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Research Summary – Structure, Examples and Writing Guide

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Research Summary

Definition:

A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings. It is often used as a tool to quickly communicate the main findings of a study to other researchers, stakeholders, or decision-makers.

Structure of Research Summary

The Structure of a Research Summary typically include:

• Introduction : This section provides a brief background of the research problem or question, explains the purpose of the study, and outlines the research objectives.
• Methodology : This section explains the research design, methods, and procedures used to conduct the study. It describes the sample size, data collection methods, and data analysis techniques.
• Results : This section presents the main findings of the study, including statistical analysis if applicable. It may include tables, charts, or graphs to visually represent the data.
• Discussion : This section interprets the results and explains their implications. It discusses the significance of the findings, compares them to previous research, and identifies any limitations or future directions for research.
• Conclusion : This section summarizes the main points of the research and provides a conclusion based on the findings. It may also suggest implications for future research or practical applications of the results.
• References : This section lists the sources cited in the research summary, following the appropriate citation style.

How to Write Research Summary

Here are the steps you can follow to write a research summary:

• Read the research article or study thoroughly: To write a summary, you must understand the research article or study you are summarizing. Therefore, read the article or study carefully to understand its purpose, research design, methodology, results, and conclusions.
• Identify the main points : Once you have read the research article or study, identify the main points, key findings, and research question. You can highlight or take notes of the essential points and findings to use as a reference when writing your summary.
• Write the introduction: Start your summary by introducing the research problem, research question, and purpose of the study. Briefly explain why the research is important and its significance.
• Summarize the methodology : In this section, summarize the research design, methods, and procedures used to conduct the study. Explain the sample size, data collection methods, and data analysis techniques.
• Present the results: Summarize the main findings of the study. Use tables, charts, or graphs to visually represent the data if necessary.
• Interpret the results: In this section, interpret the results and explain their implications. Discuss the significance of the findings, compare them to previous research, and identify any limitations or future directions for research.
• Conclude the summary : Summarize the main points of the research and provide a conclusion based on the findings. Suggest implications for future research or practical applications of the results.
• Revise and edit : Once you have written the summary, revise and edit it to ensure that it is clear, concise, and free of errors. Make sure that your summary accurately represents the research article or study.
• Add references: Include a list of references cited in the research summary, following the appropriate citation style.

Example of Research Summary

Here is an example of a research summary:

Title: The Effects of Yoga on Mental Health: A Meta-Analysis

Introduction: This meta-analysis examines the effects of yoga on mental health. The study aimed to investigate whether yoga practice can improve mental health outcomes such as anxiety, depression, stress, and quality of life.

Methodology : The study analyzed data from 14 randomized controlled trials that investigated the effects of yoga on mental health outcomes. The sample included a total of 862 participants. The yoga interventions varied in length and frequency, ranging from four to twelve weeks, with sessions lasting from 45 to 90 minutes.

Results : The meta-analysis found that yoga practice significantly improved mental health outcomes. Participants who practiced yoga showed a significant reduction in anxiety and depression symptoms, as well as stress levels. Quality of life also improved in those who practiced yoga.

Discussion : The findings of this study suggest that yoga can be an effective intervention for improving mental health outcomes. The study supports the growing body of evidence that suggests that yoga can have a positive impact on mental health. Limitations of the study include the variability of the yoga interventions, which may affect the generalizability of the findings.

Conclusion : Overall, the findings of this meta-analysis support the use of yoga as an effective intervention for improving mental health outcomes. Further research is needed to determine the optimal length and frequency of yoga interventions for different populations.

References :

• Cramer, H., Lauche, R., Langhorst, J., Dobos, G., & Berger, B. (2013). Yoga for depression: a systematic review and meta-analysis. Depression and anxiety, 30(11), 1068-1083.
• Khalsa, S. B. (2004). Yoga as a therapeutic intervention: a bibliometric analysis of published research studies. Indian journal of physiology and pharmacology, 48(3), 269-285.
• Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: a review of comparison studies. The Journal of Alternative and Complementary Medicine, 16(1), 3-12.

Purpose of Research Summary

The purpose of a research summary is to provide a brief overview of a research project or study, including its main points, findings, and conclusions. The summary allows readers to quickly understand the essential aspects of the research without having to read the entire article or study.

