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How To Write A Research Summary

It’s a common perception that writing a research summary is a quick and easy task. After all, how hard can jotting down 300 words be? But when you consider the weight those 300 words carry, writing a research summary as a part of your dissertation, essay or compelling draft for your paper instantly becomes daunting task.

A research summary requires you to synthesize a complex research paper into an informative, self-explanatory snapshot. It needs to portray what your article contains. Thus, writing it often comes at the end of the task list.

Regardless of when you’re planning to write, it is no less of a challenge, particularly if you’re doing it for the first time. This blog will take you through everything you need to know about research summary so that you have an easier time with it.

What is a Research Summary?

A research summary is the part of your research paper that describes its findings to the audience in a brief yet concise manner. A well-curated research summary represents you and your knowledge about the information written in the research paper.

While writing a quality research summary, you need to discover and identify the significant points in the research and condense it in a more straightforward form. A research summary is like a doorway that provides access to the structure of a research paper's sections.

Since the purpose of a summary is to give an overview of the topic, methodology, and conclusions employed in a paper, it requires an objective approach. No analysis or criticism.

Research summary or Abstract. What’s the Difference?

They’re both brief, concise, and give an overview of an aspect of the research paper. So, it’s easy to understand why many new researchers get the two confused. However, a research summary and abstract are two very different things with individual purpose. To start with, a research summary is written at the end while the abstract comes at the beginning of a research paper.

A research summary captures the essence of the paper at the end of your document. It focuses on your topic, methods, and findings. More like a TL;DR, if you will. An abstract, on the other hand, is a description of what your research paper is about. It tells your reader what your topic or hypothesis is, and sets a context around why you have embarked on your research.

Getting Started with a Research Summary

Before you start writing, you need to get insights into your research’s content, style, and organization. There are three fundamental areas of a research summary that you should focus on.

• While deciding the contents of your research summary, you must include a section on its importance as a whole, the techniques, and the tools that were used to formulate the conclusion. Additionally, there needs to be a short but thorough explanation of how the findings of the research paper have a significance.
• To keep the summary well-organized, try to cover the various sections of the research paper in separate paragraphs. Besides, how the idea of particular factual research came up first must be explained in a separate paragraph.
• As a general practice worldwide, research summaries are restricted to 300-400 words. However, if you have chosen a lengthy research paper, try not to exceed the word limit of 10% of the entire research paper.

How to Structure Your Research Summary

The research summary is nothing but a concise form of the entire research paper. Therefore, the structure of a summary stays the same as the paper. So, include all the section titles and write a little about them. The structural elements that a research summary must consist of are:

It represents the topic of the research. Try to phrase it so that it includes the key findings or conclusion of the task.

The abstract gives a context of the research paper. Unlike the abstract at the beginning of a paper, the abstract here, should be very short since you’ll be working with a limited word count.

Introduction

This is the most crucial section of a research summary as it helps readers get familiarized with the topic. You should include the definition of your topic, the current state of the investigation, and practical relevance in this part. Additionally, you should present the problem statement, investigative measures, and any hypothesis in this section.

Methodology

This section provides details about the methodology and the methods adopted to conduct the study. You should write a brief description of the surveys, sampling, type of experiments, statistical analysis, and the rationality behind choosing those particular methods.

Create a list of evidence obtained from the various experiments with a primary analysis, conclusions, and interpretations made upon that. In the paper research paper, you will find the results section as the most detailed and lengthy part. Therefore, you must pick up the key elements and wisely decide which elements are worth including and which are worth skipping.

This is where you present the interpretation of results in the context of their application. Discussion usually covers results, inferences, and theoretical models explaining the obtained values, key strengths, and limitations. All of these are vital elements that you must include in the summary.

Most research papers merge conclusion with discussions. However, depending upon the instructions, you may have to prepare this as a separate section in your research summary. Usually, conclusion revisits the hypothesis and provides the details about the validation or denial about the arguments made in the research paper, based upon how convincing the results were obtained.

The structure of a research summary closely resembles the anatomy of a scholarly article . Additionally, you should keep your research and references limited to authentic and  scholarly sources only.

Tips for Writing a Research Summary

The core concept behind undertaking a research summary is to present a simple and clear understanding of your research paper to the reader. The biggest hurdle while doing that is the number of words you have at your disposal. So, follow the steps below to write a research summary that sticks.

1. Read the parent paper thoroughly

You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

a. Scan: In the first read, go through it to get an understanding of its basic concept and methodologies.

b. Read: For the second step, read the article attentively by going through each section, highlighting the key elements, and subsequently listing the topics that you will include in your research summary.

c. Skim: Flip through the article a few more times to study the interpretation of various experimental results, statistical analysis, and application in different contexts.

Sincerely go through different headings and subheadings as it will allow you to understand the underlying concept of each section. You can try reading the introduction and conclusion simultaneously to understand the motive of the task and how obtained results stay fit to the expected outcome.

2. Identify the key elements in different sections

While exploring different sections of an article, you can try finding answers to simple what, why, and how. Below are a few pointers to give you an idea:

• What is the research question and how is it addressed?
• Is there a hypothesis in the introductory part?
• What type of methods are being adopted?
• What is the sample size for data collection and how is it being analyzed?
• What are the most vital findings?
• Do the results support the hypothesis?

Discussion/Conclusion

• What is the final solution to the problem statement?
• What is the explanation for the obtained results?
• What is the drawn inference?
• What are the various limitations of the study?

3. Prepare the first draft

Now that you’ve listed the key points that the paper tries to demonstrate, you can start writing the summary following the standard structure of a research summary. Just make sure you’re not writing statements from the parent research paper verbatim.

Instead, try writing down each section in your own words. This will not only help in avoiding plagiarism but will also show your complete understanding of the subject. Alternatively, you can use a summarizing tool (AI-based summary generators) to shorten the content or summarize the content without disrupting the actual meaning of the article.

SciSpace Copilot is one such helpful feature! You can easily upload your research paper and ask Copilot to summarize it. You will get an AI-generated, condensed research summary. SciSpace Copilot also enables you to highlight text, clip math and tables, and ask any question relevant to the research paper; it will give you instant answers with deeper context of the article..

4. Include visuals

One of the best ways to summarize and consolidate a research paper is to provide visuals like graphs, charts, pie diagrams, etc.. Visuals make getting across the facts, the past trends, and the probabilistic figures around a concept much more engaging.

5. Double check for plagiarism

It can be very tempting to copy-paste a few statements or the entire paragraphs depending upon the clarity of those sections. But it’s best to stay away from the practice. Even paraphrasing should be done with utmost care and attention.

Also: QuillBot vs SciSpace: Choose the best AI-paraphrasing tool

6. Religiously follow the word count limit

You need to have strict control while writing different sections of a research summary. In many cases, it has been observed that the research summary and the parent research paper become the same length. If that happens, it can lead to discrediting of your efforts and research summary itself. Whatever the standard word limit has been imposed, you must observe that carefully.

7. Proofread your research summary multiple times

The process of writing the research summary can be exhausting and tiring. However, you shouldn’t allow this to become a reason to skip checking your academic writing several times for mistakes like misspellings, grammar, wordiness, and formatting issues. Proofread and edit until you think your research summary can stand out from the others, provided it is drafted perfectly on both technicality and comprehension parameters. You can also seek assistance from editing and proofreading services , and other free tools that help you keep these annoying grammatical errors at bay.

8. Watch while you write

Keep a keen observation of your writing style. You should use the words very precisely, and in any situation, it should not represent your personal opinions on the topic. You should write the entire research summary in utmost impersonal, precise, factually correct, and evidence-based writing.

9. Ask a friend/colleague to help

Once you are done with the final copy of your research summary, you must ask a friend or colleague to read it. You must test whether your friend or colleague could grasp everything without referring to the parent paper. This will help you in ensuring the clarity of the article.

Once you become familiar with the research paper summary concept and understand how to apply the tips discussed above in your current task, summarizing a research summary won’t be that challenging. While traversing the different stages of your academic career, you will face different scenarios where you may have to create several research summaries.

In such cases, you just need to look for answers to simple questions like “Why this study is necessary,” “what were the methods,” “who were the participants,” “what conclusions were drawn from the research,” and “how it is relevant to the wider world.” Once you find out the answers to these questions, you can easily create a good research summary following the standard structure and a precise writing style.

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Home » Research Findings – Types Examples and Writing Guide

Research Findings – Types Examples and Writing Guide

Table of Contents

Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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From Data to Discovery: The Findings Section of a Research Paper

Discover the role of the findings section of a research paper here. Explore strategies and techniques to maximize your understanding.

Are you curious about the Findings section of a research paper? Did you know that this is a part where all the juicy results and discoveries are laid out for the world to see? Undoubtedly, the findings section of a research paper plays a critical role in presenting and interpreting the collected data. It serves as a comprehensive account of the study’s results and their implications.

Well, look no further because we’ve got you covered! In this article, we’re diving into the ins and outs of presenting and interpreting data in the findings section. We’ll be sharing tips and tricks on how to effectively present your findings, whether it’s through tables, graphs, or good old descriptive statistics.

Overview of the Findings Section of a Research Paper

The findings section of a research paper presents the results and outcomes of the study or investigation. It is a crucial part of the research paper where researchers interpret and analyze the data collected and draw conclusions based on their findings. This section aims to answer the research questions or hypotheses formulated earlier in the paper and provide evidence to support or refute them.

In the findings section, researchers typically present the data clearly and organized. They may use tables, graphs, charts, or other visual aids to illustrate the patterns, trends, or relationships observed in the data. The findings should be presented objectively, without any bias or personal opinions, and should be accompanied by appropriate statistical analyses or methods to ensure the validity and reliability of the results.

Organizing the Findings Section

The findings section of the research paper organizes and presents the results obtained from the study in a clear and logical manner. Here is a suggested structure for organizing the Findings section:

Introduction to the Findings

Start the section by providing a brief overview of the research objectives and the methodology employed. Recapitulate the research questions or hypotheses addressed in the study.

To learn more about methodology, read this article .

Descriptive Statistics and Data Presentation

Present the collected data using appropriate descriptive statistics. This may involve using tables, graphs, charts, or other visual representations to convey the information effectively. Remember: we can easily help you with that.

Data Analysis and Interpretation

Perform a thorough analysis of the data collected and describe the key findings. Present the results of statistical analyses or any other relevant methods used to analyze the data. 

Discussion of Findings

Analyze and interpret the findings in the context of existing literature or theoretical frameworks . Discuss any patterns, trends, or relationships observed in the data. Compare and contrast the results with prior studies, highlighting similarities and differences. 

Limitations and Constraints

Acknowledge and discuss any limitations or constraints that may have influenced the findings. This could include issues such as sample size, data collection methods, or potential biases. 

Summarize the main findings of the study and emphasize their significance. Revisit the research questions or hypotheses and discuss whether they have been supported or refuted by the findings.

Presenting Data in the Findings Section

There are several ways to present data in the findings section of a research paper. Here are some common methods:

• Tables : Tables are commonly used to present organized and structured data. They are particularly useful when presenting numerical data with multiple variables or categories. Tables allow readers to easily compare and interpret the information presented. Learn how to cite tables in research papers here .
• Graphs and Charts: Graphs and charts are effective visual tools for presenting data, especially when illustrating trends, patterns, or relationships. Common types include bar graphs, line graphs, scatter plots, pie charts, and histograms. Graphs and charts provide a visual representation of the data, making it easier for readers to comprehend and interpret.
• Figures and Images: Figures and images can be used to present data that requires visual representation, such as maps, diagrams, or experimental setups. They can enhance the understanding of complex data or provide visual evidence to support the research findings.
• Descriptive Statistics: Descriptive statistics provide summary measures of central tendency (e.g., mean, median, mode) and dispersion (e.g., standard deviation, range) for numerical data. These statistics can be included in the text or presented in tables or graphs to provide a concise summary of the data distribution.

How to Effectively Interpret Results

Interpreting the results is a crucial aspect of the findings section in a research paper. It involves analyzing the data collected and drawing meaningful conclusions based on the findings. Following are the guidelines on how to effectively interpret the results.

Step 1 – Begin with a Recap

Start by restating the research questions or hypotheses to provide context for the interpretation. Remind readers of the specific objectives of the study to help them understand the relevance of the findings.

Step 2 – Relate Findings to Research Questions

Clearly articulate how the results address the research questions or hypotheses. Discuss each finding in relation to the original objectives and explain how it contributes to answering the research questions or supporting/refuting the hypotheses.

Step 3 – Compare with Existing Literature

Compare and contrast the findings with previous studies or existing literature. Highlight similarities, differences, or discrepancies between your results and those of other researchers. Discuss any consistencies or contradictions and provide possible explanations for the observed variations.

Step 4 – Consider Limitations and Alternative Explanations

Acknowledge the limitations of the study and discuss how they may have influenced the results. Explore alternative explanations or factors that could potentially account for the findings. Evaluate the robustness of the results in light of the limitations and alternative interpretations.

Step 5 – Discuss Implications and Significance

Highlight any potential applications or areas where further research is needed based on the outcomes of the study.

Step 6 – Address Inconsistencies and Contradictions

If there are any inconsistencies or contradictions in the findings, address them directly. Discuss possible reasons for the discrepancies and consider their implications for the overall interpretation. Be transparent about any uncertainties or unresolved issues.

Step 7 – Be Objective and Data-Driven

Present the interpretation objectively, based on the evidence and data collected. Avoid personal biases or subjective opinions. Use logical reasoning and sound arguments to support your interpretations.

Reporting Statistical Significance

When reporting statistical significance in the findings section of a research paper, it is important to accurately convey the results of statistical analyses and their implications. Here are some guidelines on how to report statistical significance effectively:

• Clearly State the Statistical Test: Begin by clearly stating the specific statistical test or analysis used to determine statistical significance. For example, you might mention that a t-test, chi-square test, ANOVA, correlation analysis, or regression analysis was employed.
• Report the Test Statistic: Provide the value of the test statistic obtained from the analysis. This could be the t-value, F-value, chi-square value, correlation coefficient, or any other relevant statistic depending on the test used.
• State the Degrees of Freedom: Indicate the degrees of freedom associated with the statistical test. Degrees of freedom represent the number of independent pieces of information available for estimating a statistic. For example, in a t-test, degrees of freedom would be mentioned as (df = n1 + n2 – 2) for an independent samples test or (df = N – 2) for a paired samples test.
• Report the p-value: The p-value indicates the probability of obtaining results as extreme or more extreme than the observed results, assuming the null hypothesis is true. Report the p-value associated with the statistical test. For example, p < 0.05 denotes statistical significance at the conventional level of α = 0.05.
• Provide the Conclusion: Based on the p-value obtained, state whether the results are statistically significant or not. If the p-value is less than the predetermined threshold (e.g., p < 0.05), state that the results are statistically significant. If the p-value is greater than the threshold, state that the results are not statistically significant.
• Discuss the Interpretation: After reporting statistical significance, discuss the practical or theoretical implications of the finding. Explain what the significant result means in the context of your research questions or hypotheses. Address the effect size and practical significance of the findings, if applicable.
• Consider Effect Size Measures: Along with statistical significance, it is often important to report effect size measures. Effect size quantifies the magnitude of the relationship or difference observed in the data. Common effect size measures include Cohen’s d, eta-squared, or Pearson’s r. Reporting effect size provides additional meaningful information about the strength of the observed effects.
• Be Accurate and Transparent: Ensure that the reported statistical significance and associated values are accurate. Avoid misinterpreting or misrepresenting the results. Be transparent about the statistical tests conducted, any assumptions made, and potential limitations or caveats that may impact the interpretation of the significant results.

Conclusion of the Findings Section

The conclusion of the findings section in a research paper serves as a summary and synthesis of the key findings and their implications. It is an opportunity to tie together the results, discuss their significance, and address the research objectives. Here are some guidelines on how to write the conclusion of the Findings section:

Summarize the Key Findings

Begin by summarizing the main findings of the study. Provide a concise overview of the significant results, patterns, or relationships that emerged from the data analysis. Highlight the most important findings that directly address the research questions or hypotheses.

Revisit the Research Objectives

Remind the reader of the research objectives stated at the beginning of the paper. Discuss how the findings contribute to achieving those objectives and whether they support or challenge the initial research questions or hypotheses.

Suggest Future Directions

Identify areas for further research or future directions based on the findings. Discuss any unanswered questions, unresolved issues, or new avenues of inquiry that emerged during the study. Propose potential research opportunities that can build upon the current findings.

The Best Scientific Figures to Represent Your Findings 

Have you heard of any tool that helps you represent your findings through visuals like graphs, pie charts, and infographics? Well, if you haven’t, then here’s the tool you need to explore – Mind the Graph . It’s the tool that has the best scientific figures to represent your findings. Go, try it now, and make your research findings stand out!

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About Sowjanya Pedada

Sowjanya is a passionate writer and an avid reader. She holds MBA in Agribusiness Management and now is working as a content writer. She loves to play with words and hopes to make a difference in the world through her writings. Apart from writing, she is interested in reading fiction novels and doing craftwork. She also loves to travel and explore different cuisines and spend time with her family and friends.

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

22 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

For qualitative studies, can the findings be structured according to the Research questions? Thank you.

Do I need to include literature/references in my findings chapter?

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Research Summary: What is it & how to write one

The Research Summary is used to report facts about a study clearly. You will almost certainly be required to prepare a research summary during your academic research or while on a research project for your organization.

If it is the first time you have to write one, the writing requirements may confuse you. The instructors generally assign someone to write a summary of the research work. Research summaries require the writer to have a thorough understanding of the issue.

This article will discuss the definition of a research summary and how to write one.

What is a research summary?

A research summary is a piece of writing that summarizes your research on a specific topic. Its primary goal is to offer the reader a detailed overview of the study with the key findings. A research summary generally contains the article’s structure in which it is written.

You must know the goal of your analysis before you launch a project. A research overview summarizes the detailed response and highlights particular issues raised in it. Writing it might be somewhat troublesome. To write a good overview, you want to start with a structure in mind. Read on for our guide.

Why is an analysis recap so important?

Your summary or analysis is going to tell readers everything about your research project. This is the critical piece that your stakeholders will read to identify your findings and valuable insights. Having a good and concise research summary that presents facts and comes with no research biases is the critical deliverable of any research project.

We’ve put together a cheat sheet to help you write a good research summary below.

