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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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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.

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23 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?

This was very helpful

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• 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.

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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

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, May 31). How to Write a Summary | Guide & Examples. Scribbr. Retrieved September 12, 2024, from https://www.scribbr.com/working-with-sources/how-to-summarize/

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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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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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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.

Schünemann HJ, Puhan M, Goldstein R, Jaeschke R, Guyatt GH. Measurement properties and interpretability of the Chronic respiratory disease questionnaire (CRQ). COPD: Journal of Chronic Obstructive Pulmonary Disease 2005; 2 : 81-89.

Schünemann HJ, Guyatt GH. Commentary--goodbye M(C)ID! Hello MID, where do you come from? Health Services Research 2005; 40 : 593-597.

Schünemann HJ, Fretheim A, Oxman AD. Improving the use of research evidence in guideline development: 13. Applicability, transferability and adaptation. Health Research Policy and Systems 2006; 4 : 25.

Schünemann HJ. Methodological idiosyncracies, frameworks and challenges of non-pharmaceutical and non-technical treatment interventions. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 2013; 107 : 214-220.

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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Sun X, Briel M, Busse JW, You JJ, Akl EA, Mejza F, Bala MM, Bassler D, Mertz D, Diaz-Granados N, Vandvik PO, Malaga G, Srinathan SK, Dahm P, Johnston BC, Alonso-Coello P, Hassouneh B, Walter SD, Heels-Ansdell D, Bhatnagar N, Altman DG, Guyatt GH. Credibility of claims of subgroup effects in randomised controlled trials: systematic review. BMJ 2012; 344 : e1553.

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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

Paperpal, an AI academic writing assistant, is designed to support academics at every step of the academic writing process. Built on over two decades of experience helping researchers get published and trained on millions of published research articles, Paperpal offers human precision at machine speed. Paperpal Copilot, with advanced generative AI features, can help academics achieve 2x the writing in half the time, while transforming how they research and write.

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.
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• 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.  

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• 5 Reasons for Rejection After Peer Review
• 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

PhD Dissertation Outline: Creating a Roadmap to Success

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CHAPTER 5 SUMMARY, CONCLUSIONS, IMPLICATIONS AND RECOMMENDATIONS FOR FURTHER STUDIES

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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.

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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.

Chapter 11:  Presenting results and ‘Summary of findings’ tables

Authors: Holger J Schünemann, Andrew D Oxman, Julian PT Higgins, Gunn E Vist, Paul Glasziou and Gordon H Guyatt on behalf of the Cochrane Applicability and Recommendations Methods Group and the Cochrane Statistical Methods Group.

Tables and figures help to present included studies and their findings in a systematic and clear format.

Forest plots are the standard way to illustrate results of individual studies and meta-analyses. These can be generated using Review Manager software, and a selection of them can be chosen for inclusion in the body of a Cochrane review.

A ‘Summary of findings’ table provides key information concerning the quality of evidence, the magnitude of effect of the interventions examined, and the sum of available data on all important outcomes for a given comparison.

The Abstract of a Cochrane review should be targeted primarily at healthcare decision makers (including clinicians, informed consumers and policy makers); and a ‘Plain language summary' conveys the findings in a straightforward style that can be understood by consumers of health care.

11.1 Introduction

11.2 Results of the search and selection of studies

11.3 Data and analyses

11.4 Figures

11.5 ‘Summary of findings’ tables

11.6 Additional tables

11.7 Presenting results in the text

11.8 Writing an abstract

Box 11.8.a: Hypothetical example of an abstract

11.9 Writing a plain language summary

11.10 Chapter information

11.11 References

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Grenfell tower inquiry: phase 2 report - summary of main findings

On 4 September 2024, the Grenfell Tower Inquiry published its highly anticipated Phase 2 Report into the fire at Grenfell Tower on 14 June 2017 (the “Report”) [1] . At the centre of this is the significant human tragedy – the fire resulted in 72 deaths, injuring and impacting many others. The Public Inquiry’s Phase 1 report, detailing the events on the night of the fire and comprising over 850 pages across four volumes, was published in late October 2019.

Seven years on from the fire, the voluminous Phase 2 Report follows consideration of substantial evidence and testimonies and examines the circumstances as well as the underlying root causes contributing to the tragedy. The Inquiry’s Terms of Reference, the impact of the pandemic in halting and delaying evidence sessions, and the sheer scale and scope of evidence presented meant that the Report took longer to conclude than initially anticipated. However, it represents another important milestone for the victims and their relatives, as well as a significant step in building a safer future.