Research summaries serve several purposes, including:

• Facilitating comprehension: A research summary allows readers to quickly understand the main points and findings of a research project or study without having to read the entire article or study. This makes it easier for readers to comprehend the research and its significance.
• Communicating research findings: Research summaries are often used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public. The summary presents the essential aspects of the research in a clear and concise manner, making it easier for non-experts to understand.
• Supporting decision-making: Research summaries can be used to support decision-making processes by providing a summary of the research evidence on a particular topic. This information can be used by policymakers or practitioners to make informed decisions about interventions, programs, or policies.
• Saving time: Research summaries save time for researchers, practitioners, policymakers, and other stakeholders who need to review multiple research studies. Rather than having to read the entire article or study, they can quickly review the summary to determine whether the research is relevant to their needs.

Characteristics of Research Summary

The following are some of the key characteristics of a research summary:

• Concise : A research summary should be brief and to the point, providing a clear and concise overview of the main points of the research.
• Objective : A research summary should be written in an objective tone, presenting the research findings without bias or personal opinion.
• Comprehensive : A research summary should cover all the essential aspects of the research, including the research question, methodology, results, and conclusions.
• Accurate : A research summary should accurately reflect the key findings and conclusions of the research.
• Clear and well-organized: A research summary should be easy to read and understand, with a clear structure and logical flow.
• Relevant : A research summary should focus on the most important and relevant aspects of the research, highlighting the key findings and their implications.
• Audience-specific: A research summary should be tailored to the intended audience, using language and terminology that is appropriate and accessible to the reader.
• Citations : A research summary should include citations to the original research articles or studies, allowing readers to access the full text of the research if desired.

When to write Research Summary

Here are some situations when it may be appropriate to write a research summary:

• Proposal stage: A research summary can be included in a research proposal to provide a brief overview of the research aims, objectives, methodology, and expected outcomes.
• Conference presentation: A research summary can be prepared for a conference presentation to summarize the main findings of a study or research project.
• Journal submission: Many academic journals require authors to submit a research summary along with their research article or study. The summary provides a brief overview of the study’s main points, findings, and conclusions and helps readers quickly understand the research.
• Funding application: A research summary can be included in a funding application to provide a brief summary of the research aims, objectives, and expected outcomes.
• Policy brief: A research summary can be prepared as a policy brief to communicate research findings to policymakers or stakeholders in a concise and accessible manner.

Advantages of Research Summary

Research summaries offer several advantages, including:

• Time-saving: A research summary saves time for readers who need to understand the key findings and conclusions of a research project quickly. Rather than reading the entire research article or study, readers can quickly review the summary to determine whether the research is relevant to their needs.
• Clarity and accessibility: A research summary provides a clear and accessible overview of the research project’s main points, making it easier for readers to understand the research without having to be experts in the field.
• Improved comprehension: A research summary helps readers comprehend the research by providing a brief and focused overview of the key findings and conclusions, making it easier to understand the research and its significance.
• Enhanced communication: Research summaries can be used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public, in a concise and accessible manner.
• Facilitated decision-making: Research summaries can support decision-making processes by providing a summary of the research evidence on a particular topic. Policymakers or practitioners can use this information to make informed decisions about interventions, programs, or policies.
• Increased dissemination: Research summaries can be easily shared and disseminated, allowing research findings to reach a wider audience.

Limitations of Research Summary

Limitations of the Research Summary are as follows:

• Limited scope: Research summaries provide a brief overview of the research project’s main points, findings, and conclusions, which can be limiting. They may not include all the details, nuances, and complexities of the research that readers may need to fully understand the study’s implications.
• Risk of oversimplification: Research summaries can be oversimplified, reducing the complexity of the research and potentially distorting the findings or conclusions.
• Lack of context: Research summaries may not provide sufficient context to fully understand the research findings, such as the research background, methodology, or limitations. This may lead to misunderstandings or misinterpretations of the research.
• Possible bias: Research summaries may be biased if they selectively emphasize certain findings or conclusions over others, potentially distorting the overall picture of the research.
• Format limitations: Research summaries may be constrained by the format or length requirements, making it challenging to fully convey the research’s main points, findings, and conclusions.
• Accessibility: Research summaries may not be accessible to all readers, particularly those with limited literacy skills, visual impairments, or language barriers.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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When writing a summary, the goal is to compose a concise and objective overview of the original article. The summary should focus only on the article's main ideas and important details that support those ideas.