Research Summary Guide

• Why was this research done?  – You want to give a clear description of why this research study was done. What hypothesis was being tested?
• Who was surveyed? – The what and why or your research decides who you’re going to interview/survey. Your research summary has a detailed note on who participated in the study and why they were selected. 
• What was the methodology? – Talk about the methodology. Did you do face-to-face interviews? Was it a short or long survey or a focus group setting? Your research methodology is key to the results you’re going to get. 
• What were the key findings? – This can be the most critical part of the process. What did we find out after testing the hypothesis? This section, like all others, should be just facts, facts facts. You’re not sharing how you feel about the findings. Keep it bias-free.
• Conclusion – What are the conclusions that were drawn from the findings. A good example of a conclusion. Surprisingly, most people interviewed did not watch the lunar eclipse in 2022, which is unexpected given that 100% of those interviewed knew about it before it happened.
• Takeaways and action points – This is where you bring in your suggestion. Given the data you now have from the research, what are the takeaways and action points? If you’re a researcher running this research project for your company, you’ll use this part to shed light on your recommended action plans for the business.

LEARN ABOUT:   Action Research

If you’re doing any research, you will write a summary, which will be the most viewed and more important part of the project. So keep a guideline in mind before you start. Focus on the content first and then worry about the length. Use the cheat sheet/checklist in this article to organize your summary, and that’s all you need to write a great research summary!

But once your summary is ready, where is it stored? Most teams have multiple documents in their google drives, and it’s a nightmare to find projects that were done in the past. Your research data should be democratized and easy to use.

We at QuestionPro launched a research repository for research teams, and our clients love it. All your data is in one place, and everything is searchable, including your research summaries! 

Authors: Prachi, Anas

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How to Write the Results/Findings Section in Research

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:

“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?”

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.

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.

Wordvice Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

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Cochrane Training

Chapter 15: interpreting results and drawing conclusions.

Holger J Schünemann, Gunn E Vist, Julian PT Higgins, Nancy Santesso, Jonathan J Deeks, Paul Glasziou, Elie A Akl, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group

Key Points:

• This chapter provides guidance on interpreting the results of synthesis in order to communicate the conclusions of the review effectively.
• Methods are presented for computing, presenting and interpreting relative and absolute effects for dichotomous outcome data, including the number needed to treat (NNT).
• For continuous outcome measures, review authors can present summary results for studies using natural units of measurement or as minimal important differences when all studies use the same scale. When studies measure the same construct but with different scales, review authors will need to find a way to interpret the standardized mean difference, or to use an alternative effect measure for the meta-analysis such as the ratio of means.
• Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values, but report the confidence interval together with the exact P value.
• Review authors should not make recommendations about healthcare decisions, but they can – after describing the certainty of evidence and the balance of benefits and harms – highlight different actions that might be consistent with particular patterns of values and preferences and other factors that determine a decision such as cost.

Cite this chapter as: Schünemann HJ, Vist GE, Higgins JPT, Santesso N, Deeks JJ, Glasziou P, Akl EA, Guyatt GH. Chapter 15: Interpreting results and drawing conclusions [last updated August 2023]. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.5. Cochrane, 2024. Available from www.training.cochrane.org/handbook .

15.1 Introduction

The purpose of Cochrane Reviews is to facilitate healthcare decisions by patients and the general public, clinicians, guideline developers, administrators and policy makers. They also inform future research. A clear statement of findings, a considered discussion and a clear presentation of the authors’ conclusions are, therefore, important parts of the review. In particular, the following issues can help people make better informed decisions and increase the usability of Cochrane Reviews:

• information on all important outcomes, including adverse outcomes;
• the certainty of the evidence for each of these outcomes, as it applies to specific populations and specific interventions; and
• clarification of the manner in which particular values and preferences may bear on the desirable and undesirable consequences of the intervention.

A ‘Summary of findings’ table, described in Chapter 14 , Section 14.1 , provides key pieces of information about health benefits and harms in a quick and accessible format. It is highly desirable that review authors include a ‘Summary of findings’ table in Cochrane Reviews alongside a sufficient description of the studies and meta-analyses to support its contents. This description includes the rating of the certainty of evidence, also called the quality of the evidence or confidence in the estimates of the effects, which is expected in all Cochrane Reviews.

‘Summary of findings’ tables are usually supported by full evidence profiles which include the detailed ratings of the evidence (Guyatt et al 2011a, Guyatt et al 2013a, Guyatt et al 2013b, Santesso et al 2016). The Discussion section of the text of the review provides space to reflect and consider the implications of these aspects of the review’s findings. Cochrane Reviews include five standard subheadings to ensure the Discussion section places the review in an appropriate context: ‘Summary of main results (benefits and harms)’; ‘Potential biases in the review process’; ‘Overall completeness and applicability of evidence’; ‘Certainty of the evidence’; and ‘Agreements and disagreements with other studies or reviews’. Following the Discussion, the Authors’ conclusions section is divided into two standard subsections: ‘Implications for practice’ and ‘Implications for research’. The assessment of the certainty of evidence facilitates a structured description of the implications for practice and research.

Because Cochrane Reviews have an international audience, the Discussion and Authors’ conclusions should, so far as possible, assume a broad international perspective and provide guidance for how the results could be applied in different settings, rather than being restricted to specific national or local circumstances. Cultural differences and economic differences may both play an important role in determining the best course of action based on the results of a Cochrane Review. Furthermore, individuals within societies have widely varying values and preferences regarding health states, and use of societal resources to achieve particular health states. For all these reasons, and because information that goes beyond that included in a Cochrane Review is required to make fully informed decisions, different people will often make different decisions based on the same evidence presented in a review.

Thus, review authors should avoid specific recommendations that inevitably depend on assumptions about available resources, values and preferences, and other factors such as equity considerations, feasibility and acceptability of an intervention. The purpose of the review should be to present information and aid interpretation rather than to offer recommendations. The discussion and conclusions should help people understand the implications of the evidence in relation to practical decisions and apply the results to their specific situation. Review authors can aid this understanding of the implications by laying out different scenarios that describe certain value structures.

In this chapter, we address first one of the key aspects of interpreting findings that is also fundamental in completing a ‘Summary of findings’ table: the certainty of evidence related to each of the outcomes. We then provide a more detailed consideration of issues around applicability and around interpretation of numerical results, and provide suggestions for presenting authors’ conclusions.

15.2 Issues of indirectness and applicability

15.2.1 the role of the review author.

“A leap of faith is always required when applying any study findings to the population at large” or to a specific person. “In making that jump, one must always strike a balance between making justifiable broad generalizations and being too conservative in one’s conclusions” (Friedman et al 1985). In addition to issues about risk of bias and other domains determining the certainty of evidence, this leap of faith is related to how well the identified body of evidence matches the posed PICO ( Population, Intervention, Comparator(s) and Outcome ) question. As to the population, no individual can be entirely matched to the population included in research studies. At the time of decision, there will always be differences between the study population and the person or population to whom the evidence is applied; sometimes these differences are slight, sometimes large.

The terms applicability, generalizability, external validity and transferability are related, sometimes used interchangeably and have in common that they lack a clear and consistent definition in the classic epidemiological literature (Schünemann et al 2013). However, all of the terms describe one overarching theme: whether or not available research evidence can be directly used to answer the health and healthcare question at hand, ideally supported by a judgement about the degree of confidence in this use (Schünemann et al 2013). GRADE’s certainty domains include a judgement about ‘indirectness’ to describe all of these aspects including the concept of direct versus indirect comparisons of different interventions (Atkins et al 2004, Guyatt et al 2008, Guyatt et al 2011b).

To address adequately the extent to which a review is relevant for the purpose to which it is being put, there are certain things the review author must do, and certain things the user of the review must do to assess the degree of indirectness. Cochrane and the GRADE Working Group suggest using a very structured framework to address indirectness. We discuss here and in Chapter 14 what the review author can do to help the user. Cochrane Review authors must be extremely clear on the population, intervention and outcomes that they intend to address. Chapter 14, Section 14.1.2 , also emphasizes a crucial step: the specification of all patient-important outcomes relevant to the intervention strategies under comparison.

In considering whether the effect of an intervention applies equally to all participants, and whether different variations on the intervention have similar effects, review authors need to make a priori hypotheses about possible effect modifiers, and then examine those hypotheses (see Chapter 10, Section 10.10 and Section 10.11 ). If they find apparent subgroup effects, they must ultimately decide whether or not these effects are credible (Sun et al 2012). Differences between subgroups, particularly those that correspond to differences between studies, should be interpreted cautiously. Some chance variation between subgroups is inevitable so, unless there is good reason to believe that there is an interaction, review authors should not assume that the subgroup effect exists. If, despite due caution, review authors judge subgroup effects in terms of relative effect estimates as credible (i.e. the effects differ credibly), they should conduct separate meta-analyses for the relevant subgroups, and produce separate ‘Summary of findings’ tables for those subgroups.

The user of the review will be challenged with ‘individualization’ of the findings, whether they seek to apply the findings to an individual patient or a policy decision in a specific context. For example, even if relative effects are similar across subgroups, absolute effects will differ according to baseline risk. Review authors can help provide this information by identifying identifiable groups of people with varying baseline risks in the ‘Summary of findings’ tables, as discussed in Chapter 14, Section 14.1.3 . Users can then identify their specific case or population as belonging to a particular risk group, if relevant, and assess their likely magnitude of benefit or harm accordingly. A description of the identifying prognostic or baseline risk factors in a brief scenario (e.g. age or gender) will help users of a review further.

Another decision users must make is whether their individual case or population of interest is so different from those included in the studies that they cannot use the results of the systematic review and meta-analysis at all. Rather than rigidly applying the inclusion and exclusion criteria of studies, it is better to ask whether or not there are compelling reasons why the evidence should not be applied to a particular patient. Review authors can sometimes help decision makers by identifying important variation where divergence might limit the applicability of results (Rothwell 2005, Schünemann et al 2006, Guyatt et al 2011b, Schünemann et al 2013), including biologic and cultural variation, and variation in adherence to an intervention.

In addressing these issues, review authors cannot be aware of, or address, the myriad of differences in circumstances around the world. They can, however, address differences of known importance to many people and, importantly, they should avoid assuming that other people’s circumstances are the same as their own in discussing the results and drawing conclusions.

15.2.2 Biological variation

Issues of biological variation that may affect the applicability of a result to a reader or population include divergence in pathophysiology (e.g. biological differences between women and men that may affect responsiveness to an intervention) and divergence in a causative agent (e.g. for infectious diseases such as malaria, which may be caused by several different parasites). The discussion of the results in the review should make clear whether the included studies addressed all or only some of these groups, and whether any important subgroup effects were found.

15.2.3 Variation in context

Some interventions, particularly non-pharmacological interventions, may work in some contexts but not in others; the situation has been described as program by context interaction (Hawe et al 2004). Contextual factors might pertain to the host organization in which an intervention is offered, such as the expertise, experience and morale of the staff expected to carry out the intervention, the competing priorities for the clinician’s or staff’s attention, the local resources such as service and facilities made available to the program and the status or importance given to the program by the host organization. Broader context issues might include aspects of the system within which the host organization operates, such as the fee or payment structure for healthcare providers and the local insurance system. Some interventions, in particular complex interventions (see Chapter 17 ), can be only partially implemented in some contexts, and this requires judgements about indirectness of the intervention and its components for readers in that context (Schünemann 2013).

Contextual factors may also pertain to the characteristics of the target group or population, such as cultural and linguistic diversity, socio-economic position, rural/urban setting. These factors may mean that a particular style of care or relationship evolves between service providers and consumers that may or may not match the values and technology of the program.

For many years these aspects have been acknowledged when decision makers have argued that results of evidence reviews from other countries do not apply in their own country or setting. Whilst some programmes/interventions have been successfully transferred from one context to another, others have not (Resnicow et al 1993, Lumley et al 2004, Coleman et al 2015). Review authors should be cautious when making generalizations from one context to another. They should report on the presence (or otherwise) of context-related information in intervention studies, where this information is available.

15.2.4 Variation in adherence

Variation in the adherence of the recipients and providers of care can limit the certainty in the applicability of results. Predictable differences in adherence can be due to divergence in how recipients of care perceive the intervention (e.g. the importance of side effects), economic conditions or attitudes that make some forms of care inaccessible in some settings, such as in low-income countries (Dans et al 2007). It should not be assumed that high levels of adherence in closely monitored randomized trials will translate into similar levels of adherence in normal practice.

15.2.5 Variation in values and preferences

Decisions about healthcare management strategies and options involve trading off health benefits and harms. The right choice may differ for people with different values and preferences (i.e. the importance people place on the outcomes and interventions), and it is important that decision makers ensure that decisions are consistent with a patient or population’s values and preferences. The importance placed on outcomes, together with other factors, will influence whether the recipients of care will or will not accept an option that is offered (Alonso-Coello et al 2016) and, thus, can be one factor influencing adherence. In Section 15.6 , we describe how the review author can help this process and the limits of supporting decision making based on intervention reviews.

15.3 Interpreting results of statistical analyses

15.3.1 confidence intervals.

Results for both individual studies and meta-analyses are reported with a point estimate together with an associated confidence interval. For example, ‘The odds ratio was 0.75 with a 95% confidence interval of 0.70 to 0.80’. The point estimate (0.75) is the best estimate of the magnitude and direction of the experimental intervention’s effect compared with the comparator intervention. The confidence interval describes the uncertainty inherent in any estimate, and describes a range of values within which we can be reasonably sure that the true effect actually lies. If the confidence interval is relatively narrow (e.g. 0.70 to 0.80), the effect size is known precisely. If the interval is wider (e.g. 0.60 to 0.93) the uncertainty is greater, although there may still be enough precision to make decisions about the utility of the intervention. Intervals that are very wide (e.g. 0.50 to 1.10) indicate that we have little knowledge about the effect and this imprecision affects our certainty in the evidence, and that further information would be needed before we could draw a more certain conclusion.

A 95% confidence interval is often interpreted as indicating a range within which we can be 95% certain that the true effect lies. This statement is a loose interpretation, but is useful as a rough guide. The strictly correct interpretation of a confidence interval is based on the hypothetical notion of considering the results that would be obtained if the study were repeated many times. If a study were repeated infinitely often, and on each occasion a 95% confidence interval calculated, then 95% of these intervals would contain the true effect (see Section 15.3.3 for further explanation).

The width of the confidence interval for an individual study depends to a large extent on the sample size. Larger studies tend to give more precise estimates of effects (and hence have narrower confidence intervals) than smaller studies. For continuous outcomes, precision depends also on the variability in the outcome measurements (i.e. how widely individual results vary between people in the study, measured as the standard deviation); for dichotomous outcomes it depends on the risk of the event (more frequent events allow more precision, and narrower confidence intervals), and for time-to-event outcomes it also depends on the number of events observed. All these quantities are used in computation of the standard errors of effect estimates from which the confidence interval is derived.

The width of a confidence interval for a meta-analysis depends on the precision of the individual study estimates and on the number of studies combined. In addition, for random-effects models, precision will decrease with increasing heterogeneity and confidence intervals will widen correspondingly (see Chapter 10, Section 10.10.4 ). As more studies are added to a meta-analysis the width of the confidence interval usually decreases. However, if the additional studies increase the heterogeneity in the meta-analysis and a random-effects model is used, it is possible that the confidence interval width will increase.

Confidence intervals and point estimates have different interpretations in fixed-effect and random-effects models. While the fixed-effect estimate and its confidence interval address the question ‘what is the best (single) estimate of the effect?’, the random-effects estimate assumes there to be a distribution of effects, and the estimate and its confidence interval address the question ‘what is the best estimate of the average effect?’ A confidence interval may be reported for any level of confidence (although they are most commonly reported for 95%, and sometimes 90% or 99%). For example, the odds ratio of 0.80 could be reported with an 80% confidence interval of 0.73 to 0.88; a 90% interval of 0.72 to 0.89; and a 95% interval of 0.70 to 0.92. As the confidence level increases, the confidence interval widens.

There is logical correspondence between the confidence interval and the P value (see Section 15.3.3 ). The 95% confidence interval for an effect will exclude the null value (such as an odds ratio of 1.0 or a risk difference of 0) if and only if the test of significance yields a P value of less than 0.05. If the P value is exactly 0.05, then either the upper or lower limit of the 95% confidence interval will be at the null value. Similarly, the 99% confidence interval will exclude the null if and only if the test of significance yields a P value of less than 0.01.

Together, the point estimate and confidence interval provide information to assess the effects of the intervention on the outcome. For example, suppose that we are evaluating an intervention that reduces the risk of an event and we decide that it would be useful only if it reduced the risk of an event from 30% by at least 5 percentage points to 25% (these values will depend on the specific clinical scenario and outcomes, including the anticipated harms). If the meta-analysis yielded an effect estimate of a reduction of 10 percentage points with a tight 95% confidence interval, say, from 7% to 13%, we would be able to conclude that the intervention was useful since both the point estimate and the entire range of the interval exceed our criterion of a reduction of 5% for net health benefit. However, if the meta-analysis reported the same risk reduction of 10% but with a wider interval, say, from 2% to 18%, although we would still conclude that our best estimate of the intervention effect is that it provides net benefit, we could not be so confident as we still entertain the possibility that the effect could be between 2% and 5%. If the confidence interval was wider still, and included the null value of a difference of 0%, we would still consider the possibility that the intervention has no effect on the outcome whatsoever, and would need to be even more sceptical in our conclusions.

Review authors may use the same general approach to conclude that an intervention is not useful. Continuing with the above example where the criterion for an important difference that should be achieved to provide more benefit than harm is a 5% risk difference, an effect estimate of 2% with a 95% confidence interval of 1% to 4% suggests that the intervention does not provide net health benefit.

15.3.2 P values and statistical significance

A P value is the standard result of a statistical test, and is the probability of obtaining the observed effect (or larger) under a ‘null hypothesis’. In the context of Cochrane Reviews there are two commonly used statistical tests. The first is a test of overall effect (a Z-test), and its null hypothesis is that there is no overall effect of the experimental intervention compared with the comparator on the outcome of interest. The second is the (Chi 2 ) test for heterogeneity, and its null hypothesis is that there are no differences in the intervention effects across studies.

A P value that is very small indicates that the observed effect is very unlikely to have arisen purely by chance, and therefore provides evidence against the null hypothesis. It has been common practice to interpret a P value by examining whether it is smaller than particular threshold values. In particular, P values less than 0.05 are often reported as ‘statistically significant’, and interpreted as being small enough to justify rejection of the null hypothesis. However, the 0.05 threshold is an arbitrary one that became commonly used in medical and psychological research largely because P values were determined by comparing the test statistic against tabulations of specific percentage points of statistical distributions. If review authors decide to present a P value with the results of a meta-analysis, they should report a precise P value (as calculated by most statistical software), together with the 95% confidence interval. Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values , but report the confidence interval together with the exact P value (see MECIR Box 15.3.a ).

We discuss interpretation of the test for heterogeneity in Chapter 10, Section 10.10.2 ; the remainder of this section refers mainly to tests for an overall effect. For tests of an overall effect, the computation of P involves both the effect estimate and precision of the effect estimate (driven largely by sample size). As precision increases, the range of plausible effects that could occur by chance is reduced. Correspondingly, the statistical significance of an effect of a particular magnitude will usually be greater (the P value will be smaller) in a larger study than in a smaller study.