Key Takeaways from the Report

Volume 1, Part 1 (Introduction and Executive Summary) and Volume 7, Part 14 (Recommendations) provide an overview of the Report’s core findings. Although the Report is broken down into the numerous and important wider issues, our update summarises the key parts of relevance to our clients and the construction industry, including the core conclusions reached, recommendations and potential impacts for those professionals and stakeholders operating in this space.

The Report:

• Concludes that the deaths were avoidable, and the Tower’s residents were failed by those who should have helped to ensure their safety.
• Criticises multiple entities and individuals connected with the Tower’s management and refurbishment, highlighting various decisions and errors – ranging from incompetence to dishonesty and illegality – amounting to systemic and organisational failure. The entities criticised include the government, the local authority (and its building control function), the Tenant Management Organisation (“TMO”), the London Fire Brigade, the manufacturers, suppliers and certifiers of materials including the British Board of Agrément (“BBA”), construction parties, and industry bodies such as the Building Research Establishment and the National House Building Council.
• Considers progress made to enhance building and fire safety and further change required.
• Presents 19 pages of recommendations aiming to avoid a further tragedy, addressing central and local government, fire and rescue services and the wider construction industry.

Core Findings in the Report

• Government: Although various government departments received warnings concerning combustible panels and insulation from about 1991, there was a failure to give proper consideration to the danger of using and incorporating such materials in high rise buildings (“HRBs”); misunderstanding that regulation was effective; and failure to amend the applicable guidance. According to parts of the Report, departments displayed a complacent, defensive or dismissive attitude to fire safety matters.
• Regulatory context: This was unsatisfactory, particularly in respect of the industry’s regard to compliance with the relevant guidance (as well as the guidance not being clear enough about the need for compliance with the Building Regulations).
• Architect: The appointment of the architect without going through competitive public procurement (because they were already appointed as architect on another project) was viewed by the TMO as beneficial at the time. However, this had serious consequences including due to the architect’s lack of experience of working on high rise overcladding projects. Its failure to recognise that the products to be used were unsuitable/dangerous and to warn against their use also represented a failure to act in accordance with the applicable standard of care and in line with statutory guidance. Criticisms were also made since the architect failed to understand its responsibility for its design work, and, in its capacity as Lead Designer, work carried out by sub-contractors, and did not check relevant designs. It did not devise a proper cavity barrier strategy, produce detailed drawings of the window reveals, or notice that the materials specified for the window infill panels were unsuitable.
• Contractor and sub-contractor: The principal contractor and sub-contractor failed to fully comprehend the choice of materials (and who was responsible for the same). The Report notes that the Design and Build Contractor used an inexperienced team, gave inadequate thought to fire safety, displayed a casual attitude and its systems for managing design work did not ensure that its sub-contractors/consultants were competent and properly understood their responsibilities. The contractor did not understand where responsibility for decisions lay and failed to co-ordinate the design work properly. The sub-contractor failed to ask about the materials being considered and appeared to be induced to buy products partly by its existing relationships.
• The relevant construction parties: Were criticised for failing to clearly identify responsibilities for important aspects of the design and assumed someone else was responsible for matters affecting fire safety. The Report infers an unacceptably casual approach towards contractual arrangements and responsibilities and a pervasive trend of evading responsibility by continually shifting the blame (see the ‘web’ of blame produced following earlier stages of the Inquiry). Further, core project documents were left in draft or were incomplete, such as the fire safety strategy for the building in its refurbished form.
• Building control: This was compounded since the design or choice of materials was not scrutinised and building control failed to confirm that the completed work would comply with Building Regulations. The building control surveyor was inadequately trained, overworked, and had limited understanding of the risks of such panels. The surveyor failed to obtain full information on the construction and enquire about the completed fire safety strategy. Additionally, the Report highlights the tension between the regulatory function of building control, and the conflicting commercial interests or pressures that prevented a system that effectively served the public interest.
• Manufacturers: The Panel also found systematic inadequacies and dishonesty, for example with the deliberate manipulation of test data, provision of information, and in the marketing of products which were unsuitable for use in the external walls of HRBs. This dishonesty was compounded by the failure of certifying bodies to scrutinise the information and wording provided to them about these products and to enforce processes and contracts robustly.
• TMO: The TMO, and its relationship with the residents, was key. It was poorly run and failed to respond to residents’ requests and the recommendations made in an independent report in 2009. The antagonistic and deteriorating relationship was characterised by mistrust and the Panel deemed that the TMO failed to carry out its basic fire safety responsibilities. The Panel specifically highlighted issues with the management and importance placed on fire safety matters, considering that residents were badly let down particularly in relation to the safety of vulnerable people. The TMO’s CEO failed to keep the board properly updated and informed about fire safety concerns despite issues and notices drawn to its attention.
• Further the TMO’s failings were illustrated by its reliance on fire risk assessments across its estate portfolio by one individual who was unqualified to carry these out, failures to carry out remedial works to defects and fire safety measures, and adequately record and maintain information on vulnerable people who might need assistance in evacuating in an emergency. The TMO also failed to take sufficient care choosing the architect and paid insufficient attention to fire safety matters, including the fire engineer’s work.
• Generally: Throughout the construction industry, including product manufacturers and installers, companies had a lack of understanding or awareness and appeared to take advantage of a flawed and ineffective regulatory system to attract or retain customers, cut costs, and boost commercial drivers and profits. There effectively appeared to be a presumption that Building Regulations and building control processes were effective, construction products underwent appropriate testing and marketing, and that designers and contractors were fully competent and aware of their roles.