Guidelines for summarizing an article:

• State the main ideas.
• Identify the most important details that support the main ideas.
• Summarize in your own words.
• Do not copy phrases or sentences unless they are being used as direct quotations.
• Express the underlying meaning of the article, but do not critique or analyze.
• The summary should be about one third the length of the original article. 

Your summary should include:

• Give an overview of the article, including the title and the name of the author.
• Provide a thesis statement that states the main idea of the article.
• Use the body paragraphs to explain the supporting ideas of your thesis statement.
• One-paragraph summary - one sentence per supporting detail, providing 1-2 examples for each.
• Multi-paragraph summary - one paragraph per supporting detail, providing 2-3 examples for each.
• Start each paragraph with a topic sentence.
• Use transitional words and phrases to connect ideas.
• Summarize your thesis statement and the underlying meaning of the article.

 Adapted from "Guidelines for Using In-Text Citations in a Summary (or Research Paper)" by Christine Bauer-Ramazani, 2020

Additional Resources

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How to Write a Summary - Guide & Examples  (from Scribbr.com)

Writing a Summary  (from The University of Arizona Global Campus Writing Center)

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• Last Updated: Mar 15, 2024 9:32 AM
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Not every source you found should be included in your annotated bibliography or lit review. Only include the most relevant and most important sources.

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• Lit Review Prep Use this template to help you evaluate your sources, create article summaries for an annotated bibliography, and a synthesis matrix for your lit review outline.

Summarize your Sources

Summarize each source: Determine the most important and relevant information from each source, such as the findings, methodology, theories, etc.  Consider using an article summary, or study summary to help you organize and summarize your sources.

Paraphrasing

• Use your own words, and do not copy and paste the abstract
• The library's tutorials about plagiarism are excellent, and will help you with paraphasing correctly

Annotated Bibliographies

     Annotated bibliographies can help you clearly see and understand the research before diving into organizing and writing your literature review.        Although typically part of the "summarize" step of the literature review, annotations should not merely be summaries of each article - instead, they should be critical evaluations of the source, and help determine a source's usefulness for your lit review.  

Definition:

A list of citations on a particular topic followed by an evaluation of the source’s argument and other relevant material including its intended audience, sources of evidence, and methodology

• Explore your topic.
• Appraise issues or factors associated with your professional practice and research topic.
• Help you get started with the literature review.
• Think critically about your topic, and the literature.

Steps to Creating an Annotated Bibliography:

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10 days ago

Synthesis vs Summary

How to summarize a research article.

Image: unsplash.com

Ever found yourself swimming through the sea of a research article’s content, wondering how to find those golden nuggets of useful information? Summarizing a research article is your treasure map. It’s a great skill to know how to find the essence, pinpoint the relevant findings, and present them on a silver platter. You can do it in just a few quick steps that include understanding the article’s purpose, skimming for key points, digging deeper into its content, and then writing and refining your summary.

What Is A Research Article?

If you’re still uncertain how to summarize a research article, the first step should be to gain a complete understanding of this type of writing. Research articles are special kinds of writings found mostly in academic and scientific journals. They have a specific structure that sets them apart from other types of writing you’ve probably done before – like essays, for example. This structure includes sections such as the Title, Abstract, Introduction, Methods, Results, Discussion, and References. Each part plays its role:

Now that you’re familiar with what a research article is and its key components, let’s find out how to effectively summarize it.

Summarizing a research article can be divided into two stages. There is something you should do before writing, and there are also very important things to keep in mind during actually writing. Here we break them down for your convenience:

Before writing

Summarizing a research paper involves understanding its main ideas and communicating its results clearly. Here’s how to do it successfully:

Determine Your Focus:  First, pinpoint why you’re summarizing the article. If it’s for personal study, you might opt for a detailed summary. For inclusion in a paper, concentrate on how the article’s insights relate specifically to your work. Your purpose shapes your summary’s focus.

Read the Article:  Allow much time for comprehension. Understanding a research article can be more time-consuming than expected. You’re ready to summarize only when you can explain the study in your own words to someone unfamiliar with the article.