P values are commonly misinterpreted in two ways. First, a moderate or large P value (e.g. greater than 0.05) may be misinterpreted as evidence that the intervention has no effect on the outcome. There is an important difference between this statement and the correct interpretation that there is a high probability that the observed effect on the outcome is due to chance alone. To avoid such a misinterpretation, review authors should always examine the effect estimate and its 95% confidence interval.

The second misinterpretation is to assume that a result with a small P value for the summary effect estimate implies that an experimental intervention has an important benefit. Such a misinterpretation is more likely to occur in large studies and meta-analyses that accumulate data over dozens of studies and thousands of participants. The P value addresses the question of whether the experimental intervention effect is precisely nil; it does not examine whether the effect is of a magnitude of importance to potential recipients of the intervention. In a large study, a small P value may represent the detection of a trivial effect that may not lead to net health benefit when compared with the potential harms (i.e. harmful effects on other important outcomes). Again, inspection of the point estimate and confidence interval helps correct interpretations (see Section 15.3.1 ).

MECIR Box 15.3.a Relevant expectations for conduct of intervention reviews

15.3.3 Relation between confidence intervals, statistical significance and certainty of evidence

The confidence interval (and imprecision) is only one domain that influences overall uncertainty about effect estimates. Uncertainty resulting from imprecision (i.e. statistical uncertainty) may be no less important than uncertainty from indirectness, or any other GRADE domain, in the context of decision making (Schünemann 2016). Thus, the extent to which interpretations of the confidence interval described in Sections 15.3.1 and 15.3.2 correspond to conclusions about overall certainty of the evidence for the outcome of interest depends on these other domains. If there are no concerns about other domains that determine the certainty of the evidence (i.e. risk of bias, inconsistency, indirectness or publication bias), then the interpretation in Sections 15.3.1 and 15.3.2 . about the relation of the confidence interval to the true effect may be carried forward to the overall certainty. However, if there are concerns about the other domains that affect the certainty of the evidence, the interpretation about the true effect needs to be seen in the context of further uncertainty resulting from those concerns.

For example, nine randomized controlled trials in almost 6000 cancer patients indicated that the administration of heparin reduces the risk of venous thromboembolism (VTE), with a risk ratio of 43% (95% CI 19% to 60%) (Akl et al 2011a). For patients with a plausible baseline risk of approximately 4.6% per year, this relative effect suggests that heparin leads to an absolute risk reduction of 20 fewer VTEs (95% CI 9 fewer to 27 fewer) per 1000 people per year (Akl et al 2011a). Now consider that the review authors or those applying the evidence in a guideline have lowered the certainty in the evidence as a result of indirectness. While the confidence intervals would remain unchanged, the certainty in that confidence interval and in the point estimate as reflecting the truth for the question of interest will be lowered. In fact, the certainty range will have unknown width so there will be unknown likelihood of a result within that range because of this indirectness. The lower the certainty in the evidence, the less we know about the width of the certainty range, although methods for quantifying risk of bias and understanding potential direction of bias may offer insight when lowered certainty is due to risk of bias. Nevertheless, decision makers must consider this uncertainty, and must do so in relation to the effect measure that is being evaluated (e.g. a relative or absolute measure). We will describe the impact on interpretations for dichotomous outcomes in Section 15.4 .

15.4 Interpreting results from dichotomous outcomes (including numbers needed to treat)

15.4.1 relative and absolute risk reductions.

Clinicians may be more inclined to prescribe an intervention that reduces the relative risk of death by 25% than one that reduces the risk of death by 1 percentage point, although both presentations of the evidence may relate to the same benefit (i.e. a reduction in risk from 4% to 3%). The former refers to the relative reduction in risk and the latter to the absolute reduction in risk. As described in Chapter 6, Section 6.4.1 , there are several measures for comparing dichotomous outcomes in two groups. Meta-analyses are usually undertaken using risk ratios (RR), odds ratios (OR) or risk differences (RD), but there are several alternative ways of expressing results.

Relative risk reduction (RRR) is a convenient way of re-expressing a risk ratio as a percentage reduction:

For example, a risk ratio of 0.75 translates to a relative risk reduction of 25%, as in the example above.

The risk difference is often referred to as the absolute risk reduction (ARR) or absolute risk increase (ARI), and may be presented as a percentage (e.g. 1%), as a decimal (e.g. 0.01), or as account (e.g. 10 out of 1000). We consider different choices for presenting absolute effects in Section 15.4.3 . We then describe computations for obtaining these numbers from the results of individual studies and of meta-analyses in Section 15.4.4 .

15.4.2 Number needed to treat (NNT)

The number needed to treat (NNT) is a common alternative way of presenting information on the effect of an intervention. The NNT is defined as the expected number of people who need to receive the experimental rather than the comparator intervention for one additional person to either incur or avoid an event (depending on the direction of the result) in a given time frame. Thus, for example, an NNT of 10 can be interpreted as ‘it is expected that one additional (or less) person will incur an event for every 10 participants receiving the experimental intervention rather than comparator over a given time frame’. It is important to be clear that:

• since the NNT is derived from the risk difference, it is still a comparative measure of effect (experimental versus a specific comparator) and not a general property of a single intervention; and
• the NNT gives an ‘expected value’. For example, NNT = 10 does not imply that one additional event will occur in each and every group of 10 people.

NNTs can be computed for both beneficial and detrimental events, and for interventions that cause both improvements and deteriorations in outcomes. In all instances NNTs are expressed as positive whole numbers. Some authors use the term ‘number needed to harm’ (NNH) when an intervention leads to an adverse outcome, or a decrease in a positive outcome, rather than improvement. However, this phrase can be misleading (most notably, it can easily be read to imply the number of people who will experience a harmful outcome if given the intervention), and it is strongly recommended that ‘number needed to harm’ and ‘NNH’ are avoided. The preferred alternative is to use phrases such as ‘number needed to treat for an additional beneficial outcome’ (NNTB) and ‘number needed to treat for an additional harmful outcome’ (NNTH) to indicate direction of effect.

As NNTs refer to events, their interpretation needs to be worded carefully when the binary outcome is a dichotomization of a scale-based outcome. For example, if the outcome is pain measured on a ‘none, mild, moderate or severe’ scale it may have been dichotomized as ‘none or mild’ versus ‘moderate or severe’. It would be inappropriate for an NNT from these data to be referred to as an ‘NNT for pain’. It is an ‘NNT for moderate or severe pain’.

We consider different choices for presenting absolute effects in Section 15.4.3 . We then describe computations for obtaining these numbers from the results of individual studies and of meta-analyses in Section 15.4.4 .

15.4.3 Expressing risk differences

Users of reviews are liable to be influenced by the choice of statistical presentations of the evidence. Hoffrage and colleagues suggest that physicians’ inferences about statistical outcomes are more appropriate when they deal with ‘natural frequencies’ – whole numbers of people, both treated and untreated (e.g. treatment results in a drop from 20 out of 1000 to 10 out of 1000 women having breast cancer) – than when effects are presented as percentages (e.g. 1% absolute reduction in breast cancer risk) (Hoffrage et al 2000). Probabilities may be more difficult to understand than frequencies, particularly when events are rare. While standardization may be important in improving the presentation of research evidence (and participation in healthcare decisions), current evidence suggests that the presentation of natural frequencies for expressing differences in absolute risk is best understood by consumers of healthcare information (Akl et al 2011b). This evidence provides the rationale for presenting absolute risks in ‘Summary of findings’ tables as numbers of people with events per 1000 people receiving the intervention (see Chapter 14 ).

RRs and RRRs remain crucial because relative effects tend to be substantially more stable across risk groups than absolute effects (see Chapter 10, Section 10.4.3 ). Review authors can use their own data to study this consistency (Cates 1999, Smeeth et al 1999). Risk differences from studies are least likely to be consistent across baseline event rates; thus, they are rarely appropriate for computing numbers needed to treat in systematic reviews. If a relative effect measure (OR or RR) is chosen for meta-analysis, then a comparator group risk needs to be specified as part of the calculation of an RD or NNT. In addition, if there are several different groups of participants with different levels of risk, it is crucial to express absolute benefit for each clinically identifiable risk group, clarifying the time period to which this applies. Studies in patients with differing severity of disease, or studies with different lengths of follow-up will almost certainly have different comparator group risks. In these cases, different comparator group risks lead to different RDs and NNTs (except when the intervention has no effect). A recommended approach is to re-express an odds ratio or a risk ratio as a variety of RD or NNTs across a range of assumed comparator risks (ACRs) (McQuay and Moore 1997, Smeeth et al 1999). Review authors should bear these considerations in mind not only when constructing their ‘Summary of findings’ table, but also in the text of their review.

For example, a review of oral anticoagulants to prevent stroke presented information to users by describing absolute benefits for various baseline risks (Aguilar and Hart 2005, Aguilar et al 2007). They presented their principal findings as “The inherent risk of stroke should be considered in the decision to use oral anticoagulants in atrial fibrillation patients, selecting those who stand to benefit most for this therapy” (Aguilar and Hart 2005). Among high-risk atrial fibrillation patients with prior stroke or transient ischaemic attack who have stroke rates of about 12% (120 per 1000) per year, warfarin prevents about 70 strokes yearly per 1000 patients, whereas for low-risk atrial fibrillation patients (with a stroke rate of about 2% per year or 20 per 1000), warfarin prevents only 12 strokes. This presentation helps users to understand the important impact that typical baseline risks have on the absolute benefit that they can expect.

15.4.4 Computations

Direct computation of risk difference (RD) or a number needed to treat (NNT) depends on the summary statistic (odds ratio, risk ratio or risk differences) available from the study or meta-analysis. When expressing results of meta-analyses, review authors should use, in the computations, whatever statistic they determined to be the most appropriate summary for meta-analysis (see Chapter 10, Section 10.4.3 ). Here we present calculations to obtain RD as a reduction in the number of participants per 1000. For example, a risk difference of –0.133 corresponds to 133 fewer participants with the event per 1000.

RDs and NNTs should not be computed from the aggregated total numbers of participants and events across the trials. This approach ignores the randomization within studies, and may produce seriously misleading results if there is unbalanced randomization in any of the studies. Using the pooled result of a meta-analysis is more appropriate. When computing NNTs, the values obtained are by convention always rounded up to the next whole number.

15.4.4.1 Computing NNT from a risk difference (RD)

A NNT may be computed from a risk difference as

where the vertical bars (‘absolute value of’) in the denominator indicate that any minus sign should be ignored. It is convention to round the NNT up to the nearest whole number. For example, if the risk difference is –0.12 the NNT is 9; if the risk difference is –0.22 the NNT is 5. Cochrane Review authors should qualify the NNT as referring to benefit (improvement) or harm by denoting the NNT as NNTB or NNTH. Note that this approach, although feasible, should be used only for the results of a meta-analysis of risk differences. In most cases meta-analyses will be undertaken using a relative measure of effect (RR or OR), and those statistics should be used to calculate the NNT (see Section 15.4.4.2 and 15.4.4.3 ).

15.4.4.2 Computing risk differences or NNT from a risk ratio

To aid interpretation of the results of a meta-analysis of risk ratios, review authors may compute an absolute risk reduction or NNT. In order to do this, an assumed comparator risk (ACR) (otherwise known as a baseline risk, or risk that the outcome of interest would occur with the comparator intervention) is required. It will usually be appropriate to do this for a range of different ACRs. The computation proceeds as follows:

As an example, suppose the risk ratio is RR = 0.92, and an ACR = 0.3 (300 per 1000) is assumed. Then the effect on risk is 24 fewer per 1000:

The NNT is 42:

15.4.4.3 Computing risk differences or NNT from an odds ratio

Review authors may wish to compute a risk difference or NNT from the results of a meta-analysis of odds ratios. In order to do this, an ACR is required. It will usually be appropriate to do this for a range of different ACRs. The computation proceeds as follows:

As an example, suppose the odds ratio is OR = 0.73, and a comparator risk of ACR = 0.3 is assumed. Then the effect on risk is 62 fewer per 1000:

The NNT is 17:

15.4.4.4 Computing risk ratio from an odds ratio

Because risk ratios are easier to interpret than odds ratios, but odds ratios have favourable mathematical properties, a review author may decide to undertake a meta-analysis based on odds ratios, but to express the result as a summary risk ratio (or relative risk reduction). This requires an ACR. Then

It will often be reasonable to perform this transformation using the median comparator group risk from the studies in the meta-analysis.

15.4.4.5 Computing confidence limits

Confidence limits for RDs and NNTs may be calculated by applying the above formulae to the upper and lower confidence limits for the summary statistic (RD, RR or OR) (Altman 1998). Note that this confidence interval does not incorporate uncertainty around the ACR.

If the 95% confidence interval of OR or RR includes the value 1, one of the confidence limits will indicate benefit and the other harm. Thus, appropriate use of the words ‘fewer’ and ‘more’ is required for each limit when presenting results in terms of events. For NNTs, the two confidence limits should be labelled as NNTB and NNTH to indicate the direction of effect in each case. The confidence interval for the NNT will include a ‘discontinuity’, because increasingly smaller risk differences that approach zero will lead to NNTs approaching infinity. Thus, the confidence interval will include both an infinitely large NNTB and an infinitely large NNTH.

15.5 Interpreting results from continuous outcomes (including standardized mean differences)

15.5.1 meta-analyses with continuous outcomes.

Review authors should describe in the study protocol how they plan to interpret results for continuous outcomes. When outcomes are continuous, review authors have a number of options to present summary results. These options differ if studies report the same measure that is familiar to the target audiences, studies report the same or very similar measures that are less familiar to the target audiences, or studies report different measures.

15.5.2 Meta-analyses with continuous outcomes using the same measure

If all studies have used the same familiar units, for instance, results are expressed as durations of events, such as symptoms for conditions including diarrhoea, sore throat, otitis media, influenza or duration of hospitalization, a meta-analysis may generate a summary estimate in those units, as a difference in mean response (see, for instance, the row summarizing results for duration of diarrhoea in Chapter 14, Figure 14.1.b and the row summarizing oedema in Chapter 14, Figure 14.1.a ). For such outcomes, the ‘Summary of findings’ table should include a difference of means between the two interventions. However, when units of such outcomes may be difficult to interpret, particularly when they relate to rating scales (again, see the oedema row of Chapter 14, Figure 14.1.a ). ‘Summary of findings’ tables should include the minimum and maximum of the scale of measurement, and the direction. Knowledge of the smallest change in instrument score that patients perceive is important – the minimal important difference (MID) – and can greatly facilitate the interpretation of results (Guyatt et al 1998, Schünemann and Guyatt 2005). Knowing the MID allows review authors and users to place results in context. Review authors should state the MID – if known – in the Comments column of their ‘Summary of findings’ table. For example, the chronic respiratory questionnaire has possible scores in health-related quality of life ranging from 1 to 7 and 0.5 represents a well-established MID (Jaeschke et al 1989, Schünemann et al 2005).

15.5.3 Meta-analyses with continuous outcomes using different measures

When studies have used different instruments to measure the same construct, a standardized mean difference (SMD) may be used in meta-analysis for combining continuous data. Without guidance, clinicians and patients may have little idea how to interpret results presented as SMDs. Review authors should therefore consider issues of interpretability when planning their analysis at the protocol stage and should consider whether there will be suitable ways to re-express the SMD or whether alternative effect measures, such as a ratio of means, or possibly as minimal important difference units (Guyatt et al 2013b) should be used. Table 15.5.a and the following sections describe these options.

Table 15.5.a Approaches and their implications to presenting results of continuous variables when primary studies have used different instruments to measure the same construct. Adapted from Guyatt et al (2013b)

15.5.3.1 Presenting and interpreting SMDs using generic effect size estimates

The SMD expresses the intervention effect in standard units rather than the original units of measurement. The SMD is the difference in mean effects between the experimental and comparator groups divided by the pooled standard deviation of participants’ outcomes, or external SDs when studies are very small (see Chapter 6, Section 6.5.1.2 ). The value of a SMD thus depends on both the size of the effect (the difference between means) and the standard deviation of the outcomes (the inherent variability among participants or based on an external SD).

If review authors use the SMD, they might choose to present the results directly as SMDs (row 1a, Table 15.5.a and Table 15.5.b ). However, absolute values of the intervention and comparison groups are typically not useful because studies have used different measurement instruments with different units. Guiding rules for interpreting SMDs (or ‘Cohen’s effect sizes’) exist, and have arisen mainly from researchers in the social sciences (Cohen 1988). One example is as follows: 0.2 represents a small effect, 0.5 a moderate effect and 0.8 a large effect (Cohen 1988). Variations exist (e.g. <0.40=small, 0.40 to 0.70=moderate, >0.70=large). Review authors might consider including such a guiding rule in interpreting the SMD in the text of the review, and in summary versions such as the Comments column of a ‘Summary of findings’ table. However, some methodologists believe that such interpretations are problematic because patient importance of a finding is context-dependent and not amenable to generic statements.

15.5.3.2 Re-expressing SMDs using a familiar instrument

The second possibility for interpreting the SMD is to express it in the units of one or more of the specific measurement instruments used by the included studies (row 1b, Table 15.5.a and Table 15.5.b ). The approach is to calculate an absolute difference in means by multiplying the SMD by an estimate of the SD associated with the most familiar instrument. To obtain this SD, a reasonable option is to calculate a weighted average across all intervention groups of all studies that used the selected instrument (preferably a pre-intervention or post-intervention SD as discussed in Chapter 10, Section 10.5.2 ). To better reflect among-person variation in practice, or to use an instrument not represented in the meta-analysis, it may be preferable to use a standard deviation from a representative observational study. The summary effect is thus re-expressed in the original units of that particular instrument and the clinical relevance and impact of the intervention effect can be interpreted using that familiar instrument.

The same approach of re-expressing the results for a familiar instrument can also be used for other standardized effect measures such as when standardizing by MIDs (Guyatt et al 2013b): see Section 15.5.3.5 .

Table 15.5.b Application of approaches when studies have used different measures: effects of dexamethasone for pain after laparoscopic cholecystectomy (Karanicolas et al 2008). Reproduced with permission of Wolters Kluwer

1 Certainty rated according to GRADE from very low to high certainty. 2 Substantial unexplained heterogeneity in study results. 3 Imprecision due to wide confidence intervals. 4 The 20% comes from the proportion in the control group requiring rescue analgesia. 5 Crude (arithmetic) means of the post-operative pain mean responses across all five trials when transformed to a 100-point scale.

