer to recourse available when something goes wrong with AI systems or in cyber security. As more companies use LLMs the greater the risk becomes of data being leaked. Without clear guidelines or regulation in place, what recourse do users have if their data is used in a training model and then subsequently exposed? If a company is using a third-party AI provider, is it clear where the responsibility for any failures lies?

A follow on from the ethical considerations of justice, is the principle of explicability. With the rapid implementation of generative AI, are customers being informed whether their data is being used to train new company models? Large companies such as Facebook, Amazon and X (formerly Twitter) all have plans to train LLMs using user data [ 93 ]. Amazon plans to train its LLM using voice data from Alexa conversations [ 93 ]. Do customers need to opt-in to their data being used to train generative AI models? If their data is exposed in a generative AI hack, who is responsible? What legal avenues could they pursue? Explicability entails who is made accountable for failures in cyber security, in the result of a breach due to generative AI, do companies know who would be at fault or where the responsibility lies?

8 Ethical implementation of generative AI

The above analysis shows the many ethical questions which are raised by thinking about cyber security and generative AI for business. We argue that cyber security needs to be an ethical consideration for businesses implementing generative AI. As such, we offer five key recommendations which companies can adopt to ensure that the security risk of using AI models is limited.

A secure and ethical AI model design.

When designing an AI model, companies should ensure that their designs take into consideration the principles such as beneficence and non-maleficence. This means considering the potential harms and security risks which could be exposed through the model. Each design should also include non-discriminatory principles to avoid biases and unexpected outcomes from the AI models. Following the principle of explicability, companies should ensure their AI training is easily explainable and transparent in its design.

A trusted and fair data collection process.

Companies need to ensure data collected is accurate, fair, representative, and legally sourced. As the principle of autonomy demonstrates, there should be considerations of how much users can have a say about how their data is used in the training of a model. Companies should consider whether they will need to have an opt-in or opt-out systems to protect the privacy of users or customers.

A secure data storage.

Companies will need to adhere to the privacy best practices for all the data stored, whether it is training data or input data from users. This should also be done while considering the risk of leaks through hacks such as training data extraction. With regulation of generative AI on the horizon, companies must now prepare by putting in place their own policies over what data is used, while considering the risk that this data could be exposed. This takes into consideration the principle of justice, in the prevention of possible data leaks.

Ethical AI model retraining and maintenance.

To maintain model currency and accuracy, AI models require retraining from time to time. Companies need to perform sufficient checks and tests after retraining the AI model and updating the generative AI applications to ensure it maintains its ethical standards and accuracy. In terms of cyber security, this also means constant monitoring for signs of influence, malware or the AI focused attacks as outlined in this paper. New defence training and policies will be needed to monitor for these threats.

Upskilling, training staff and managing staff.

One of the biggest pain points for business is upskilling and training staff. When implementing a strategy with generative AI, companies should consider what benefit the AI is bringing, while also considering the human impact this will have on staff. If staff are being asked to work with, train or implement models, they might be concerned that they will soon be replaced by these models. Upskilling and training will also be essential to mitigate the potential threats from overreliance and over-trust in new generative AI models.

9 Conclusion

We have seen that implementation of generative AI comes with considerable cyber security risk for businesses. When rushing to implement generative AI and not fall behind others in industry, companies are also increasing the risk for cyber security breaches. While there is a great momentum toward incorporating generative AI, there also needs to be a consideration of the ethical responsibility toward the protection of data and prevention against threats.

A major risk with the rush to market of generative AI is its adoption by workers without guidance or understanding of how various generative AI tools are produced, managed or of the risks they pose. This lack of understanding can leave companies open to cyber security threats. We point out two ways in which this can happen: overreliance and over-trust in generative AI systems. While these two are related, each offers distinct risks and ethical challenges.

The ethical principles of beneficence, non-maleficence, autonomy, justice and explicability are useful lenses through which business can view their obligations when planning to implement data-safe and cyber-secure generative AI solutions.