Scan the context:  Avoid getting lost in the details by scanning the article to identify the main points. Focus on understanding the research question, hypotheses, methods, findings, and their interpretation. This step helps in getting the study’s core without getting bogged down.

Underline Key Points:  As you scan, underline significant sentences or jot down key points in the margins. This practice helps in isolating the essential information, although relying only on the abstract is not advisable due to its condensed nature.

Read for Depth:  After identifying the main points, dedicate more attention to each section. Ask critical questions about the study’s design, the results convincingness, and what new insights the study contributes. This in-depth reading provides a deeper understanding.

Avoid Plagiarism:  Ensure originality by taking notes in your own words and paraphrasing key points. Understanding the content well enough to rephrase it is crucial for avoiding plagiarism.

Or just leave it to our Free Summarizing Tool

During writing.

Now that all the preparation work has been completed and all the required material has been collected, you can start working.

Write a First Draft:  Begin by drafting your summary, following the article’s structure. Highlight the research question, hypotheses, methods, results, and their significance. Initially, focus on content rather than length, which can be adjusted later.

Focus on the Hypotheses, Methods, and Results:  Start by writing about these sections as they form the study’s backbone. Explaining these elements provides a clear overview of the research’s objectives, execution, and findings.

Introduction and Discussion:  Write about these sections after covering the core elements. They provide context and interpret the study’s implications, enriching your summary with a comprehensive understanding of the research’s scope and significance.

Edit for Completeness and Accuracy:  Refine your draft by checking that all critical information is included and accurately represented. This step might involve cutting redundant details or adding necessary information for clarity.

Edit for Style:  Aim for clear, concise language that speaks directly to your audience. Avoid jargon, and unnecessary adverbs, and ensure your summary is easily understandable. Paraphrasing is preferred over direct quoting in scientific summaries. Cite the original study to give due credit.

As you’ve already figured, summarizing a research article is like drawing a map of a huge, unknown area. It’s all about understanding the main ideas, going through the detailed science, and sharing the important points in a way that’s easy to understand. With some practice, you’ll get really good at this and might even start to enjoy exploring all this new information.

How long should my summary be?

The length varies based on the article’s complexity and your summarizing purpose. Aim for conciseness while ensuring all critical information is covered.

Can I use direct quotes from the article in my summary?

Direct quotes are rare in scientific writing. They can be included only if it is necessary. Preferably, paraphrase and cite appropriately to maintain academic integrity.

How do I ensure I don’t plagiarize when summarizing?

Paraphrase in your own words, take thorough notes, and cite the original work. If you understand the article well, expressing its contents in your own language should come naturally.

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Understanding Article Summaries

An article summary is a short, focused paper about one scholarly article that is informed by a critical reading of that article. For argumentative articles, the summary identifies, explains, and analyses the thesis and supporting arguments; for empirical articles, the summary identifies, explains, and analyses the research questions, methods, findings, and implications of the study.

Although article summaries are often short and rarely account for a large portion of your grade, they are a strong indicator of your reading and writing skills. Professors ask you to write article summaries to help you to develop essential skills in critical reading, summarizing, and clear, organized writing. Furthermore, an article summary requires you to read a scholarly article quite closely, which provides a useful introduction to the conventions of writing in your discipline (e.g. Political Studies, Biology, or Anthropology).

The most common problem that students have when writing an article summary is that they misunderstand the goal of the assignment. In an article summary, your job is to write about the article, not about the actual topic of the article. For example, if you are summarizing Smith’s article about the causes of the Bubonic plague in Europe, your summary should be about Smith’s article: What does she want to find out about the plague? What evidence does she use? What is her argument? You are not writing a paper about the actual causes of Bubonic plague in Europe.

Further, as a part of critical reading, you will often consider your own position on a topic or an argument; it is tempting to include an assessment or opinion about the thesis or findings, but this is not the goal of an article summary. Rather, you must identify, explain, and analyse the main point and how it is supported.

Your key to success in writing an article summary is your understanding of the article; therefore, it is essential to read carefully and closely. The Academic Skills Centre offers helpful instruction on the steps for critical reading: pre-reading, active and analytical reading, and reflection.