15.5.3.3 Re-expressing SMDs through dichotomization and transformation to relative and absolute measures

A third approach (row 1c, Table 15.5.a and Table 15.5.b ) relies on converting the continuous measure into a dichotomy and thus allows calculation of relative and absolute effects on a binary scale. A transformation of a SMD to a (log) odds ratio is available, based on the assumption that an underlying continuous variable has a logistic distribution with equal standard deviation in the two intervention groups, as discussed in Chapter 10, Section 10.6  (Furukawa 1999, Guyatt et al 2013b). The assumption is unlikely to hold exactly and the results must be regarded as an approximation. The log odds ratio is estimated as

(or approximately 1.81✕SMD). The resulting odds ratio can then be presented as normal, and in a ‘Summary of findings’ table, combined with an assumed comparator group risk to be expressed as an absolute risk difference. The comparator group risk in this case would refer to the proportion of people who have achieved a specific value of the continuous outcome. In randomized trials this can be interpreted as the proportion who have improved by some (specified) amount (responders), for instance by 5 points on a 0 to 100 scale. Table 15.5.c shows some illustrative results from this method. The risk differences can then be converted to NNTs or to people per thousand using methods described in Section 15.4.4 .

Table 15.5.c Risk difference derived for specific SMDs for various given ‘proportions improved’ in the comparator group (Furukawa 1999, Guyatt et al 2013b). Reproduced with permission of Elsevier 

15.5.3.4 Ratio of means

A more frequently used approach is based on calculation of a ratio of means between the intervention and comparator groups (Friedrich et al 2008) as discussed in Chapter 6, Section 6.5.1.3 . Interpretational advantages of this approach include the ability to pool studies with outcomes expressed in different units directly, to avoid the vulnerability of heterogeneous populations that limits approaches that rely on SD units, and for ease of clinical interpretation (row 2, Table 15.5.a and Table 15.5.b ). This method is currently designed for post-intervention scores only. However, it is possible to calculate a ratio of change scores if both intervention and comparator groups change in the same direction in each relevant study, and this ratio may sometimes be informative.

Limitations to this approach include its limited applicability to change scores (since it is unlikely that both intervention and comparator group changes are in the same direction in all studies) and the possibility of misleading results if the comparator group mean is very small, in which case even a modest difference from the intervention group will yield a large and therefore misleading ratio of means. It also requires that separate ratios of means be calculated for each included study, and then entered into a generic inverse variance meta-analysis (see Chapter 10, Section 10.3 ).

The ratio of means approach illustrated in Table 15.5.b suggests a relative reduction in pain of only 13%, meaning that those receiving steroids have a pain severity 87% of those in the comparator group, an effect that might be considered modest.

15.5.3.5 Presenting continuous results as minimally important difference units

To express results in MID units, review authors have two options. First, they can be combined across studies in the same way as the SMD, but instead of dividing the mean difference of each study by its SD, review authors divide by the MID associated with that outcome (Johnston et al 2010, Guyatt et al 2013b). Instead of SD units, the pooled results represent MID units (row 3, Table 15.5.a and Table 15.5.b ), and may be more easily interpretable. This approach avoids the problem of varying SDs across studies that may distort estimates of effect in approaches that rely on the SMD. The approach, however, relies on having well-established MIDs. The approach is also risky in that a difference less than the MID may be interpreted as trivial when a substantial proportion of patients may have achieved an important benefit.

The other approach makes a simple conversion (not shown in Table 15.5.b ), before undertaking the meta-analysis, of the means and SDs from each study to means and SDs on the scale of a particular familiar instrument whose MID is known. For example, one can rescale the mean and SD of other chronic respiratory disease instruments (e.g. rescaling a 0 to 100 score of an instrument) to a the 1 to 7 score in Chronic Respiratory Disease Questionnaire (CRQ) units (by assuming 0 equals 1 and 100 equals 7 on the CRQ). Given the MID of the CRQ of 0.5, a mean difference in change of 0.71 after rescaling of all studies suggests a substantial effect of the intervention (Guyatt et al 2013b). This approach, presenting in units of the most familiar instrument, may be the most desirable when the target audiences have extensive experience with that instrument, particularly if the MID is well established.

15.6 Drawing conclusions

15.6.1 conclusions sections of a cochrane review.

Authors’ conclusions in a Cochrane Review are divided into implications for practice and implications for research. While Cochrane Reviews about interventions can provide meaningful information and guidance for practice, decisions about the desirable and undesirable consequences of healthcare options require evidence and judgements for criteria that most Cochrane Reviews do not provide (Alonso-Coello et al 2016). In describing the implications for practice and the development of recommendations, however, review authors may consider the certainty of the evidence, the balance of benefits and harms, and assumed values and preferences.

15.6.2 Implications for practice

Drawing conclusions about the practical usefulness of an intervention entails making trade-offs, either implicitly or explicitly, between the estimated benefits, harms and the values and preferences. Making such trade-offs, and thus making specific recommendations for an action in a specific context, goes beyond a Cochrane Review and requires additional evidence and informed judgements that most Cochrane Reviews do not provide (Alonso-Coello et al 2016). Such judgements are typically the domain of clinical practice guideline developers for which Cochrane Reviews will provide crucial information (Graham et al 2011, Schünemann et al 2014, Zhang et al 2018a). Thus, authors of Cochrane Reviews should not make recommendations.

If review authors feel compelled to lay out actions that clinicians and patients could take, they should – after describing the certainty of evidence and the balance of benefits and harms – highlight different actions that might be consistent with particular patterns of values and preferences. Other factors that might influence a decision should also be highlighted, including any known factors that would be expected to modify the effects of the intervention, the baseline risk or status of the patient, costs and who bears those costs, and the availability of resources. Review authors should ensure they consider all patient-important outcomes, including those for which limited data may be available. In the context of public health reviews the focus may be on population-important outcomes as the target may be an entire (non-diseased) population and include outcomes that are not measured in the population receiving an intervention (e.g. a reduction of transmission of infections from those receiving an intervention). This process implies a high level of explicitness in judgements about values or preferences attached to different outcomes and the certainty of the related evidence (Zhang et al 2018b, Zhang et al 2018c); this and a full cost-effectiveness analysis is beyond the scope of most Cochrane Reviews (although they might well be used for such analyses; see Chapter 20 ).

A review on the use of anticoagulation in cancer patients to increase survival (Akl et al 2011a) provides an example for laying out clinical implications for situations where there are important trade-offs between desirable and undesirable effects of the intervention: “The decision for a patient with cancer to start heparin therapy for survival benefit should balance the benefits and downsides and integrate the patient’s values and preferences. Patients with a high preference for a potential survival prolongation, limited aversion to potential bleeding, and who do not consider heparin (both UFH or LMWH) therapy a burden may opt to use heparin, while those with aversion to bleeding may not.”

15.6.3 Implications for research

The second category for authors’ conclusions in a Cochrane Review is implications for research. To help people make well-informed decisions about future healthcare research, the ‘Implications for research’ section should comment on the need for further research, and the nature of the further research that would be most desirable. It is helpful to consider the population, intervention, comparison and outcomes that could be addressed, or addressed more effectively in the future, in the context of the certainty of the evidence in the current review (Brown et al 2006):

• P (Population): diagnosis, disease stage, comorbidity, risk factor, sex, age, ethnic group, specific inclusion or exclusion criteria, clinical setting;
• I (Intervention): type, frequency, dose, duration, prognostic factor;
• C (Comparison): placebo, routine care, alternative treatment/management;
• O (Outcome): which clinical or patient-related outcomes will the researcher need to measure, improve, influence or accomplish? Which methods of measurement should be used?

While Cochrane Review authors will find the PICO domains helpful, the domains of the GRADE certainty framework further support understanding and describing what additional research will improve the certainty in the available evidence. Note that as the certainty of the evidence is likely to vary by outcome, these implications will be specific to certain outcomes in the review. Table 15.6.a shows how review authors may be aided in their interpretation of the body of evidence and drawing conclusions about future research and practice.

Table 15.6.a Implications for research and practice suggested by individual GRADE domains

The review of compression stockings for prevention of deep vein thrombosis (DVT) in airline passengers described in Chapter 14 provides an example where there is some convincing evidence of a benefit of the intervention: “This review shows that the question of the effects on symptomless DVT of wearing versus not wearing compression stockings in the types of people studied in these trials should now be regarded as answered. Further research may be justified to investigate the relative effects of different strengths of stockings or of stockings compared to other preventative strategies. Further randomised trials to address the remaining uncertainty about the effects of wearing versus not wearing compression stockings on outcomes such as death, pulmonary embolism and symptomatic DVT would need to be large.” (Clarke et al 2016).

A review of therapeutic touch for anxiety disorder provides an example of the implications for research when no eligible studies had been found: “This review highlights the need for randomized controlled trials to evaluate the effectiveness of therapeutic touch in reducing anxiety symptoms in people diagnosed with anxiety disorders. Future trials need to be rigorous in design and delivery, with subsequent reporting to include high quality descriptions of all aspects of methodology to enable appraisal and interpretation of results.” (Robinson et al 2007).

15.6.4 Reaching conclusions

A common mistake is to confuse ‘no evidence of an effect’ with ‘evidence of no effect’. When the confidence intervals are too wide (e.g. including no effect), it is wrong to claim that the experimental intervention has ‘no effect’ or is ‘no different’ from the comparator intervention. Review authors may also incorrectly ‘positively’ frame results for some effects but not others. For example, when the effect estimate is positive for a beneficial outcome but confidence intervals are wide, review authors may describe the effect as promising. However, when the effect estimate is negative for an outcome that is considered harmful but the confidence intervals include no effect, review authors report no effect. Another mistake is to frame the conclusion in wishful terms. For example, review authors might write, “there were too few people in the analysis to detect a reduction in mortality” when the included studies showed a reduction or even increase in mortality that was not ‘statistically significant’. One way of avoiding errors such as these is to consider the results blinded; that is, consider how the results would be presented and framed in the conclusions if the direction of the results was reversed. If the confidence interval for the estimate of the difference in the effects of the interventions overlaps with no effect, the analysis is compatible with both a true beneficial effect and a true harmful effect. If one of the possibilities is mentioned in the conclusion, the other possibility should be mentioned as well. Table 15.6.b suggests narrative statements for drawing conclusions based on the effect estimate from the meta-analysis and the certainty of the evidence.

Table 15.6.b Suggested narrative statements for phrasing conclusions

Another common mistake is to reach conclusions that go beyond the evidence. Often this is done implicitly, without referring to the additional information or judgements that are used in reaching conclusions about the implications of a review for practice. Even when additional information and explicit judgements support conclusions about the implications of a review for practice, review authors rarely conduct systematic reviews of the additional information. Furthermore, implications for practice are often dependent on specific circumstances and values that must be taken into consideration. As we have noted, review authors should always be cautious when drawing conclusions about implications for practice and they should not make recommendations.

15.7 Chapter information

Authors: Holger J Schünemann, Gunn E Vist, Julian PT Higgins, Nancy Santesso, Jonathan J Deeks, Paul Glasziou, Elie Akl, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group

Acknowledgements: Andrew Oxman, Jonathan Sterne, Michael Borenstein and Rob Scholten contributed text to earlier versions of this chapter.

Funding: This work was in part supported by funding from the Michael G DeGroote Cochrane Canada Centre and the Ontario Ministry of Health. JJD receives support from the National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. JPTH receives support from the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

15.8 References

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Santesso N, Carrasco-Labra A, Langendam M, Brignardello-Petersen R, Mustafa RA, Heus P, Lasserson T, Opiyo N, Kunnamo I, Sinclair D, Garner P, Treweek S, Tovey D, Akl EA, Tugwell P, Brozek JL, Guyatt G, Schünemann HJ. Improving GRADE evidence tables part 3: detailed guidance for explanatory footnotes supports creating and understanding GRADE certainty in the evidence judgments. Journal of Clinical Epidemiology 2016; 74 : 28-39.

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Schünemann HJ, Tugwell P, Reeves BC, Akl EA, Santesso N, Spencer FA, Shea B, Wells G, Helfand M. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Research Synthesis Methods 2013; 4 : 49-62.

Schünemann HJ, Wiercioch W, Etxeandia I, Falavigna M, Santesso N, Mustafa R, Ventresca M, Brignardello-Petersen R, Laisaar KT, Kowalski S, Baldeh T, Zhang Y, Raid U, Neumann I, Norris SL, Thornton J, Harbour R, Treweek S, Guyatt G, Alonso-Coello P, Reinap M, Brozek J, Oxman A, Akl EA. Guidelines 2.0: systematic development of a comprehensive checklist for a successful guideline enterprise. CMAJ: Canadian Medical Association Journal 2014; 186 : E123-142.

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How to Write a Research Paper Summary

One of the most important skills you can imbibe as an academician is to know how to summarize a research paper. During your academic journey, you may need to write a summary of findings in research quite often and for varied reasons – be it to write an introduction for a peer-reviewed publication , to submit a critical review, or to simply create a useful database for future referencing.

It can be quite challenging to effectively write a research paper summary for often complex work, which is where a pre-determined workflow can help you optimize the process. Investing time in developing this skill can also help you improve your scientific acumen, increasing your efficiency and productivity at work. This article illustrates some useful advice on how to write a research summary effectively. But, what is research summary in the first place?  

A research paper summary is a crisp, comprehensive overview of a research paper, which encapsulates the purpose, findings, methods, conclusions, and relevance of a study. A well-written research paper summary is an indicator of how well you have understood the author’s work. 

Table of Contents

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• 2. Invest enough time to understand the topic deeply 

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• Mistakes to avoid while writing your research paper summary 

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Frequently asked questions (faq), how to write a research paper summary.

Writing a good research paper summary comes with practice and skill. Here is some useful advice on how to write a research paper summary effectively.  

1. Determine the focus of your summary

Before you begin to write a summary of research papers, determine the aim of your research paper summary. This will give you more clarity on how to summarize a research paper, including what to highlight and where to find the information you need, which accelerates the entire process. If you are aiming for the summary to be a supporting document or a proof of principle for your current research findings, then you can look for elements that are relevant to your work.

On the other hand, if your research summary is intended to be a critical review of the research article, you may need to use a completely different lens while reading the paper and conduct your own research regarding the accuracy of the data presented. Then again, if the research summary is intended to be a source of information for future referencing, you will likely have a different approach. This makes determining the focus of your summary a key step in the process of writing an effective research paper summary. 

2. Invest enough time to understand the topic deeply

In order to author an effective research paper summary, you need to dive into the topic of the research article. Begin by doing a quick scan for relevant information under each section of the paper. The abstract is a great starting point as it helps you to quickly identify the top highlights of the research article, speeding up the process of understanding the key findings in the paper. Be sure to do a careful read of the research paper, preparing notes that describe each section in your own words to put together a summary of research example or a first draft. This will save your time and energy in revisiting the paper to confirm relevant details and ease the entire process of writing a research paper summary.

When reading papers, be sure to acknowledge and ignore any pre-conceived notions that you might have regarding the research topic. This will not only help you understand the topic better but will also help you develop a more balanced perspective, ensuring that your research paper summary is devoid of any personal opinions or biases. 

3. Keep the summary crisp, brief and engaging

A research paper summary is usually intended to highlight and explain the key points of any study, saving the time required to read through the entire article. Thus, your primary goal while compiling the summary should be to keep it as brief, crisp and readable as possible. Usually, a short introduction followed by 1-2 paragraphs is adequate for an effective research article summary. Avoid going into too much technical detail while describing the main results and conclusions of the study. Rather focus on connecting the main findings of the study to the hypothesis , which can make the summary more engaging. For example, instead of simply reporting an original finding – “the graph showed a decrease in the mortality rates…”, you can say, “there was a decline in the number of deaths, as predicted by the authors while beginning the study…” or “there was a decline in the number of deaths, which came as a surprise to the authors as this was completely unexpected…”.

Unless you are writing a critical review of the research article, the language used in your research paper summaries should revolve around reporting the findings, not assessing them. On the other hand, if you intend to submit your summary as a critical review, make sure to provide sufficient external evidence to support your final analysis. Invest sufficient time in editing and proofreading your research paper summary thoroughly to ensure you’ve captured the findings accurately. You can also get an external opinion on the preliminary draft of the research paper summary from colleagues or peers who have not worked on the research topic. 

Mistakes to avoid while writing your research paper summary

Now that you’ve understood how to summarize a research paper, watch out for these red flags while writing your summary. 

• Not paying attention to the word limit and recommended format, especially while submitting a critical review 
• Evaluating the findings instead of maintaining an objective , unbiased view while reading the research paper 
• Skipping the essential editing step , which can help eliminate avoidable errors and ensure that the language does not misrepresent the findings 
• Plagiarism, it is critical to write in your own words or paraphrase appropriately when reporting the findings in your scientific article summary 

We hope the recommendations listed above will help answer the question of how to summarize a research paper and enable you to tackle the process effectively. 

Summarize your research paper with Paperpal

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How to summarize a research paper with Paperpal?

To generate your research paper summary, simply login to the platform and use the Paperpal Copilot Summary feature to create a flawless summary of your work. Here’s a step-by-step process to help you craft a summary in minutes:

• Paste relevant research articles to be summarized into Paperpal; the AI will scan each section and extract key information.
• In minutes, Paperpal will generate a comprehensive summary that showcases the main paper highlights while adhering to academic writing conventions.
• Check the content to polish and refine the language, ensure your own voice, and add citations or references as needed.

The abstract and research paper summary serve similar purposes but differ in scope, length, and placement. The abstract is a concise yet detailed overview of the research, placed at the beginning of a paper, with the aim of providing readers with a quick understanding of the paper’s content and to help them decide whether to read the full article. Usually limited to a few hundred words, it highlights the main objectives, methods, results, and conclusions of the study. On the other hand, a research paper summary provides a crisp account of the entire research paper. Its purpose is to provide a brief recap for readers who may want to quickly grasp the main points of the research without reading the entire paper in detail.

The structure of a research summary can vary depending on the specific requirements or guidelines provided by the target publication or institution. A typical research summary includes the following key sections: introduction (including the research question or objective), methodology (briefly describing the research design and methods), results (summarizing the key findings), discussion (highlighting the implications and significance of the findings), and conclusion (providing a summary of the main points and potential future directions).

The summary of a research paper is important because it provides a condensed overview of the study’s purpose, methods, results, and conclusions. It allows you to quickly grasp the main points and relevance of the research without having to read the entire paper. Research summaries can also be an invaluable way to communicate research findings to a broader audience, such as policymakers or the general public.