Relevant Recommendations

The Panel believe that consideration and implementation of its recommendations will increase fire safety, particularly for HRBs, and lead to efficiencies in the construction industry and fire and rescue services.

The Report acknowledges that the safety of people in the built environment depends principally on a combination of good design, choice of suitable materials and sound methods of construction, each of which also rely on the skill, knowledge and experience of those engaged in the construction industry. The Inquiry’s investigations unfortunately revealed serious deficiencies in all those areas.

As a result, the main recommendations to note include:

• Establishing a single construction regulator (person) to oversee all aspects of the construction industry and inform government of developments or changes to such requirements. The regulator’s role would involve driving change in industry culture; promoting and exchanging of information or ideas across industry (both in the UK and abroad); certifying construction products; and licensing contractors for HRBs, amongst other functions currently discharged by one or various bodies.
• The Secretary of State appointing a Chief Construction Adviser with good working knowledge and practical experience of the construction industry.
• Consolidating fragmented government fire safety responsibilities into one department so proper regulatory oversight can be given and efficiencies gained.
• Reviewing and amending the current law as necessary given the Report’s conclusions and recommendations (including a potential widening of the definition of HRBs, since the reference to the height of the building is deemed unsatisfactory and arbitrary in nature).
• Clarifying and updating the Approved Documents and statutory guidance accompanying Building Regulations, primarily driven by safety concerns and with appropriate review and input from representatives from academia and those with practical industry experience chosen for experience and skill.
• New statutory requirements for certain documentation or statements to be produced in support of the relevant building control applications and at Gateways 2 or 3. For example, a fire safety strategy produced by a registered fire engineer; and appropriate statements from the principal designer and principal contractor confirming that all reasonable steps have been taken to ensure that the building will be safe on completion.
• A review and development of new test methods to provide reliable assessments and information, with the construction regulator responsible for assessing the conformity of construction products with the requirements of legislation, statutory guidance and industry standards and certifying as appropriate.
• A professional fire engineering body with regulation , including on title, qualifications and conduct. Coupled with the acceleration of an objective authoritative statement of the skills a fire engineer should be expected to possess, i.e. from a practical, scientific and intellectual perspective, and the number of places on high-quality fire engineering courses to help improve competence and promote effective communication overall.
• Whilst the Report acknowledges that the Architects Registration Board and the Royal Institute of British Architects have taken steps to improve the education and training of architects, these should be reviewed to ensure they are sufficient in light of findings.
• Licencing scheme for contractors on HRBs and mandatory accreditation system for fire risk assessors .
• The creation and maintenance of a public record of recommendations and steps taken in response to feedback from public inquiries, coroners, and select committees.
• Creation of a construction library with key information including product and test data.
• Evaluating building control functions including whether these should be performed by parties with a commercial interest, or possibly by a national authority.
• Appropriate and regular training for fire and rescue services .

Concluding Commentary

The comprehensive Report has already received widespread attention across the UK and the construction industry and is likely to inform the approach to building and fire safety taken in other jurisdictions in the future.

The Report will interest legal practitioners and construction professionals since it will impact future change within the construction industry (including cultural, legislative, regulatory and technological change). It notes that Section 2(1) of the Inquiries Act 2005 expressly prohibits the Panel from ruling on questions of legal liability, civil or criminal – that is a matter for courts. However, Section 2(2) provides that it is not to be inhibited in the discharge of its functions by any likelihood of liability being inferred from any facts found or recommendations made, and so it may be persuasive for future procurements and negotiations of construction contracts or on the future claims or prosecutions. The Report’s suggestions are also expected to shape further government policy on fire and building safety in England and Wales, akin to the influence of the earlier Hackitt Report after the tragedy in 2018. In light of recent fires reported, including within the London area, the Report may also lead to the review and acceleration of remedial works to other affected HRBs.