Argumentative Articles

As you read an argumentative article, consider the following questions:

• What is the topic?
• What is the research question? In other words, what is the author trying to find out about that topic?
• How does the author position his/her article in relation to other studies of the topic?
• What is the thesis or position? What are the supporting arguments?
• How are supporting arguments developed? What kind of evidence is used?
• What is the significance of the author’s thesis? What does it help you to understand about the topic?

Empirical Articles

As you read an empirical article, consider the following questions:

• What is the research question?
• What are the predictions and the rationale for these predictions?
• What methods were used (participants, sampling, materials, procedure)? What were the variables and controls?
• What were the main results?
• Are the findings supported by previous research?
• What are the limitations of the study?
• What are the implications or applications of the findings?

Create a Reverse Outline

Creating a reverse outline is one way to ensure that you fully understand the article. Pre-read the article (read the abstract, introduction, and/or conclusion). Summarize the main question(s) and thesis or findings. Skim subheadings and topic sentences to understand the organization; make notes in the margins about each section. Read each paragraph within a section; make short notes about the main idea or purpose of each paragraph. This strategy will help you to see how parts of the article connect to the main idea or the whole of the article.

A summary is written in paragraph form and generally does not include subheadings. An introduction is important to clearly identify the article, the topic, the question or purpose of the article, and its thesis or findings. The body paragraphs for a summary of an argumentative article will explain how arguments and evidence support the thesis. Alternatively, the body paragraphs of an empirical article summary may explain the methods and findings, making connections to predictions. The conclusion explains the significance of the argument or implications of the findings. This structure ensures that your summary is focused and clear.

Professors will often give you a list of required topics to include in your summary and/or explain how they want you to organize your summary. Make sure you read the assignment sheet with care and adapt the sample outlines below accordingly.

One significant challenge in writing an article summary is deciding what information or examples from the article to include. Remember, article summaries are much shorter than the article itself. You do not have the space to explain every point the author makes. Instead, you will need to explain the author’s main points and find a few excellent examples that illustrate these points.

You should also keep in mind that article summaries need to be written in your own words. Scholarly writing can use complex terminology to explain complicated ideas, which makes it difficult to understand and to summarize correctly. In the face of difficult text, many students tend to use direct quotations, saving them the time and energy required to understand and reword it. However, a summary requires you to summarize, which means “to state briefly or succinctly” (Oxford English Dictionary) the main ideas presented in a text. The brevity must come from you, in your own words, which demonstrates that you understand the article.

Sample Outlines and Paragraph

Sample outline for an argumentative article summary.

• General topic of article
• Author’s research question or approach to the topic
• Author’s thesis
• Explain some key points and how they support the thesis
• Provide a key example or two that the author uses as evidence to support these points
• Review how the main points work together to support the thesis?
• How does the author explain the significance or implications of his/her article?

Sample Outline for an Empirical Article Summary

• General topic of study
• Author’s research question
• Variables and hypotheses
• Participants
• Experiment design
• Materials used
• Key results
• Did the results support the hypotheses?
• Implications or applications of the study
• Major limitations of the study

Sample Paragraph

The paragraph below is an example of an introductory paragraph from a summary of an empirical article:

Tavernier and Willoughby’s (2014) study explored the relationships between university students’ sleep and their intrapersonal, interpersonal, and educational development. While the authors cited many scholars who have explored these relationships, they pointed out that most of these studies focused on unidirectional correlations over a short period of time. In contrast, Tavernier and Willoughby tested whether there was a bidirectional or unidirectional association between participants’ sleep quality and duration and several psychosocial factors including intrapersonal adjustment, friendship quality, and academic achievement. Further they conducted a longitudinal study over a period of three years in order to determine whether there were changes in the strength or direction of these associations over time. They predicted that sleep quality would correlate with measures of intrapersonal adjustment, friendship quality, and academic achievement; they further hypothesized that this correlation would be bidirectional: sleep quality would predict psychosocial measures and at the same time, psychosocial measures would predict sleep quality.

Research Guides

Ggr401: geomorphology and the anthropocene.

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How Do You Summarize an Article

A summary has  two aims :

• to reproduce the overarching ideas in a text, identifying the  general  concepts that run through the entire piece, and
• to express these overarching ideas using  precise , specific language.