  When writing a research paper summary, it is crucial to avoid plagiarism by properly attributing the original authors’ work. To learn how to summarize a research paper while avoiding plagiarism, follow these critical guidelines: (1) Read the paper thoroughly to understand the main points and key findings. (2) Use your own words and sentence structures to restate the information, ensuring that the research paper summary reflects your understanding of the paper. (3) Clearly indicate when you are paraphrasing or quoting directly from the original paper by using appropriate citation styles. (4) Cite the original source for any specific ideas, concepts, or data that you include in your summary. (5) Review your summary to ensure it accurately represents the research paper while giving credit to the original authors.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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• Ethical Research Practices For Research with Human Subjects
• How to Write a Conclusion for Research Papers (with Examples)
• Publish or Perish – Understanding the Importance of Scholarly Publications in Academia

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A Complete Guide to Writing a Research Summary

A summary is a key part of any research. So, how should you go about writing one?

You will find many guides on the Internet about writing research. But, any article seldom covers the prospect of writing a research summary. While many things are shortened versions of the original article, there’s much more to research summaries.

From descriptive statistics to writing scientific research, a summary plays a vital role in describing the key ideas within. So, it begs a few questions, such as:

• What exactly is a research summary?
• How do you write one?
• What are some of the tips for writing a good research summary ?

In this guide, we’ll answer all of these questions and explore a few essential factors about research writing. So, let’s jump right into it.

What is a Research Summary?

A research summary is a short, concise summary of an academic research paper. It is often used to summarize the results of an experiment, summarize the major findings and conclusions, and provide a brief overview of the methods and procedures used in the study.

The purpose of a research summary is to provide readers with enough information about an article to decide whether they want to read it in its entirety. It should be no more than two paragraphs long and should include:

• A brief introduction summarizing why the article was written
• The main idea of the article
• The major findings and conclusions
• An overview of how the study was conducted

In order to write effective research summaries, it is important that you can capture the essential points of the research and provide a concise overview. The key step in writing a good summary is to read through the article and make notes of the key points.

This can be done by underlining or highlighting key phrases in the article. One essential thing is to organize these points into an outline format, which includes an introduction and conclusion paragraph.

Another best and quick way to generate a precise summary of your research paper is to take assistance from the online text summarizer, like Summarizer.org .

The online summarizing tool gets the research paper and creates a precise summary of it by taking the important points.

Finally, you must edit your work for grammar and spelling errors before submitting it for grading.

The purpose of the research summary is to provide a comprehensive sum of everything that’s in the research. This includes a summarization of scientific/literal research, as well as of the writer’s aim and personal thoughts.

As for the summary length, it shouldn’t be more than 10% of the entire content. So, if your research is around 1000-words or so, then your summary should be 100-words. But, considering how most research papers are around 3000-4000 words, it should be 300-400 words.

Key pillars of a Research Summary

The summary of any research doesn’t just include the summarized text of the entire research paper. It includes a few other key things, which we’ll explore later on in this article. But, the purpose of a summary is to give proper insights to the reader, such as:

• The writer’s intention
• sources and bases of research
• the purpose & result.

That’s why it’s important to understand that the summary should tell your reader all these elements. So, the fundamentals of any summary include:

• Write a section and state the importance of the research paper from your perspective. In this section, you will have to describe the techniques, tools, and sources you employed to get the conclusion.
• Besides that, it’s also meant to provide a brief and descriptive explanation of the actionable aspect of your research. In other words, how it can be implemented in real life.
• Treat your research summary like a smaller article or blog. So, each important section of your research should be written within a subheading. However, this is highly optional to keep things organized.
• As mentioned before, the research summary shouldn’t exceed 300-400 words. But, some research summaries are known to surpass 10000-words. So, try to employ the 10% formula and write one-tenth of the entire length of your research paper.

These four main points allow you to understand how a research summary is different from the research itself. So, it’s like a documentary where research and other key factors are left to the science (research paper), while the narration explains the key points (research summary)

How do you write a Research Summary?

Writing a research summary is a straightforward affair. Yet, it requires some understanding, as it’s not a lengthy process but rather a tricky and technical one. In a research summary, a few boxes must be checked. To help you do just that, here are 6 things you should tend to separately:

A summary’s title can be the same as the title of your primary research. However, putting separate titles in both has a few benefits. Such as:

• A separate title shifts attention towards the conclusion.
• A different title can focus on the main point of your research.
• Using two different titles can provide a better abstract.

Speaking of an abstract, a summary is the abstract of your research. Therefore, a title representing that very thought is going to do a lot of good too. That’s why it’s better if the title of your summary differs from the title of your research paper.

2. Abstract

The abstract is the summarization of scientific or research methods used in your primary paper. This allows the reader to understand the pillars of the study conducted. For instance, there has been an array of astrological research since James Webb Space Telescope started sending images and data.

So, many research papers explain this Telescope’s technological evolution in their abstracts. This allows the reader to differentiate from the astrological research made by previous space crafts, such as Hubble or Chandra .

The point of providing this abstract is to ensure that the reader grasps the standards or boundaries within which the research was held.

3. Introduction

This is the part where you introduce your topic. In your main research, you’d dive right into the technicalities in this part. However, you’ll try to keep things mild in a research summary. Simply because it needs to summarize the key points in your main introduction.

So, a lot of introductions you’ll find as an example will be extensive in length. But, a research summary needs to be as concise as possible. Usually, in this part, a writer includes the basics and standards of investigation.

For instance, if your research is about James Webb’s latest findings , then you’ll identify how the studies conducted by this Telescope’s infrared and other technology made this study possible. That’s when your introduction will hook the reader into the main premise of your research.

4. Methodology / Study

This section needs to describe the methodology used by you in your research. Or the methodology you relied on when conducting this particular research or study. This allows the reader to grasp the fundamentals of your research, and it’s extremely important.

Because if the reader doesn’t understand your methods, then they will have no response to your studies. How should you tend to this? Include things such as:

• The surveys or reviews you used;
• include the samplings and experiment types you researched;
• provide a brief statistical analysis;
• give a primary reason to pick these particular methods.

Once again, leave the scientific intricacies for your primary research. But, describe the key methods that you employed. So, when the reader is perusing your final research, they’ll have your methods and study techniques in mind.

5. Results / Discussion

This section of your research needs to describe the results that you’ve achieved. Granted, some researchers will rely on results achieved by others. So, this part needs to explain how that happened – but not in detail.

The other section in this part will be a discussion. This is your interpretation of the results you’ve found. Thus, in the context of the results’ application, this section needs to dive into the theoretical understanding of your research. What will this section entail exactly? Here’s what:

• Things that you covered, including results;
• inferences you provided, given the context of your research;
• the theory archetype that you’ve tried to explain in the light of the methodology you employed;
• essential points or any limitations of the research.

These factors will help the reader grasp the final idea of your research. But, it’s not full circle yet, as the pulp will still be left for the actual research.

6. Conclusion

The final section of your summary is the conclusion. The key thing about the conclusion in your research summary, compared to your actual research, is that they could be different. For instance, the actual conclusion in your research should bring around the study.

However, the research in this summary should bring your own ideas and affirmations to full circle. Thus, this conclusion could and should be different from the ending of your research.

5 Tips for writing a Research Summary

Writing a research summary is easy once you tend to the technicalities. But, there are some tips and tricks that could make it easier. Remember, a research summary is the sum of your entire research. So, it doesn’t need to be as technical or in-depth as your primary work.

Thus, to make it easier for you, here are four tips you can follow:

1. Read & read again

Reading your own work repeatedly has many benefits. First, it’ll help you understand any mistakes or problems your research might have. After that, you’ll find a few key points that stand out from the others – that’s what you need to use in your summary.

So, the best advice anyone can give you is to read your research again and again. This will etch the idea in your mind and allow you to summarize it better.

2. Focus on key essentials in each section

As we discussed earlier, each section of your research has a key part. To write a thoroughly encapsulating summary, you need to focus on and find each such element in your research.

Doing so will give you enough leverage to write a summary that thoroughly condenses your research idea and gives you enough to write a summary out of it.

3. Write the research using a summarizing tool

The best advice you can get is to write a summary using a tool. Condensing each section might be a troublesome experience for some – as it can be time-consuming.

To avoid all that, you can simply take help from an online summarizer. It gets the lengthy content and creates a precise summary of it by using advanced AI technology.

As you can see, the tool condenses this particular section perfectly while the details are light.

Bringing that down to 10% or 20% will help you write each section accordingly. Thus, saving precious time and effort.

4. Word count limit

As mentioned earlier, word count is something you need to follow thoroughly. So, if your section is around 200-word, then read it again. And describe it to yourself in 20-words or so. Doing this to every section will help you write exactly a 10% summary of your research.

5. Get a second opinion

If you’re unsure about quality or quantity, get a second opinion. At times, ideas are in our minds, but we cannot find words to explain them. In research or any sort of creative process, getting a second opinion can save a lot of trouble.

There’s your guide to writing a research summary, folks. While it’s not different from condensing the entire premise of your research, writing it in simpler words will do wonders. So, try to follow the tips, tools, and ideas provided in this article, and write outstanding summaries for your research.

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• Knowledge Base
• Working with sources
• How to Write a Summary | Guide & Examples

How to Write a Summary | Guide & Examples

Published on November 23, 2020 by Shona McCombes . Revised on May 31, 2023.

Summarizing , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.

There are five key steps that can help you to write a summary:

• Read the text
• Break it down into sections
• Identify the key points in each section
• Write the summary
• Check the summary against the article

Writing a summary does not involve critiquing or evaluating the source . You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).

Table of contents

When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, other interesting articles, frequently asked questions about summarizing.

There are many situations in which you might have to summarize an article or other source:

• As a stand-alone assignment to show you’ve understood the material
• To keep notes that will help you remember what you’ve read
• To give an overview of other researchers’ work in a literature review

When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.

But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyze or critique it.

In any case, the goal of summarizing is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.

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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:

• Scan the article quickly to get a sense of its topic and overall shape.
• Read the article carefully, highlighting important points and taking notes as you read.
• Skim the article again to confirm you’ve understood the key points, and reread any particularly important or difficult passages.

There are some tricks you can use to identify the key points as you read:

• Start by reading the abstract . This already contains the author’s own summary of their work, and it tells you what to expect from the article.
• Pay attention to headings and subheadings . These should give you a good sense of what each part is about.
• Read the introduction and the conclusion together and compare them: What did the author set out to do, and what was the outcome?

To make the text more manageable and understand its sub-points, break it down into smaller sections.

If the text is a scientific paper that follows a standard empirical structure, it is probably already organized into clearly marked sections, usually including an introduction , methods , results , and discussion .

Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.

Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?

Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.

In a scientific article, there are some easy questions you can ask to identify the key points in each part.

If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.

In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.

Prevent plagiarism. Run a free check.

Now that you know the key points that the article aims to communicate, you need to put them in your own words.

To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.

The best way to do this is to put the article aside and write out your own understanding of the author’s key points.

Examples of article summaries

Let’s take a look at an example. Below, we summarize this article , which scientifically investigates the old saying “an apple a day keeps the doctor away.”

Davis et al. (2015) set out to empirically test the popular saying “an apple a day keeps the doctor away.” Apples are often used to represent a healthy lifestyle, and research has shown their nutritional properties could be beneficial for various aspects of health. The authors’ unique approach is to take the saying literally and ask: do people who eat apples use healthcare services less frequently? If there is indeed such a relationship, they suggest, promoting apple consumption could help reduce healthcare costs.

The study used publicly available cross-sectional data from the National Health and Nutrition Examination Survey. Participants were categorized as either apple eaters or non-apple eaters based on their self-reported apple consumption in an average 24-hour period. They were also categorized as either avoiding or not avoiding the use of healthcare services in the past year. The data was statistically analyzed to test whether there was an association between apple consumption and several dependent variables: physician visits, hospital stays, use of mental health services, and use of prescription medication.

Although apple eaters were slightly more likely to have avoided physician visits, this relationship was not statistically significant after adjusting for various relevant factors. No association was found between apple consumption and hospital stays or mental health service use. However, apple eaters were found to be slightly more likely to have avoided using prescription medication. Based on these results, the authors conclude that an apple a day does not keep the doctor away, but it may keep the pharmacist away. They suggest that this finding could have implications for reducing healthcare costs, considering the high annual costs of prescription medication and the inexpensiveness of apples.

However, the authors also note several limitations of the study: most importantly, that apple eaters are likely to differ from non-apple eaters in ways that may have confounded the results (for example, apple eaters may be more likely to be health-conscious). To establish any causal relationship between apple consumption and avoidance of medication, they recommend experimental research.

An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.

For example, in a literature review or meta analysis you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.

Using national survey data, Davis et al. (2015) tested the assertion that “an apple a day keeps the doctor away” and did not find statistically significant evidence to support this hypothesis. While people who consumed apples were slightly less likely to use prescription medications, the study was unable to demonstrate a causal relationship between these variables.

Citing the source you’re summarizing

When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.

You can easily create your citations and references in APA or MLA using our free citation generators.

APA Citation Generator MLA Citation Generator

Finally, read through the article once more to ensure that:

• You’ve accurately represented the author’s work
• You haven’t missed any essential information
• The phrasing is not too similar to any sentences in the original.

If you’re summarizing many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• ChatGPT citations
• Is ChatGPT trustworthy?
• Using ChatGPT for your studies
• What is ChatGPT?
• Chicago style
• Paraphrasing

 Plagiarism

• Types of plagiarism
• Self-plagiarism
• Avoiding plagiarism
• Academic integrity
• Consequences of plagiarism
• Common knowledge

A summary is a short overview of the main points of an article or other source, written entirely in your own words. Want to make your life super easy? Try our free text summarizer today!

A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarizing, and on the purpose of the summary.

You might have to write a summary of a source:

• As a stand-alone assignment to prove you understand the material
• For your own use, to keep notes on your reading
• To provide an overview of other researchers’ work in a literature review
• In a paper , to summarize or introduce a relevant study

To avoid plagiarism when summarizing an article or other source, follow these two rules:

• Write the summary entirely in your own words by paraphrasing the author’s ideas.
• Cite the source with an in-text citation and a full reference so your reader can easily find the original text.

An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarize the whole text, not just introduce it.

An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarize a source in a paper , in a literature review , or as a standalone assignment.

All can be done within seconds with our free text summarizer .

Cite this Scribbr article

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McCombes, S. (2023, May 31). How to Write a Summary | Guide & Examples. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/working-with-sources/how-to-summarize/

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Literature Review

12 Best Tools For Perfect Research Summary Writing

Discover the 12 best tools to streamline your research summary writing, ensuring clarity and precision every time.

Aug 29, 2024

Consider you finally find the time to tackle that research paper for your class. You pull up your literature search and see dozens of articles and studies staring back at you. As you scroll through the titles and abstracts, you realize you need to figure out how to summarize the research to get started on your paper. 

Writing a practical research summary can feel daunting, but it doesn’t have to. In this guide, we’ll break down what a research summary is, why it’s essential, and how to write one. This information lets you confidently write your research summary and finish your paper. 

Otio’s AI research and writing partner can help you write efficient research summaries and papers. Our tool can summarize academic articles so you can understand the material and finish your writing.

Table Of Contents

What is a research summary, purpose of a research summary, how do you write a research summary in 10 simple steps, what is a phd research summary, examples of research summary, supercharge your researching ability with otio — try otio for free today.

A research summary is a piece of writing that summarizes your research on a specific topic. Its primary goal is to offer the reader a detailed study overview with critical findings. A research summary generally contains the structure of the article. 

You must know the goal of your analysis before you launch a project. A research overview summarizes the detailed response and highlights particular issues. Writing it may be troublesome. You want to start with a structure in mind to write a good overview. 

Related Reading

• Systematic Review Vs Meta Analysis • Impact Evaluation • How To Critique A Research Article • How To Synthesize Sources • Annotation Techniques • Skimming And Scanning • Types Of Literature Reviews • Literature Review Table • Literature Review Matrix • How To Increase Reading Speed And Comprehension • How To Read Research Papers • How To Summarize A Research Paper • Literature Gap

A research summary provides a brief overview of a study to readers. When searching for literature, a reader can quickly grasp the central ideas of a paper by reading its summary. It is also a great way to elaborate on the significance of the findings, reminding the reader of the strengths of your main arguments. 

Having a good summary is almost as important as writing a research paper. The benefit of summarizing is showing the "big picture," which allows the reader to contextualize your words. In addition to the advantages of summarizing for the reader, as a writer, you gain a better sense of where you are going with your writing, which parts need elaboration, and whether you have comprehended the information you have collected. 

1. Read The Entire Research Paper

Before writing a research summary , you must read and understand the entire research paper. This may seem like a time-consuming task, but it is essential to write a good summary. Make sure you know the paper's main points before you begin writing.

2. Take Notes As You Read

As you read, take notes on the main points of the paper. These notes will come in handy when you are writing your summary. Be sure to note any necessary information, such as the main conclusions of the author's writing. This helpful tip will also help you write a practical blog summary in less time.

3. Organize Your Thoughts

Once you have finished reading and taking notes on the paper, it is time to start writing your summary. Before you begin, take a few minutes to organize your thoughts. Write down the main points that you want to include in your summary. Then, arrange these points in a logical order.

4. Write The Summary

Now that you have organized your thoughts, it is time to start writing the summary. Begin by stating the author’s thesis statement or main conclusion. Then, briefly describe each of the main points from the paper. Be sure to write clearly and concisely. When you finish, reread your summary to ensure it accurately reflects the paper's content.

5. Write The Introduction

After you have written the summary, it is time to write the introduction. The introduction should include an overview of the paper and a summary description. It should also state the main idea.

6. Introduce The Report's Purpose

The summary of a research paper should include a brief description of the paper's purpose. It should state the paper's thesis statement and briefly describe each of the main points of the paper.

7. Use Keywords To Introduce The Report

When introducing the summary of a research paper, use keywords familiar to the reader. This will help them understand the summary and why it is essential.

8. State The Author's Conclusions

The summary of a research paper should include a brief statement of the author's conclusions. This will help your teacher understand what the paper is trying to achieve.

9. Keep It Concise

A summary should be concise and to the point. It should not include any new information or arguments. It should be one paragraph long at maximum.

10. Edit And Proofread

After you have written the summary, edit and proofread it to ensure it is accurate and precise. This will help ensure that your summary is effective and free of any grammar or spelling errors.

1. Otio: Your AI Research Assistant  

Knowledge workers, researchers, and students today need help with content overload and are left to deal with it using fragmented, complex, and manual tooling. Too many settle for stitching together complicated bookmarking, read-it-later, and note-taking apps to get through their workflows. Now that anyone can create content with a button, this problem will only worsen. Otio solves this problem by providing researchers with one AI-native workspace. It helps them: 

1. Collect a wide range of data sources, from bookmarks, tweets, and extensive books to YouTube videos. 

2. extract key takeaways with detailed ai-generated notes and source-grounded q&a chat. , 3. create draft outputs using the sources you’ve collected. .

Otio helps you to go from a reading list to the first draft faster. Along with this, Otio also enables you to write research papers/essays faster. Here are our top features that researchers love: AI-generated notes on all bookmarks (Youtube videos, PDFs, articles, etc.), Otio enables you to chat with individual links or entire knowledge bases, just like you chat with ChatGPT, as well as AI-assisted writing. 