The rapid adoption of generative AI seems to be moving faster than the industry’s understanding of the technology and its inherent ethical and cyber security risks. Companies will need to manage the risk from new vulnerabilities due to generative AI, requiring new forms of governance and regulatory frameworks. Employee training, procedures and managed implementation are an ethical responsibility to protect workers, sensitive company information and the public. Companies now have the opportunity to prevent expensive and unnecessary consequences of generative AI, by addressing the ethical and cyber security threats and investing in data protection measures.

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Humphreys, D., Koay, A., Desmond, D. et al. AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business. AI Ethics (2024). https://doi.org/10.1007/s43681-024-00443-4

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3 Questions: Shaping the future of work in an age of AI

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Simon Johnson speaks from behind a lectern at MIT. Boston’s skyline is out of focus in the windows behind him.

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The MIT Shaping the Future of Work Initiative , co-directed by MIT professors Daron Acemoglu, David Autor, and Simon Johnson, celebrated its official launch on Jan. 22. The new initiative’s mission is to analyze the forces that are eroding job quality and labor market opportunities for non-college workers and identify innovative ways to move the economy onto a more equitable trajectory. Here, Acemoglu, Autor, and Johnson speak about the origins, goals, and plans for their new initiative.

Q: What was the impetus for creating the MIT Shaping the Future of Work Initiative?

David Autor: The last 40 years have been increasingly difficult for the 65 percent of U.S. workers who do not have a four-year college degree. Globalization, automation, deindustrialization, de-unionization, and changes in policy and ideology have led to fewer jobs, declining wages, and lower job quality, resulting in widening inequality and shrinking opportunities.

The prevailing economic view has been that this erosion is inevitable — that the best we can do is focus on the supply side, educating workers to meet market demands, or perhaps providing some offsetting transfers to those who have lost employment opportunities.

Underpinning this fatalism is a paradigm which says that the factors shaping demand for work, such as technological change, are immutable: workers must adapt to these forces or be left behind. This assumption is false. The direction of technology is something we choose, and the institutions that shape how these forces play out (e.g., minimum wage laws, regulations, collective bargaining, public investments, social norms) are also endogenous.

To challenge a prevailing narrative, it is not enough to simply say that it is wrong — to truly change a paradigm we must lead by showing a viable alternative pathway. We must answer what sort of work we want and how we can make policies and shape technology that builds that future.

Q: What are your goals for the initiative?

Daron Acemoglu: The initiative's ambition is not modest. Simon, David, and I are hoping to make advances in new empirical work to interpret what has happened in the recent past and understand how different types of technologies could be impacting prosperity and inequality. We want to contribute to the emergence of a coherent framework that can inform us about how institutions and social forces shape the trajectory of technology, and that helps us to identify, empirically and conceptually, the inefficiencies and the misdirections of technology. And on this basis, we are hoping to contribute to policy discussions in which policy, institutions, and norms are part of what shapes the future of technology in a more beneficial direction. Last but not least, our mission is not just to do our own research, but to help build an ecosystem in which other, especially younger, researchers are inspired to explore these issues.

Q: What are your next steps?

Simon Johnson: David, Daron, and I plan for this initiative to move beyond producing insightful and groundbreaking research — our aim is to identify innovative pro-worker ideas that policymakers, the private sector, and civil society can use. We will continue to translate research into practice by regularly convening students, scholars, policymakers, and practitioners who are shaping the future of work — to include fortifying and diversifying the pipeline of emerging scholars who produce policy-relevant research around our core themes.

We will also produce a range of resources to bring our work to wider audiences. Last fall, David, Daron, and I wrote the initiative’s inaugural policy memo, entitled “ Can we Have Pro-Worker AI ? Choosing a path of machines in service of minds.” Our thesis is that, instead of focusing on replacing workers by automating job tasks as quickly as possible, the best path forward is to focus on developing worker-augmenting AI tools that enable less-educated or less-skilled workers to perform more expert tasks — as well as creating work, in the form of new productive tasks, for workers across skill and education levels.

As we move forward, we will also look for opportunities to engage globally with a wide range of scholars working on related issues.

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