Your Summary Should Include :

1. Introduction

• Start with an overview of the article which includes the author’s name and the title of the article.
• Include a sentence that states the main idea of the article.

​​2. Body 

• Each paragraph should begin with a topic sentence.
• The number of paragraphs in your summary depends on the length of the original article.
• Each paragraph focuses on a separate main idea and just the most important details from the article.

​3. Conclusion

• Summarize the main idea and the underlying meaning of the article.

Adapted from " Guidelines for Writing a Summary " by Christine Bauer-Ramazani, Saint Michael's College.

Tips on Summarizing

Please see the video  Tips on Summarizing  on the  Ohio State Flipped ESL  YouTube channel.  This video investigates the basic elements needed to create an effective one-sentence summary and a summary paragraph.

Additional Resources on How to Summarize

Here are some additional resources on how to summarize an academic article:

• Summarizing (U of T)
• How to Summarize a Research Article (UConn)
• Guidelines for Summarizing an article (Andrews University)
• Guidelines for using In-text citations in a summary or research paper (Saint Michael's College)
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How to Summarize Any Research Article Better: Proven Tips Outlined

Rev › Blog › Marketing › How to Summarize Any Research Article Better: Proven Tips Outlined

You’ve got content gold on your hands—  primary  and  secondary  research materials from some of the top market research companies. Now, it’s time to decide how it relates to your products, project, or consumers. What’s more, you need to distill each article’s essential parts into easy-to-read, accurate, informative, and, most importantly, concise summaries. Overwhelming? Maybe. Impossible? Heck no; you just need a good strategy. So, where to start?

You’ve landed on the right page! These tips and techniques provide a template to help guide you through the process. 

Know Your Focus

The streaming TV hit,  Cobra Kai,  brings to mind Mr. Miyagi’s age-old wisdom– ‘Focus, Daniel-San.’ Focus is vital, as some sections of a research article are more relevant to your strategy than others. 

For example, a summary crafted for a school project or a university may focus on the experiment itself. In contrast, the article’s results and discussion sections may be more relevant to consumer marketing or for a business model.

Once you establish your focus, you’re less likely to waste time.

Read The Research Article

But before you do, let’s look at the makeup of these articles. Market research, focus group data, and surveys usually consist of five or more sections.

• An abstract or hypothesis
• Explanation of the methods used
• Tests or experiments performed
• Summation and or discussion of the results
• A list of references or source materials

Read The Abstract

Since some of the research articles you find will not work for your purpose, you should always start with the abstract. It’s an overview of the data and explains the purpose of the study as well as the expected results. So you’ll know whether to include the article or move on to the next piece of research.

Take Good Notes

The next step– read the article from abstract to references. But be prepared! Your mind may wander when faced with numbers, statistics, and long-winded wording. So grab your highlighter and pen and start taking notes.

Depending on the space available, you can write your notes in the margin. If you’re in a time crunch, check out  Rev . We’ve designed a convenient application perfect for taking notes! Download our  Voice Recorder App  for free and read your notes out loud. You’ll get a 99% accurate transcription of your summary notes sent to your email or account with a simple tap. 

Research Hack:  As an overview, a research article may not include every insight from the participants, interviews, or market data. Take a look at the references. You may find some hidden gems that will help your strategy stand out.    

Outline Your Thoughts

You’ve made notes, sifted through the numbers and statistics; but, there’s still a ton of information. An outline will make your writing process much more efficient. Although each research article is relatively straight-forward, you want your summary to stay on strategy.

Write A Summary

Okay, you’re ready to condense someone else’s work. Rather than stress over grammar and length, take the pressure off by writing a rough draft. Use key points from your notes, REV transcriptions, your outline, and the research article’s sections as your guide. 

Identify The Goal And The Methods Used

Like the author’s abstract, the beginning of your summary should address the research article’s fundamental objective .  This section may also include critical details about demographics, customer behavior, or trends. When summarizing, consider three key questions. 

• What is the goal of the research?
• What methods did the author(s) use?
• Are potential obstacles to success listed?

Methods vary in market research. You may have focus groups ,  in-depth interviews , or online discussions. Depending on the reason for your summary, the raw audio or video clips used in the study may hold nuggets. If full transcripts aren’t available, save time by uploading the clips to Rev. Our human transcription service costs $1.50 per minute, and we offer a 99% accuracy guarantee. We also offer a more cost-effective A.I. speech-to-text solution for only $0.25 per minute .