Let Otio be your AI research and writing partner — try Otio for free today ! 

2. Hypotenuse AI: The Versatile Summarizer  

Like all the AI text summarizers on this list, Hypotenuse AI can take the input text and generate a short summary. One area where it stands out is its ability to handle various input options: You can simply copy-paste the text, directly upload a PDF, or even drop a YouTube link to create summaries. 

You can summarize nearly 200,000 characters (or 50,000 words) at once. 

Hypotenuse AI summarizes articles, PDFs, paragraphs, documents, and videos. 

With the AI tool, you can create engaging hooks and repurpose content for social media. 

You'll need a paid plan after the 7-day free trial. 

There needs to be a free plan available. 

The AI tool majorly focuses on generating eCommerce and marketing content. 

3. Scalenut: The Beginner-Friendly AI Summarizer  

Scalenut is one of the powerful AI text summarizers for beginners or anyone starting out. While it's not as polished as some other business-focused apps, it's significantly easier to use — and the output is just as good as others. If you want a basic online text summarizer that lets you summarize the notes within 800 characters (not words), Scalenut is your app. 

With Scalenut, you get a dedicated summary generation tool for more granular control. 

The keyword planner available helps build content directly from the short and sweet summaries. 

The AI tool integrates well with a whole suite of SEO tools, making it a more SEO-focused summarizer. 

You only get to generate one summary per day. 

Scalenut's paid plans are expensive compared to other AI tools. 

You must summarize long-form articles or blogs at most the limit of 800 characters. 

4. SciSummary: The Academic AI Summarizer  

SciSummary is an AI summarizer that helps summarize single or multiple research papers. It combines and compares the content summaries from research papers, article links, etc. 

It can save time and effort for scientists, students, and enthusiasts who want to keep up with the latest scientific developments. 

It can provide accurate and digestible summaries powered by advanced AI models that learn from feedback and expert guidance. 

It can help users read between the lines and understand complex scientific texts' main points and implications. 

It may only capture some nuances and details of the original articles or papers, which may be necessary for some purposes or audiences. 

Some types of scientific texts, such as highly technical, specialized, or interdisciplinary, may require more domain knowledge or context. 

Some sources of scientific information, such as websites, videos, or podcasts not in text format, may need help summarizing. 

5. Quillbot: The AI Summarizer for Academic Papers  

QuillBot uses advanced neural network models to summarize research papers accurately and effectively. The tool leverages cutting-edge technology to condense lengthy papers into concise and informative summaries, making it easier for users to navigate vast amounts of literature. 

You can upload the text for summarization directly from a document. 

It's excellent for summarizing essays, papers, and lengthy documents. 

You can summarize long texts up to 1200 words for free. 

The free plan is limited to professionals. 

There could have been some more output types. 

QuillBot's Premium plan only gives you 6000 words for summaries per month. 

6. Scribbr: The Research Paper Assistant  

Scribbr is an AI-driven academic writing assistant with a summarization feature tailored for research papers. The tool assists users in the research paper writing process by summarizing and condensing information from various sources, offering support in structuring and organizing content effectively. 

7. TLDR This: The Online Article Summarizer  

TLDR This uses advanced AI to effectively filter out unimportant arguments from online articles and provide readers only with vital takeaways. Its streamlined interface eliminates ads and distractions while summarizing key points, metadata, images, and other crucial article details. 

TLDR This condenses even very lengthy materials into compact summaries users can quickly consume, making it easier to process a vast range of internet content efficiently. 

Ten free "AI" summaries 

Summarization of long text 

Basic summary extraction 

Premium option cost 

No significant improvement in premium options 

8. AI Summarizer: The Text Document Summarizer  

AI Summarizer harnesses artificial intelligence to summarize research papers and other text documents automatically. The tool streamlines the summarization process, making it efficient and accurate, enabling users to extract essential information from extensive research papers efficiently. 

Easy-to-understand interface 

1500-word limit 

Multiple language support 

Contains advertisements 

Requires security captcha completion 

Struggles with lengthy content summarization 

9. Jasper: The Advanced Summarizer  

Jasper AI is a robust summarizing tool that helps users generate AI-powered paper summaries quickly and effectively. The tool supports the prompt creation of premium-quality summaries, assisting researchers in distilling complex information into concise and informative outputs. 

Jasper offers some advanced features, like generating a text from scratch and even summarizing it. 

It integrates well with third-party apps like Surfer, Grammarly, and its own AI art generator. 

It's versatile and can be used to create summaries of blogs, articles, website copy, emails, and even social media posts. 

There's no free plan available — though you get a 7-day free trial. 

You'll need to have a flexible budget to use Jasper AI. 

The Jasper app has a steep learning curve. 

10. Resoomer: The Summary Extractor  

Resoomer rapidly analyzes textual documents to determine the essential sentences and summarizes these key points using its proprietary semantic analysis algorithm. 

By automatically identifying what information matters most, Resoomer can condense elaborate texts across diverse subjects into brief overviews of their core message. With swift copy-and-paste functionality requiring no signup, this specialized tool simplifies the reading experience by extracting only vital details from complex writings. 

Clear and accurate summaries 

Creative sentence combining 

Variety of modes and options 

Lengthy text summarization without word limit in premium mode 

Confusing interface with irrelevant features 

Long-winded summaries spread across multiple pages 

11. Anyword: The Marketing-Focused Summarizer  

When I saw Anyword's summary, I could easily state that the content was unique and worth sharing, making this AI tool an excellent choice for marketers. Plus, it's very easy to use.  

Once you've copied-pasted the text and chosen a summary type, paragraph, keywords, or TL;DR, it generates a summary in minutes. Approve it; you can share the text directly without worrying about plagiarized content. 

You can test the AI tool with the 7-day free trial. 

The Anyword's text generator and summarizer are perfect for creating long-form pieces like blog posts with snippets. 

You can give detailed prompts to the AI tool to customize the generated text. 

Any word is expensive for a more limited set of features than other AI summarizers. 

It can sometimes be slower to use. 

There is no free Anyword plan available. 

12. Frase: The SEO Summarizer  

Frase is a powerful AI-powered summarizer that focuses on SEO. This means it can generate summaries that attract audiences and rank higher. Its proprietary model stands out, providing more flexibility, competitive pricing, and custom features. 

Frase uses BLUF and Reverse Pyramid techniques to generate summaries, improving ranking chances. 

It's free to use Frase's summary generator. 

Instead of GPT-3.5 or GPT-4, Frase uses its proprietary model. 

There's no way to add links to the blog or article to generate a summary. 

You can input up to 600-700 words for summarization. 

It might not be an ideal article summarizer for those who don't care about SEO. 

A research summary for a PhD is called a research statement . The research statement (or statement of research interests) is included in academic job applications. It summarizes your research accomplishments, current work, and future direction and potential. The statement can discuss specific issues such as funding history and potential requirements for laboratory equipment and space and other resources, possible research and industrial collaborations, and how your research contributes to your field's future research direction. 

The research statement should be technical but intelligible to all department members, including those outside your subdiscipline. So keep the “big picture” in mind. The strongest research statements present a readable, compelling, and realistic research agenda that fits well with the department's needs, facilities, and goals. Research statements can be weakened by: overly ambitious proposals lack of apparent direction lack of big-picture focus, and inadequate attention to the needs and facilities of the department or position. 

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Research Summary Example 1: A Look at the Probability of an Unexpected Volcanic Eruption in Yellowstone 

Introduction  .

If the Yellowstone supervolcano erupted massively , the consequences would be catastrophic for the United States. The importance of analyzing the likelihood of such an eruption cannot be overstated.  

Hypothesis  

An eruption of the Yellowstone supervolcano would be preceded by intense precursory activity manifesting a few weeks up to a few years in advance.  

Results     

Statistical data from multiple volcanic eruptions happening worldwide show activity that preceded these events (in particular, how early each type of activity was detected).   

Discussion and Conclusion  

Given that scientists continuously monitor Yellowstone and that signs of an eruption are normally detected much in advance, at least a few days in advance, the hypothesis is confirmed. This could be applied to creating emergency plans detailing an organized evacuation campaign and other response measures.     

Research Summary Example 2: The Frequency of Extreme Weather Events in the US from 2000-2008 as Compared to the ‘50s

Weather events bring immense material damage and cause human victims.    

Extreme weather events are significantly more frequent nowadays than in the ‘50s.   

Several categories of extreme events occur regularly now and then: droughts and associated fires, massive rainfall/snowfall and associated floods, hurricanes, tornadoes, Arctic cold waves, etc.   

Discussion and Conclusion 

Several extreme events have become significantly more frequent recently, confirming this hypothesis. This increasing frequency correlates reliably with rising CO2 levels in the atmosphere and growing temperatures worldwide. 

In the absence of another recent significant global change that could explain a higher frequency of disasters, and knowing how growing temperature disturbs weather patterns, it is natural to assume that global warming (CO2) causes this increase in frequency. This, in turn, suggests that this increased frequency of disasters is not a short-term phenomenon but is here to stay until we address CO2 levels.  

Researchers, students, and knowledge workers have long struggled with the initial stages of research projects. The early steps of gathering and organizing information , taking notes, and synthesizing the material into a coherent summary are vital for establishing a solid foundation for any research endeavor. 

These steps can be tedious, overwhelming, and time-consuming. Otio streamlines this process so you can go from the reading list to the first draft faster. Along with this, Otio also helps you write research papers/essays faster. Here are our top features that researchers love: 

AI-generated notes on all bookmarks (Youtube videos, PDFs, articles, etc.), Otio enables you to chat with individual links or entire knowledge bases, just like you chat with ChatGPT, as well as AI-assisted writing. 

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Six elements a research summary should include

Summarizing a research paper (or papers) sounds like it should be a pretty quick, easy task. After all, how hard can writing 200 words be?! But whether you’re writing a summary to include in your essay or dissertation, or you need to draft a compelling abstract for your own paper, distilling complex research into an informative, easy-to-read snapshot can be one of the most daunting parts of the research process. For that reason, it’s often the activity that gets left to last.

Having a few questions top of mind while you draft your summary can really help to structure your thoughts and make sure you include the most important aspects of the research. In short, every academic summary should cover ‘the why’, ‘the how’, ‘the who’ and ‘the what’ of a study. Asking yourself the following six questions as you start to think about your summary can help you to structure your thoughts and find the right words.

1.  Why is this study necessary and important?

The ‘why’ can often be found in the first sentence of the introduction or background of a research article. Let’s have a look at a 2014 paper about plastic pollution in the world’s oceans (1) :

" Plastic pollution is globally distributed across all oceans due to its properties of buoyancy and durability, and the sorption of toxicants to plastic while traveling through the environment have led some researchers to claim that synthetic polymers in the ocean should be regarded as hazardous waste."

Another quick way of identifying the ‘why’ of the research is to search for the subject of the study (eg. ‘Plastic pollution in the world’s oceans’) in Wikipedia. This can help inject wider significance into your research summary, for example:

"Waterborne plastic poses a serious threat to fish , seabirds , marine reptiles , and marine mammals , as well as to boats and coasts."

The Abstract of this paper also points to a gap in the research – the lack of data on the amount of plastic waste in the Southern Hemisphere.

2.    Who were the participants?

It’s good practice to include statistical information about the study subjects or participants in your summary. This will quickly tell your reader how well the key findings are backed up. This part of the summary can combine a short narrative description of the participants (eg. age, location etc); what was ‘done’ to the participants as part of the study; what impact the study had on the participants and a brief description of the control group.

3.    What were the methods used?

How was the study carried out? What kind of materials were used to conduct the study and in what quantities or doses? Again, where possible include statistics here: number of materials; sample sizes; metrics (weight, volume, concentration etc). Here’s an example summary of a methods section from the above paper on ocean plastic:

"Net tows were conducted using neuston nets with a standard mesh size of 0.33 mm towed between 0.5 and 2 m s −1 at the sea surface for 15–60 minutes outside of the vessel’s wake to avoid downwelling of debris. Samples were preserved in 5% formalin.Microplastic was manually separated from natural debris, sorted through stacked Tyler sieves into three size classes counted individually and weighed together."

Including information about the consistency of methods or techniques used will help underline the credibility of the research.

4.    What were the key findings of the study?

Stick to the high level, headline finding of the research here. What do the quantitative results of the study reveal that was previously unknown? Again, including statistics where you can will help reinforce the findings, but remember to keep it brief. Here’s an example from the same plastic pollution paper:

"Based on the model results, the authors estimate that at least 5.25 trillion plastic particles weighing 268,940 tons are currently floating at sea."

5.    What conclusion was drawn from the research?

At this stage,  try to focus on the overall outcome of the research, but also what makes the study both significant and novel. What was uncovered as part of the research that wasn’t previously known? Do the results of the study tell us something different to what was previously known or assumed?In the plastic pollution paper, what was previously unknown was an estimate of the amount of plastic in the oceans of the Southern Hemisphere. The authors explain that their results confirm the same pattern of dispersal in the Southern Hemisphere as for the Northern Hemisphere:

"Surprisingly, the total amounts of plastics determined for the southern hemisphere oceans are within the same range as for the northern hemisphere oceans, which is unexpected given that inputs are substantially higher in the northern than in the southern hemisphere ."

6.    What kind of relevance does the research have for the wider world? (the big why)

Rounding off your summary with a powerful statement that shows how the outcome of the research has a wider significance is good practice. The ‘big why’ can often be found in the Discussion or at the end of the Conclusion of a research article, and often in the Abstract as well.Including clear, concise research summaries in your essay or dissertation can be very beneficial in strengthening your argument and demonstrating your understanding of complex research, all of which can help to improve your final grade. Using this six-point formula as a way of structuring your summary will also help you to think more critically about the research you read and make it easier for you to communicate your understanding both verbally and in writing. Try out Scholarcy’s Smart Summarizer to help draft your own research summary. ‍

• ‍ ‍ Eriksen, M., Lebreton, L., Carson, H., Thiel, M., Moore, C., Borerro, J., Galgani, F., Ryan, P. and Reisser, J., 2014. Plastic Pollution in the World's Oceans: More than 5 Trillion Plastic Pieces Weighing over 250,000 Tons Afloat at Sea. PLoS ONE , 9(12), p.e111913.

• Open access
• Published: 02 September 2024

Benefits, barriers and recommendations for youth engagement in health research: combining evidence-based and youth perspectives

• Katherine Bailey 1 , 2   na1 ,
• Brooke Allemang 3   na1 ,
• Ashley Vandermorris 4 , 5 ,
• Sarah Munce 6 , 7 , 8 ,
• Kristin Cleverley 1 , 9 , 10 ,
• Cassandra Chisholm 11 ,
• Eva Cohen 12 ,
• Cedar Davidson 13 ,
• Asil El Galad 14 ,
• Dahlia Leibovich 15 ,
• Trinity Lowthian 16 ,
• Jeanna Pillainayagam 17 ,
• Harshini Ramesh 18 ,
• Anna Samson 19 ,
• Vjura Senthilnathan 6 , 7 ,
• Paul Siska 18 ,
• Madison Snider 18 &
• Alene Toulany 2 , 4 , 5  

Research Involvement and Engagement volume  10 , Article number:  92 ( 2024 ) Cite this article

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Youth engagement refers to the collaboration between researchers and youth to produce research. Youth engagement in health research has been shown to inform effective interventions aimed at improving health outcomes. However, limited evidence has identified promising practices to meaningfully engage youth. This synthesis aims to describe youth engagement approaches, frameworks, and barriers, as well as provide both evidence-based and youth-generated recommendations for meaningful engagement.

This review occurred in two stages: 1) a narrative review of existing literature on youth engagement and 2) a Youth Advisory Council (YAC) to review and supplement findings with their perspectives, experiences, and recommendations. The terms ‘youth engagement’ and ‘health research’ were searched in Google Scholar, PubMed, Web of Science, Scopus, and PsycINFO. Articles and non-peer reviewed research works related to youth engagement in health research were included, reviewed, and summarized. The YAC met with research team members and in separate youth-only forums to complement the narrative review with their perspectives. Types of youth engagement include participation as research participants, advisors, partners, and co-investigators. Barriers to youth engagement were organized into youth- (e.g., time commitments), researcher- (e.g., attitudes towards youth engagement), organizational- (e.g., inadequate infrastructure to support youth engagement), and system-level (e.g., systemic discrimination and exclusion from research). To enhance youth engagement, recommendations focus on preparing and supporting youth by offering flexible communication approaches, mentorship opportunities, diverse and inclusive recruitment, and ensuring youth understand the commitment and benefits involved.

Conclusions

To harness the potential of youth engagement, researchers need to establish an inclusive and enabling environment that fosters collaboration, trust, and valuable contributions from youth. Future research endeavors should prioritize investigating the dynamics of power-sharing between researchers and youth, assessing the impact of youth engagement on young participants, and youth-specific evaluation frameworks.

Plain English summary

Engaging and partnering with youth in research related to healthcare is important, but often not done well. As researchers, we recognize that youth perspectives are needed to make sure we are asking the right questions, using appropriate research methods, and interpreting the results correctly. We searched the literature to identify challenges researchers have faced engaging youth in health research, as well as strategies to partner with youth in a meaningful way. We worked closely with 11 youth from across Canada with experience in healthcare, who formed a Youth Advisory Council. The youth advisors reviewed the literature we found and discussed how it fit with their own experiences and perspectives through group meetings with the research team. Youth advisors divided into four groups to co-author parts of this paper, including identifying the importance, benefits, and challenges of engaging in research and providing reflections on their positive and negative previous experiences as youth advisors. This paper provides an overview of recommendations for researchers to engage with youth in a meaningful way, including how they communicate and meet with youth, recognize their contributions, and implement feedback to improve the experiences of youth partners.