Notwithstanding the impact of the earlier reforms, the Report unequivocally indicates that these are insufficient. According to the recommendations, there is a pressing need for more comprehensive regulation of the construction industry and fire safety professions, and in terms of overhauling the industry’s culture. Ensuring building and fire safety must remain the top priority for all involved in designing, constructing, maintaining, and managing buildings.

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Study Unveils Shared Cellular Mechanisms in Major Dementias

Summary: Researchers have identified common and distinct molecular markers across Alzheimer’s disease, frontotemporal dementia, and progressive supranuclear palsy, potentially revolutionizing our understanding and treatment of these disorders.

Estimated reading time: 6 minutes

A new study has uncovered shared and unique cellular mechanisms across three major forms of dementia, offering fresh insights into these devastating neurological disorders. The research, led by scientists at the University of California, Los Angeles (UCLA), marks a significant step forward in our understanding of how different types of dementia affect the brain at the cellular level.

The study, published in the journal Cell, analyzed over one million individual brain cells from 41 participants, focusing on three types of dementia: Alzheimer’s disease (AD), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP). By examining multiple brain regions and comparing different disorders, the researchers identified both common features and disease-specific changes that could lead to new therapeutic approaches.

Shared Vulnerabilities and Unique Signatures

Dr. Daniel Geschwind, the study’s senior author and a professor at UCLA, explained the significance of their approach: “This work provides new insight into the mechanisms of neurodegeneration and identifies new candidate pathways for development of therapeutics.”

Unlike previous studies that typically focused on a single disorder and brain region, this research examined three related conditions across multiple areas of the brain. This comprehensive approach revealed 32 cell types with shared disease-associated changes and 14 that were specific to individual disorders.

The team discovered that different dementias affect distinct types of neurons:

• Alzheimer’s disease primarily impacts layer 5 intratelencephalic neurons
• Frontotemporal dementia affects layer 2/3 intratelencephalic neurons
• Progressive supranuclear palsy targets layer 5/6 near-projection neurons

These findings help explain why symptoms can vary significantly between different types of dementia, despite their shared underlying feature of neurodegeneration.

Genetic Risk and Cellular Vulnerability

One of the study’s most intriguing discoveries was how genetic risk factors for these diseases relate to specific cellular changes. Dr. Jessica Rexach, the study’s first author, noted that this finding “opens new avenues for understanding why and how certain genes influence the risk of developing one brain disease over another closely related condition.”

The research identified four genes that marked vulnerable neurons across all three disorders, highlighting potential targets for developing treatments that could address multiple forms of dementia.

Why It Matters

This research is crucial for several reasons:

• It provides a more comprehensive understanding of how different dementias affect the brain, which could lead to more targeted treatments.
• The identification of shared mechanisms across multiple disorders may allow for the development of therapies that could benefit patients with various types of dementia.
• By pinpointing disease-specific changes, the study opens up new avenues for personalized medicine approaches in dementia treatment.

With over 28 million people worldwide affected by Alzheimer’s, FTD, and PSP combined, and no current cures available, this research offers hope for more effective treatments in the future.

Unexpected Findings and Future Directions

The study yielded several surprising results that challenge our current understanding of dementia:

• Changes in the primary visual cortex: The researchers found alterations in brain cells in an area previously thought to be unaffected by dementia, suggesting that these disorders may have wider-ranging effects than previously believed.
• Cellular resilience programs: The team discovered that molecular mechanisms supporting cells in response to injury activated differently across the three disorders, providing clues about why some brain cells are more vulnerable than others.
• Specific changes in PSP: The study identified unique alterations in tau-related genes in progressive supranuclear palsy, which may explain the distinct pattern of brain cell degeneration observed in this condition.

Dr. Rexach emphasized the potential impact of these findings: “We have created an extensive data resource that paves the way for identifying and exploring new therapeutic candidates for neurodegenerative dementias.”

The research team plans to conduct further experiments to validate their findings and explore their causal nature. They hope their work will inspire similar cross-disorder studies, potentially leading to breakthroughs in our understanding and treatment of various neurodegenerative diseases.

As we continue to unravel the complex mechanisms underlying different forms of dementia, this study represents a significant step towards more effective, targeted therapies for millions of affected individuals worldwide.