Peer Review reports

Introduction

Patient engagement in health research is essential to improving the relevance, processes, and impact of their findings [ 1 , 2 , 3 ]. Defined as the collaboration between researchers and those with lived experience in planning and conducting research, interpreting findings, and informing knowledge translation activities [ 1 ], patient engagement in research has been shown to produce and disseminate findings that are more applicable and comprehensible for patients, their families, and the greater community [ 3 , 4 , 5 , 6 , 7 ]. Youth engagement refers specifically to the involvement of youth populations in the research process, with youth often being defined as young people between the ages of 15 to 24 years old [ 8 , 9 , 10 , 11 ]. Youth, particularly those with chronic physical health (e.g., cystic fibrosis, congenital heart disease, diabetes), mental health (e.g., anxiety, depression), and neurodevelopmental conditions (e.g., cerebral palsy), face unique challenges in engaging with the healthcare system compared to adult populations. These include navigating healthcare transitions, developing relationships with multiple care providers, learning to advocate for themselves, and assuming greater responsibility for their healthcare as they grow and mature [ 12 , 13 ]. Existing research has shown that engaging youth in research leads to more effective and impactful interventions, policies, and healthcare services aimed at supporting health outcomes of young people, informed by the priorities and experiences of youth themselves [ 14 , 15 , 16 , 17 , 18 , 19 ]. Several nationally representative child health organizations and leaders have identified youth engagement as a priority area in youth health, highlighting the urgent imperative to include their voices in health research and public policy decisions [ 20 ]. Despite the evidence suggesting that youth are eager and capable of being engaged, there is limited evidence on the unique considerations needed to meaningfully involve youth in health research given their distinct developmental stage [ 8 , 10 , 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. These considerations include an emphasis on peer connections, mentorship, flexibility given competing priorities, and the use of technology to allow for broad participation [ 30 , 31 ]. In collaboration with a Youth Advisory Council (YAC), this review aims to:

Outline key types of youth engagement identified in the literature (Aim 1);

Review existing youth engagement frameworks identified in the literature (Aim 2);

Explore barriers to youth engagement identified in the literature and from YAC member perspectives (Aim 3);

Summarize recommendations for engaging youth in research identified in the literature and from YAC member perspectives (Aim 4).

The YAC identified a secondary aim, which was to:

Describe the benefits and impact of youth engagement from YAC member perspectives (Aim 5).

This project was comprised of two phases. First, the research team conducted a narrative review of the literature. Next, a project-specific YAC was established to review the literature findings and integrate the essential insights and perspectives of youth into the project. The methods pertaining to each phase are elaborated upon below. Our Research Ethics Board did not require a formal review of this project as it did not involve research participants.

Phase 1: Narrative Review

A narrative review was conducted to explore existing research on engaging youth in health research. Narrative review methodology is often employed to broadly describe the current state of the literature and provide insights for future research [ 32 ]. This review method was chosen to establish a broad understanding of the youth engagement literature and provide recommendations for researchers seeking to gain an overview of strategies for meaningful engagement. Narrative reviews also provide flexibility in terms of methodology (often based on the subjectivity of the research team) [ 33 ] and are less formal than other types of knowledge syntheses (e.g., systematic reviews) [ 34 , 35 ]. This review methodology allowed the research team to prioritize and integrate the perspectives of youth into the synthesis of information. Aims 1 to 4 were addressed in Phase 1. Aim 5 was not initially identified as an objective by the research team, and was therefore not included in the review of the literature. Upon establishment of the YAC, youth advisors deemed personal reflections on the benefits and impact of youth engagement from their perspectives critical to the manuscript.

Inclusion and Exclusion Criteria

Articles included in this narrative review met the following primary inclusion criteria: 1) published in English language, 2) published prior to April 2023, 3) focused on youth engagement in health research, and 4) described key types of youth engagement strategies (Aim 1), youth engagement frameworks (Aim 2), barriers to youth engagement (Aim 3), or recommendations for youth engagement (Aim 4). For the purposes of this review, ‘youth’ was defined as individuals between the ages of 15 to 24 years old, which is consistent with the definition provided by the United Nations [ 11 ], and ‘youth engagement’ was defined as the involvement of young people within this age range in research processes. This population was chosen for the focus of this review as the needs of youth are often distinct from children and adults due to their unique developmental stage (e.g., navigating healthcare transitions, increasing autonomy, etc.) [ 12 , 13 ]. Articles from any geographic location were included. Grey literature, websites, and non-peer reviewed research works (e.g., conference abstracts, theses) were also included using the same criteria as above.

Search Strategy and Synthesis

The search terms ‘youth engagement’ and ‘health research’ were searched in Google Scholar, PubMed, Web of Science, Scopus, and PsycInfo. Articles were hand-searched by members of the research team and selected according to the inclusion criteria above. Reference lists of relevant articles were also scanned. While other knowledge syntheses (e.g., systematic or scoping reviews) review all works identified by the literature search, narrative reviews do not aim to be inclusive of all literature available on a given topic [ 36 ]. As such, our review of the literature was concluded once we felt that sufficiency was achieved, which was characterized by reviewing works that yielded recurrent concepts. Additionally, the literature was reviewed iteratively following feedback from youth advisors who critically reviewed the narrative review manuscript. Some aspects of the manuscript were deemed critical to expand upon by youth advisors, and literature was reviewed again accordingly.

Relevant peer-reviewed and non-peer reviewed literature was organized and summarized descriptively according to study aims 1 to 4. Barriers to youth engagement were organized into individual-, organizational-, and systems-level. Recommendations for youth engagement were organized into common overarching themes.

Phase 2: Collaboration with Youth Advisory Council

The research team identified the criticality of collaborating with youth themselves in the review, formatting, and presentation of findings from the narrative review. As the review was being conducted and written, the research team began recruiting a group of youth advisors to contribute their perspectives, experiences, and recommendations for the manuscript. The development and procedural aspects of the YAC as they relate to the review are described below and in Fig.  1 . The operation of the YAC was guided by the McCain Model of Youth Engagement [ 31 ] and the Canadian Institutes of Health Research’s (CIHR) Patient Engagement Framework [ 1 ]. These frameworks, which prioritize reciprocity, respect, mutual learning, flexibility, and mentorship, supported the use of youth-driven and adaptable engagement strategies throughout the project [ 1 , 31 ]. Specifically, the research team employed engagement practices including co-building of a terms of reference document, inviting YAC members to co-chair meetings to foster mutual learning, and offering YAC members a menu of options for contribution, that aligned with the principles outlined in these models [ 1 , 31 ]. Aims 3 (i.e., identifying barriers to youth engagement) and 4 (i.e., summarizing recommendations for youth engagement) were expanded upon by the YAC in Phase 2. As described above, Aim 5 (i.e., benefits and impact of engagement on youth themselves) was deemed crucial by members of the YAC and was exclusively addressed in Phase 2 of this project. It should be noted that while the YAC specifically contributed reflections to Aims 3–5, each member critically reviewed the manuscript and offered feedback as co-authors.

Recruitment of Youth Advisory Council Members

Recruitment for the YAC began in June 2023 through distribution of a recruitment poster via professional contacts (e.g., researchers conducting youth-engaged research, youth advisory council facilitators), social media pages, and email lists (e.g., patient-oriented research listservs, youth advisory council lists). Eligible youth advisors were Canadian youth between the ages of 15–24 years with an expressed interest in youth engagement in health research. Youth applicants completed a Google Form to describe their motivations to become involved and past experience, if applicable. To ensure a diverse range of perspectives, we considered age, sex/gender, race and ethnicity, geographic location, and a range of previous experiences with research (from limited to extensive) in our recruitment process. The research team received interest from 55 individuals, of which 17 were invited to complete a 30-min virtual interview co-led by a researcher and a youth research partner. Eleven youth were selected to join the YAC, and all accepted the team’s invitation to participate. The youth invited to compose the YAC predominantly had previous experience with health care, including as a patient, advocate, youth advisor, research participant, or research assistant. Having and/or disclosing a diagnosis of a chronic health condition was not a criterion for participation in the YAC. A collective discussion was held with youth advisors and it was determined that members preferred not to share their demographic information, though there was representation of members with varying ages, ethnicities, years of experience with engagement, and from different provinces. The research team consisted of female-identified researchers, clinicians, and trainees across interdisciplinary professional backgrounds (e.g., medicine, nursing, social work) with experience engaging youth in research and/or clinical care. As many team members do not have previous youth lived experiences in research and/or clinical care, we were committed to closely collaborating and amplifying youth voices in our research, recognizing that our work, interpretations, and applications to the broader community were limited by our non-experiential understanding of youth engagement in research. The composition of the research team and YAC allowed for critical reflection on the roles of positionality, intersectionality, power, and privilege within youth engagement. The team engaged in reflexive discussions about the importance of prioritizing equity and addressing discrimination in engagement, especially for youth with marginalized identities.

Scheduling and Meetings

In July 2023, a Doodle Poll link was sent out to all youth advisors to find three meeting times that could accommodate the majority of the youth advisors and research team. Subsequently, Microsoft Teams invites were sent via email, and meetings were recorded and transcribed for notetaking purposes.

Prior to each meeting, a meeting agenda and documents were sent for review. Meetings lasted between 1.5 and 2 h and were recorded for those who could not attend. Both the recording and the minutes were collated following each meeting and made available to all youth advisors. Prior to the first meeting, a draft terms of reference document (ToR) was distributed to all youth advisors for review. The ToR contained the purpose and expectations of youth contributing to the project. A preliminary draft of the narrative review was provided to each youth advisor for their consideration both in advance of and during the meetings. Throughout the meetings, a range of communication methods, including Jamboards, chat messaging, and online verbal discussions, were employed to enable youth to exchange ideas and actively facilitate discussions.

During the initial meeting, youth advisors were provided with guidelines aimed at creating a secure environment using a digital interactive whiteboard on Google Jamboard. To maintain confidentiality and facilitate continuous improvement, the youth advisors proposed and subsequently implemented an anonymous feedback form, accessible for youth to complete at their discretion. Subsequently, the youth advisors engaged in a collaborative ideation session to conceptualize their contributions to the synthesis. It was decided that a Slack channel would serve as the primary platform for communication among the youth advisors.

In the second meeting, the council deliberated on the ToR initially formulated by the research team, with the ToR subsequently revised to incorporate the feedback and insights provided by the youth advisors. Additions to the ToR from YAC members included greater options for compensation, strategies for addressing microaggressions, more clarity regarding YAC tasks, roles, and responsibilities, and rationale for selecting 11 advisors for the group. Following this, the group engaged in a comprehensive discussion centered on their reflections concerning the draft of the narrative review. This dialogue highlighted the identified gaps and obstacles associated with involving youth in research from YAC members’ perspectives, proposed recommendations for future research endeavors, and stressed the importance of integrating youth voices into the research process.

In the third meeting, the focus shifted towards the establishment of more focused working groups. These smaller working groups were structured to address specific aspects, including 1) the rationale behind the research (the “why”), 2) reflections on past experiences with youth engagement, 3) methodologies for engaging youth in the context of this review, and 4) formulating recommendations for future research endeavors. Youth advisors were invited to complete a form to rank their areas of interest in these four areas. Based on their ranked responses, working groups were formed and considered the alignment between youth advisor’s preferred method of contribution (e.g., developing visuals, writing a personal reflection, contributing to a table) and the specific topic of the working group.

During the fourth meeting, which was co-chaired by a research team member and a youth advisor (TL) who volunteered for this role, youth advisors and members of the research team reviewed written materials from each working group, discussed each section of the paper, and reached consensus on how the sections would be presented within the article. It was determined that youth advisor work would be combined with the existing narrative review and showcased using textboxes, figures, and tables.

Independent Working Groups

All youth advisors worked in four designated working groups over a 3-week period. Youth advisors communicated via Slack channels, email or personal messaging, with the research team available for support and guidance, as needed. Guidelines for authorship, methods of contributing to each section of the paper (e.g., brainstorming, making point form notes, developing figures), and suggestions on length/format were discussed at YAC meetings. Youth advisors were also provided with a series of resources on a collaborative drive to support their contributions to the review, including a youth-friendly guide to academic writing and examples of reports/journal articles co-authored by youth. All groups worked independently and provided finalized drafts to the research team prior to the fourth meeting.

Compensation

All youth advisors were compensated $25 per hour at the end of their involvement. All youth advisors tracked their hours with a maximum of 20 h. Youth advisors were able to track meetings, self-directed work, and all time dedicated to the project outside of meetings.

Methodology used to engage the Youth Advisory Council in the co-development of this article. Figure developed by the Youth Advisory Council

A total of 65 articles were included, of which 56 were peer-reviewed and 9 were non-peer reviewed. Of the peer-reviewed articles, 14 were qualitative studies, 12 case studies, 7 mixed-methods, 6 commentaries, 2 curriculum development studies, and 2 randomized controlled trials. Additionally, 13 syntheses were included ( n  = 7 unstructured literature reviews, n  = 3 scoping reviews, n  = 2 systematic reviews, n  = 1 scoping review protocol). Of the non-peer reviewed studies, 4 were websites and 5 were reports. A table is available in Appendix A displaying included article citations, categorization of peer-reviewed versus non-peer reviewed works, and study methods used.

In this section of the article, results pertaining to each of the five aims are presented. Aims 1 to 4 were addressed in Phase 1 of this project to outline types, frameworks, and barriers to youth engagement and summarize the literature’s recommendations on how to meaningfully engage youth. Aims 3 and 4 were addressed in collaboration with youth advisors in Phase 2 to highlight the benefits and barriers of youth engagement and recommendations from the perspectives of the youth advisors on meaningful youth engagement. Aim 5 was identified as a priority for youth advisors and their reflections are provided on the benefits and impact of engagement on youth themselves.

Aim 1: Key Types of Youth Engagement

There are several approaches to youth engagement in health research, which are based on the aim(s) of a given project, resources available, and preferences of youth themselves (shown in Table  1 ) [ 37 ]. Youth may be involved as research participants , such as completing a survey or participating in a focus group [ 24 , 31 , 38 , 39 , 40 ]. Youth may also take on advisory or consultation roles , where they provide input on the research scope, recruitment strategies, and methods, as well as reviews analyses, results, and/or manuscripts, from which the researcher may decide if or how to implement their suggestions (e.g., advisory councils) [ 24 , 38 , 39 , 40 , 41 ]. Youth may assume co-production roles , which actively involves youth in the development of research objectives and design, funding proposals, study informational materials, recruitment of participants, data collection instruments, co-facilitating focus groups/interviews, analysis of data, presentations, manuscripts, and knowledge translation activities [ 10 , 24 , 41 ]. This may also be referred to as partnership , which involves active collaboration of youth with researchers to support and/or lead aspects of the project (e.g., collaborate on research methodology, lead certain research activities) [ 24 , 31 , 38 , 39 , 40 ]. Finally, youth-led research refers to projects that are entirely led by youth, with or without the support of an adult researcher [ 24 , 31 , 38 , 39 , 40 ].

A recent systematic review identified youth engagement practices in mental health-specific research, highlighting the most common youth engagement types were advisory roles, where youth were often involved in providing feedback on the research topic, analysis of qualitative data, and dissemination of findings, with less emphasis placed on co-production methods [ 10 ]. Authors identified one study which utilized a youth-led participatory action research approach in the mental health research setting, which is a power-equalizing methodology involving collaborative decision-making and viewing youth as experts based on their own lived experience [ 44 , 46 , 47 , 48 ].

Aim 2: Frameworks for Youth Engagement

A significant body of literature has proposed various frameworks for supporting patient engagement in research, with research teams more recently developing frameworks specific to youth engagement [ 49 ]. For example, the Youth Engagement in Research Framework , designed by youth and researchers at the University of Manitoba, identified seven strategies to create a culturally-inclusive research environment for youth to meaningfully contribute to the research process [ 50 ]. Strategies included 1) understanding motivations of youth to engage in research, 2) sharing intentions to implement research findings, 3) supporting diverse youth identities in engagement, 4) actively addressing the barriers to youth engagement, 5) reinforcing that engaging in research is a choice, 6) developing trusting relationships through listening and acknowledging contributions, and 7) respecting different forms of knowledge creation, acquisition, and dissemination [ 51 ].

Youth engagement has also been achieved through health research communities of practice , a framework aimed at promoting a space for youth to develop identity, build capacity for youth to develop research, communication, and advocacy skills, lead projects, and develop relationships with the research team [ 52 , 53 , 54 ]. A Canadian research team developed IN•GAUGE®, a health research community of practice which aims to promote collaboration between youth, families, researchers, and policy makers and support the development of strategies to improve child and family health [ 51 , 52 ]. This program uses Youth and Family Advisory Councils, a group of youth and family members who contribute to the direction of the project and provide input on research methods based on their own lived experiences [ 51 ]. This community of practice has built a robust network of youth and family researchers, which helps alleviate some challenges associated with finding youth to support a project.

Researchers at the Centre for Addiction and Mental Health (CAMH) in Toronto, Ontario, Canada have developed the McCain Model for Youth Engagement, which is specific to mental health populations [ 55 ]. This model is based on flexibility (i.e., the youth and research team work together to co-design deliverables/timelines and develop skills that are relevant to the youth’s goals), mentorship (i.e., in the development of research skills, incorporating youth strengths into research design), authentic decision-making (i.e., avoiding ‘tokenism’, carefully considering and implementing youth feedback), and reciprocal learning (i.e., both youth and researchers are ‘teachers’ and ‘learners’). Based on the implementation of the McCain Model, researchers propose that youth engagement should be established when research projects are in the early planning stages, reflect on organizational-level barriers to youth engagement and plan policies and practices around them, and train researchers on the value of engaging youth [ 55 ].

A recent commentary made key recommendations for youth engagement in the context of the COVID-19 pandemic [ 30 ]. First, authors propose adapting youth engagement strategies to facilitate rapid decision-making, such as utilizing connections with pre-existing youth advisory councils, providing additional compensation, and offering opportunities for online participation. Additionally, they suggest leveraging virtual platforms for youth engagement methods, while ensuring that youth with disabilities or chronic health conditions are offered appropriate accommodations. Finally, subsidies or shared tablets or computers may be offered to youth researchers to ensure virtual platforms are accessible and reduce technological barriers [ 30 ].

Aim 3: Barriers to Engaging Youth in Research

A series of barriers for engaging youth in health research have been identified in the literature through a narrative review. These barriers are grouped into individual, organizational, and systemic factors and are presented below. In Table  2 , a summary of these barriers, as outlined in the published literature is presented. Youth advisors were invited to review this list and provide their own expansions, reactions, and additions based on their knowledge and experiences. A key limitation in the exploration of barriers related to youth engagement is that much of the existing literature does not specify what level of youth enagagement was being employed.

Individual-Level Barriers: Youth-Specific

Many youth may be discouraged from engaging in research due to their own negative lived experiences with the healthcare system. For example, youth may be distrustful of adult clinicians and researchers, particularly those who may have had traumatic medical experiences (e.g., lengthy hospital/intensive care unit admissions, surgeries, invasive treatments), complex and chronic healthcare conditions, or marginalized identities [ 56 ]. While understanding these perspectives and experiences is crucial to improve health service structures and delivery, they may not be captured without carefully considering and applying appropriate youth engagement methods. Similarly, those with negative previous experiences with youth engagement may feel tokenized or patronized, particularly if they did not feel authentically valued or listened to by the research team [ 57 , 59 ].