• Which three types of dementia did the study focus on?
• How many shared disease-associated cell types did the researchers identify?
• What unexpected finding did the study reveal about the primary visual cortex?

Answer Key:

• The study focused on Alzheimer’s disease, frontotemporal dementia, and progressive supranuclear palsy.
• The researchers identified 32 shared disease-associated cell types.
• The study found changes in cells of the primary visual cortex, an area previously thought to be unaffected by dementia.

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A bibliometric and visual analysis of the research status and hotspots of seborrheic dermatitis based on web of science

Affiliations.

• 1 Changchun University of Chinese Medicine, Changchun, China.
• 2 Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China.
• PMID: 39252564
• PMCID: PMC11386261
• DOI: 10.1111/srt.70048

Background: Seborrheic dermatitis (SD) is a common chronic inflammatory skin disease. In recent years, significant progress has been made in the field of SD, but there has been no bibliometric research yet. This study aims to use bibliometric methods to analyze the current research status and hot topics of SD, to understand further the research trends and future development prospects in this field.

Methods: Retrieve core literature on SD from the Web of Science database and conduct a detailed analysis using CiteSpace and VOSviewer software based on factors such as publication volume, countries (regions), research institutions, journals, authors, highly-cited papers, and keywords.

Results: From 1996 to 2024, a total of 1436 publications were included in the bibliometric analysis. The number of publications has shown an increasing trend year by year. The USA is the leading country in this field of research. The University of California System is the primary research institution. The International Journal of Dermatology is the journal with the highest number of publications. The author Yang Won Lee has the highest number of publications, while the article "Seborrheic Dermatitis" (2004) by Gupta, A.K. has been cited the most. "Seborrheic dermatitis" is the most frequently occurring keyword. The main research hotspots and frontiers in SD are as follows: (1) The relationship between SD and other skin diseases is a popular research topic; (2) Malassezia and inflammation are current research hotspots in SD; and (3) Focusing on antifungal and anti-inflammatory treatments for SD is the current frontier direction in this field.

Conclusion: This study is a summary of the current status and hot trends of SD research, which helps clinical doctors and researchers quickly understand the insights and valuable information of SD research and provides reference for clinical decision-making and finding future research directions.

Keywords: CiteSpace; VOSviewer; bibliometric; seborrheic dermatitis; visual analysis.

© 2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.

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Conflict of interest statement

The authors declare no conflicts of interest.

Publications screening flowchart.

Trends in annual publications of…

Trends in annual publications of seborrheic dermatitis from 1996 to 2024.

Visualization of countries researching seborrheic…

Visualization of countries researching seborrheic dermatitis.

Co‐occurrence collaboration network of research…

Co‐occurrence collaboration network of research institutions on seborrheic dermatitis.

Journal co‐occurrence collaboration network.

Author co‐occurrence collaboration network.

(A) Co‐occurrence network of keywords.…

(A) Co‐occurrence network of keywords. (B) VOSviewer keywords density map.

Keywords clustering map.

Keywords temporal weight map.

Timeline visualization analysis of keywords.

Top 25 keywords with the…

Top 25 keywords with the strongest citation bursts.

• Jackson JM, Alexis A, Zirwas M, Taylor S. Unmet needs for patients with seborrheic dermatitis. J Am Acad Dermatol. 2024;90(3):597‐604. doi:10.1016/j.jaad.2022.12.017 - DOI - PubMed
• Dall'Oglio F, Nasca MR, Gerbino C, Micali G. An overview of the diagnosis and management of seborrheic dermatitis. Clin Cosmet Investig Dermatol. 2022;15:1537‐1548. doi:10.2147/CCID.S284671 - DOI - PMC - PubMed
• Leroy AK, Cortez de Almeida RF, Obadia DL, Frattini S, Melo DF. Scalp seborrheic dermatitis: what we know so far. Skin Appendage Disord. 2023;9(3):160‐164. doi:10.1159/000529854 - DOI - PMC - PubMed
• Mustarichie R, Rostinawati T, Pitaloka DAE, Saptarini NM, Iskandar Y. Herbal therapy for the treatment of seborrhea dermatitis. Clin Cosmet Investig Dermatol. 2022;15:2391‐2405. doi:10.2147/CCID.S376700 - DOI - PMC - PubMed
• Mangion SE, Mackenzie L, Roberts MS, Holmes AM. Seborrheic dermatitis: topical therapeutics and formulation design. Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV. 2023;185:148‐164. doi:10.1016/j.ejpb.2023.01.023 - DOI - PubMed
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• YDZJ202301ZYTS140/Natural Science Foundation of Jilin Province

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