Youth characteristics may also result in exclusion from youth engagement and/or exacerbate existing barriers to partnering, particularly the presence of physical disabilities, visual/hearing impairments, intellectual disabilities, neurological conditions, mental health conditions, and/or socioeconomic factors [ 69 , 70 , 78 ]. Youth with disabilities may experience mobility impairments preventing them from easily attending research team meetings, may require additional time and supports to complete research tasks, or utilize assistive devices (e.g., communication tools) [ 69 , 70 , 78 ]. Low literacy levels and/or language barriers may also make engagement inaccessible without appropriate accommodations [ 78 ].

Furthermore, youth priorities may impact willingness to engage in research. Specifically, youth may not feel valued without formal recognition for their contributions, such as financial compensation, volunteer hours, authorship on manuscripts, or opportunities to present research at academic meetings [ 59 ]. They may also not want youth engagement opportunities to infringe on their leisure or personal time, or may be hesitant to engage in projects with long time commitments [ 61 ]. A study highlighting experiences with engaging youth with Bipolar Disorder as peer researchers identified that attrition was also affected by illness relapse, as well as difficulties balancing the responsibilities of the research project with post-secondary education and employment commitments [ 44 ].

Individual-Level Barriers: Adult Researcher-Specific

Research team members may also hold specific beliefs or attitudes towards youth engagement. For example, some researchers may feel anxious about losing control over the research process, may not see youth as experts themselves, or hold biases about the value of youth perspectives [ 24 ]. Researchers may also perceive youth engagement as an added layer of complexity, fear that engagement may impact the scientific rigor of the research design, or be concerned that youth engagement may negatively impact the research quality [ 24 , 26 , 27 , 79 , 80 , 81 ]. Further, some studies have highlighted that researchers do not feel equipped with the skills or knowledge to engage and communicate with youth, or to design studies using youth engagement principles [ 24 , 62 ]. Finally, researchers may experience challenges navigating differing priorities between youth partners and members of the research team. For example, researchers may prioritize more traditional markers of research success, including peer-reviewed manuscripts and grant proposals which often require rapid turnaround times, and be concerned that youth engagement may add to the timeline of a project [ 24 , 62 ].

Organizational-Level Barriers

As youth engagement has emerged as a best practice recently, many academic institutions do not yet have the infrastructure or resources to support engagement opportunities [ 24 ]. While examples of capacity-building programs for youth co-researchers exist in the participatory action research literature [ 82 ], there is a need for further development of training resources to support youth who are engaging in health research [ 83 ]. Formal education on youth engagement is often not included in research training programs, despite many granting agencies recently making changes to require and/or promote patient engagement considerations in funding applications [ 1 , 62 ]. Further, many organizations have not adopted policies to outline best practices for youth engagement, and academic workplace culture also may not yet value youth engagement, resulting in limited willingness to adapt research practices [ 24 , 62 ]. These factors may exacerbate existing difficulties with securing sufficient time and resources to support relationship-building between youth partners and adult members of the research team, which is a commonly cited challenge with youth engagement [ 26 , 27 , 84 , 85 ].

System-Level Barriers

Youth with complex health conditions, such as those with developmental disabilities, often experience stigma and exclusion from clinical research [ 69 , 70 , 71 , 72 ]. Specifically, research teams may inaccurately perceive youth with chronic medical conditions as ‘vulnerable’ or ‘fragile’, thus deeming them unable or incapable to contribute meaningfully or complete study-related tasks [ 24 , 70 , 72 , 73 , 86 , 87 ]. Youth with marginalized identities, including Black, Indigenous, and 2SLGBTQIA+ youth, often experience discrimination within the healthcare system, with several studies suggesting mistrust of research institutions, researchers, and healthcare systems stemming from community experiences of mistreatment in research as the most significant barrier to participating in clinical research [ 65 , 66 , 67 , 68 ]. Furthermore, youth from racial and ethnic minorities often receive less information and attention from healthcare providers compared to white youth, potentially limiting awareness of the opportunities and/or value in contributing to health services research [ 68 , 88 ]. Notably, limited literature has considered the impact of other social and structural determinants of health on youth engagement, including income, housing, and geographic location.

Youth may also be apprehensive to share their perspectives, critiques, or suggestions for improvement with adult researchers due to inherent power imbalances [ 74 , 75 , 76 , 77 ]. Given the differences in power between adults and youth, as well as between patients and clinicians/researchers, youth engagement may involve researchers dominating the conversation, thus preventing equal contribution and collaboration. Ultimately, these dynamics have the potential to produce harmful cultures or practices for youth entering research environments, especially among youth from marginalized groups. These barriers and possible outcomes resulting from these power imbalances are elaborated on in Table  2 .

Finally, researchers themselves may face barriers as many major funding agencies have yet to prioritize or incorporate youth engagement in their strategy, resulting in limited funding opportunities to support this type of engagement work or a lack of dedicated time and resources for researchers to build relationships with youth [ 73 ]. Of note, the CIHR has developed a Strategy for Patient-Oriented Research, and requires grant proposals in certain funding streams to utilize patient engagement methods [ 1 ]. However, this is not yet universally implemented across funding agencies and does not guide engagement with youth specifically. Additionally, funding agencies often have strict eligibility and assessment criteria, including level of education and evidence of prior research and scholarly outputs, which may inherently exclude youth researchers from participating in funding applications. Finally, granting agencies have funding deadlines which may not accommodate the flexibility needed to build meaningful relationships with youth partners.

Further, while some academic journals have incorporated mandatory reporting on stakeholder and patient involvement in the research design, this is not a standard of practice, and many of these journals are engagement-focused [ 55 , 62 , 89 ]. Finally, there is a lack of consensus around how to report on engagement practice and outcomes of engagement across studies, which contributes to inconsistencies in what constitutes meaningful and effective engagement. While tools are emerging to enhance transparency in reporting engagement, including the Guidance for Reporting Involvement of Patients and the Public (GRIPP), no tools exist for youth engagement specifically [ 90 , 91 ]. Barriers to engaging youth in health research from both the literature and the perspectives of the youth advisors involved in this project are summarized in Table  2 .

Aim 4: Facilitators and Recommendations for Youth Engagement

Many studies have highlighted recommendations to improve the implementation of youth engagement across research contexts. Canada’s Youth Policy was created in 2020 to develop a greater understanding of the experiences and perspectives of youth living in Canada [ 92 ]. As part of this, funding opportunities through Canada’s major funding body for health research (CIHR) have begun to focus on providing meaningful opportunities to empower youth in research such as the Healthy Youth Initiative [ 93 ]. Our study findings are in line with these newly implemented policies as they lay the foundation for researchers on how to meaningfully engage youth in health research. In the following section, current strategies, strengths, and facilitators in the health sector that can support youth engagement are outlined, along with areas for improvement. As in Table  2 , these recommendations were reviewed and expanded upon by the YAC in Table  3 .

Engaging Youth from Structurally Marginalized Populations

Engagement of youth with intersecting marginalized identities, such as Black, Indigenous, or 2SLGBTQIA+ youth, and youth with disabilities, language/communication barriers, immigrants and refugees, experiencing homelessness, or living in foster care, may involve several unique considerations [ 31 ]. Research teams should engage both youth and researchers from communities with lived experience to provide insights and support engagement strategies [ 31 ]. It is also important to recognize that engaging youth from Indigenous communities may involve a unique approach. Practices adopted by Indigenous-led organizations may exist that focus on youth empowerment that are specific to their communities. For example, the ‘Indigenous Youth Voices Report ’ produced by The Yellowhead Institute at Toronto Metropolitan University in collaboration with the First Nations Child and Family Caring Society outlined requirements for engaging and conducting research with and by Indigenous youth, which included themes such as ensuring research is accessible, uplifting Indigenous youth to co-create research, relationship-building and reciprocity, and using holistic approaches to ensure Two-Spirit, 2SLGBTQ+ youth, and Elders are meaningfully included in research approaches [ 107 ]. Further, a recent study showed evidence supporting the use of web-conferencing technology to engage Aboriginal and Torres Strait Islander in Australia through co-facilitation of an Online Yarning Circle, an Indigenous methodology that involves sharing, listening, interpreting, and understanding information in an informal setting [ 108 , 109 ].

Additionally, teams should partner with researchers who have experience working with youth from these populations. Women’s College Hospital in Toronto, Ontario, Canada has recently developed an innovative and inclusive patient engagement model, called Equity-Mobilizing Partnerships in Community (EMPaCT) , designed to highlight the priorities and needs of diverse communities informed by the perspectives of individuals with lived experience [ 110 , 111 ]. Research teams can consult this service to identify approaches to advance equity and social justice within their projects [ 110 , 111 ]. Researchers may also consider using the ‘Valuing All Voices Framework’ , which is a trauma-informed, intersectional framework that guides researchers on how to embed a social justice and health equity lens into patient engagement, with the goal of enhancing inclusivity within health research [ 112 ]. This framework is based on four core concepts, including trust (e.g., focusing on resilience/strength rather than challenges, allowing time to build relationships), self-awareness (e.g., practicing honesty, creating safe spaces), empathy (e.g., allowing the space to share stories), and relationship building (e.g., share experiences, promote ongoing communication, show awareness and sensitivity towards cultural differences) [ 112 ].

All research team members engaged in this work should be offered training on best practices for communicating and engaging with specific populations [ 31 ]. Appropriate accommodations, such as communication tools, accessibility aids, and financial support for involvement, should be offered consistently to optimize engagement of youth with diverse experiences and perspectives [ 78 ]. While not specific to youth engagement, the National Health Service in the United Kingdom has a guidance document which outlines considerations to increase diversity in research participation, including a focus on building trust, conducting research in places familiar to participants, developing accessible recruitment materials, and incorporating peer-led activities [ 113 ]. Finally, researchers should adhere to existing ethical standards for specific marginalized communities, such as the CIHR guidelines for conducting research involving Indigenous people [ 114 ].

Evaluation of Youth Engagement

Robust evaluation of youth engagement strategies is a core component of youth involvement in research and should be used to enhance implementation of principles in research, provide feedback, and ensure researchers are held accountable in upholding best practices [ 104 , 115 ]. While there are no empirically-tested tools for the evaluation of youth engagement in research, qualitative, quantitative, and mixed methods may be used, including the Youth Engagement Guidebook developed through the CAMH [ 31 ], the Public and Patient Engagement Evaluation Tool (PPEET) [ 116 ], and the Patient Engagement in Research Scale (PEIRS) [ 117 ]. These instruments are co-designed by patients and are used to evaluate the quality of engagement strategies from the perspective of patient partners themselves [ 117 ]. It should be noted, however, that empirically-tested tools for measuring youth-adult partnerships more broadly do exist [ 118 , 119 , 120 ] and could likely contribute useful information to the measurement of youth engagement in research, specifically. It is also recommended to evaluate the impact of youth engagement from the researchers’ perspectives, which may include reflecting on how valuable the team considered youth partners to be, the extent of youth involvement, and the impact of youth engagement on project outcomes [ 31 ]. Alberta Health Services has developed a resource tool kit containing survey instruments to assist research teams with routine evaluation of their collaboration skills [ 121 ]. Research teams should carefully evaluate and iteratively modify their engagement strategies to ensure youth are meaningfully involved.

Capacity Development

Several independent training programs exist to educate researchers, community stakeholders, patients, youth, and caregivers on engaging patients in health research, including the Patient and Community Engagement in Research (PaCER) program [ 122 ], McMaster University Family Engagement in Research (FER) course [ 123 ], Patient-Oriented Research Curriculum in Child Health (PORCCH) [ 124 ], and Partners in Research (PiR) [ 125 ]. Further, a recent study was conducted to develop simulations in collaboration with interdisciplinary stakeholders to train researchers on how to engage youth in childhood disability research [ 126 ]. These simulation videos focused on aspects of the research process where challenges may arise based on previous experiences of youth and family advisors [ 126 ].

Aim 5: Youth Advisor Reflections on the Impact of Youth Engagement

While describing the evidence-based benefits of youth engagement in research within the literature was beyond the initial scope of the narrative review, youth advisors deemed it critical to present their experiences regarding their motivations for becoming involved in research and the impact of research opportunities on youth. Two youth advisors reflected on the benefits of youth engagement in research from their own experiences and collectively developed the content displayed in Table 4 in a small working group. The same two advisors considered their prior involvement in research and outlined the impact of engagement on their lives in Table 5 . They were invited to share any aspects of their experiences they felt were important to communicate with a broad audience, and selected the format and method of organization of their reflections. These reflections offer unique and valuable insights into the importance of creating opportunities for meaningful and conscientious youth engagement in research using youths’ own language.

Conclusions, Limitations & Future Directions

This narrative review provides an overview of the current literature in youth engagement in health research in combination with the perspectives of youth advisors themselves. The research team and YAC collectively identified key types and frameworks for youth engagement, synthesized several barriers and recommendations for implementing youth engagement, and provided critical reflections on the impact and benefits of youth engagement in the youth voice. While many evidence-based frameworks exist to incorporate and evaluate patient engagement in research, gaps remain in the identification of the best practices for youth engagement specifically [ 49 ]. Much of the available youth engagement literature has focused on involving youth in mental health research, with limited evidence regarding best practices to engage youth with chronic physical health and neurodevelopmental conditions [ 10 , 21 , 24 ]. Further, a paucity of evidence has highlighted the barriers and best practices to engaging youth with low income, those experiencing homelessness, and rural/remote communities in health research.

Limitations

This article employed narrative review methodology to provide an overview of existing research in youth engagement in research. A more structured and systematic review and critical appraisal of included literature by multiple independent reviewers was not within the scope of this paper, which may have excluded relevant literature. The information presented in this article may serve as a foundation for a systematic review of the literature on this topic, which our research team endeavours to complete in the future. Additionally, the search was limited to articles published in English, which may have excluded relevant literature, including potential barriers or recommendations specific to non-English speaking youth. Future research should consider a fulsome exploration of youth engagement strategies, barriers, and recommendations published in languages other than English. Demographic information of youth advisors was not collected or presented as part of this article due to YAC member preference. In addition, a previous diagnosis of a chronic health condition and/or lived experience as a patient was not a criterion for inclusion in the YAC. Rather, youth advisors had a diverse set of experiences with health care (e.g., as patients, advocates, previous youth advisors, research assistants, and/or research participants). Furthermore, youth members were self-selected by the research team, and not recruited from established youth organizations with elected representatives. As such, we are unable to determine whether the youth composing the YAC are representative of the target population. Future studies could examine how demographic characteristics and/or prior experiences with engagement influence youths’ perceptions of barriers, enablers, and recommendations for youth engagement.

Future Directions

To address many of the barriers identified in this review, further work is needed at the organizational- and systems-levels to build policies and programs that support youth engagement in research. As such, youth advisors developed a call to action for researchers and their hopes for the future of youth engagement in research, available in Table 6 . Finally, robust studies are needed to develop and validate youth engagement evaluation tools [ 31 ].

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Youth Advisory Council

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Acknowledgements

The authors would like to acknowledge the Edwin S.H. Leong Centre for Healthy Children, The Hospital for Sick Children for supporting this work through the Leong Centre Studentship Award.

This work is supported by the Leong Centre Studentship Award received by Katherine Bailey and Dr. Alene Toulany. The other authors received no additional funding.

Author information

Katherine Bailey and Brooke Allemang contributed equally as co-primary authors.

Authors and Affiliations

Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Katherine Bailey & Kristin Cleverley

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

Katherine Bailey & Alene Toulany

Child Health Evaluative Sciences, SickKids Research Institute, Toronto, ON, Canada

Brooke Allemang

Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Ashley Vandermorris & Alene Toulany

Division of Adolescent Medicine, The Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada

KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada

Sarah Munce & Vjura Senthilnathan

Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada

Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada

Sarah Munce

Lawrence S. Bloomberg School of Nursing, University of Toronto, Toronto, ON, Canada

Kristin Cleverley

Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada

Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada

Cassandra Chisholm

Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada

Neurosciences and Mental Health, SickKids Research Institute, Toronto, ON, Canada

Cedar Davidson

Michael De Groote School of Medicine, McMaster University, Hamilton, ON, Canada

Asil El Galad

McGill University, Montreal, QC, Canada

Dahlia Leibovich

Department of Health Sciences, University of Ottawa, Ottawa, ON, Canada

Trinity Lowthian

McMaster University, Hamilton, ON, Canada

Jeanna Pillainayagam

Collaborator, Toronto, ON, Canada

Harshini Ramesh, Paul Siska & Madison Snider

Patient Partner, Canadian Arthritis Patient Alliance, Toronto, ON, Canada

Anna Samson

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KB synthesized the literature, drafted the initial manuscript, and approved the final manuscript as submitted. BA provided youth engagement expertise, facilitated youth advisor meetings, revised the manuscript, and approved the final manuscript as submitted. CC, EC, CD, AEG, DL, TL, JP, HR, AS, PS, MS contributed their perspectives and expertise as part of the Youth Advisory Council, drafted components of the manuscript, revised the manuscript, and approved the final manuscript as submitted. BA, AV, SM, KC, VS, and AT conceptualized the design and methods of this study, revised the manuscript, and approved the final manuscript as submitted.

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Correspondence to Alene Toulany .

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Bailey, K., Allemang, B., Vandermorris, A. et al. Benefits, barriers and recommendations for youth engagement in health research: combining evidence-based and youth perspectives. Res Involv Engagem 10 , 92 (2024). https://doi.org/10.1186/s40900-024-00607-w

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DOI : https://doi.org/10.1186/s40900-024-00607-w

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Floating Doubt: The Risks of FSRUs in Expanding Methane Gas

Rachel Eunbi Shin

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• [SFOC] Floating Doubt - The Risks of FSRUs in Expanding Methane Gas.pdf

Executive summary

The global floating storage regasification unit (FSRU) market is booming, with European countries leading the deployment of these medium-sized LNG import terminals. Originally utilized in Asia and other developing regions, FSRUs are now being promoted as a quick solution to Europe's energy security concerns. In just two years from 2021, Europe deployed 9 FSRUs in response to Russia's reduction of piped-gas supply.

This report provides a comprehensive analysis of FSRUs, examining their growing trend, key industry players, associated risks, and illustrative case studies from Germany, Croatia, Bangladesh, Indonesia, Pakistan and the Philippines. It reveals that the FSRU trend overlooks critical factors, including their failure to effectively address energy security, their long-term climate impact, and the substantial environmental and financial costs of shifting from pipeline gas to LNG.

The report also highlights a concerning trend of declining utilization rates for global LNG import facilities, with rates dropping to as low as 37% in May 2024. This trend indicates a growing risk of stranded assets and overinvestment in LNG infrastructure .

In light of these findings, the report calls for urgent action from policymakers and financiers, including:

Strengthening regulatory oversight of FSRU projects

Accelerating the transition to renewable energy

Protecting local communities and ecosystems

Conducting comprehensive risk assessments for FSRU investments

Prioritizing the shift of finance towards renewables

By shedding light on these critical issues, this report aims to inform decision-making and promote more sustainable approaches to energy security and climate change mitigation.

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