Describe The Observations

The experiment is the “meat” of the research. In your own words, briefly explain what the author(s) observed as the testing played out in real-time. You can talk about the time it took participants to complete tasks or directives. Were they excited about the client’s brand or disinterested? Basically, you’re recapping the participant’s reactions. 

Discuss The Outcome

As with any study, the results make or break the goal of the research. Was the test successful? Was anyone surprised by the outcome, or were there any unexpected developments? Pay careful attention to detail as you layout all conclusions reached by the author(s).

Article Summary Quick Tips: Do This, Not That

Is your head spinning yet? You can simplify the editing process by following these technical takeaways.

• Be Careful Not To Draw Your Own Conclusions:  You are summarizing the results of the research. The last thing you want to do is editorialize your summary. To avoid this, use the third-person point of view and present tense.  
• Keep Your Copy Clean And Free Of Errors:  Reread your text. Eliminate words like “that,” “in fact,” “however,” and adverbs. Make sure your summary is accurate. Then, use free websites like  Hemingway App  or paid services such as  Grammarly  to check for grammar or spelling issues. 
• Watch For Plagiarism:  Unless you’re using a word coined by the researcher, paraphrase your text. If you notice similar wording in your summary, reread the article so you can explain the data in your own words.
• Cite Your Sources: Steer clear of directly quoting the research. It’s best to paraphrase the data and reference the source using: the name of the university, the name of the journal and year of publication, or the name of the researcher, team, or society and year of study.  

Finalize Your Article Summary

Remember, you want your summary to be clear, straight-forward, and compelling. The market research article or study you’ve chosen may prove vital to you or your client’s business strategy and brand analysis. Take your time. Read and reread your summary. Make sure it’s representative of the research. And always triple-check your text for technical and factual accuracy.

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CPS Online Library Research Guide (UNH Manchester Library): Summarize an Article

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Summarize or Analyze an Article

Perhaps your instructor has given you an article, or you have found one on your own. The assignment is to summarize or analyze the article. Summarizing and analyzing are not the same activity. Below are outlined the steps to take in fulfilling each type of assignment.

 Summarize an Article

A summary is a report of author’s viewpoint. A summary is rewriting what you have read in your own words. One can think of the summary as the short version of the original writing. You should tell the reader what were the main and important points of the writing. Your summary should include the thesis or the main argument of the paper. In the summary, you should not include your opinion or what you think the author is trying to imply by writing it. It should only focus on what the author has written. Summary should also not include any kind of evaluation by the reader. You should not write what you think are the author’s strong or weak arguments.

One of the other important information the summary should include is the name of the book or article, the author’s name and the publication information. The publication information is when that piece of writing was first published (Date or year) and where was it published? This information usually goes in the introductory paragraph which is also going to include the thesis statement of the writing you have read.

The summary should also be formal. You should not address the author by their first name; use only their last name of the author. It is typed and usually only one paragraph depending on what you are writing about. I have only listed some of the most common factors that need to be included in the summary. Your instructor could give you a different structure they want you to follow and other guidelines.

What Your Summary Should Address

A brief paragraph describing and informing the reader on three or more of the following elements:

• Who: those involved
• What: the event or topic being covered
• When: time, period, era, night or day
• Where: the location, distance, place
• Why: the cause or causes
• How: the process(es)

 Analyze an Article

An analysis is breaking a large topic into smaller pieces to better understand the subject. In an analysis you are not telling the reader about the main viewpoints of the author or what the writing is about, it is examining the structure and the details of the writing. You break the story into smaller parts to understand it better. Many instructors do not want you to express your opinion about the subject discussed in the paper. You can only give your opinion on how well the author did to convince the reader.

The first paragraph should be the introductory paragraph and it should include the title, author’s name, and publication details. You can also give the reader some background information on the subject being discussed in the writing and then give the thesis statement of the paper. First paragraph can also have a short summary about the paper.

In your analysis paper, you should address what is the main argument that the author is making and how well do they support the argument. The other factor to address is how reliable are the sources, and the authority that the author cites to make their argument strong. An analysis paper can also include the strengths and weaknesses of the paper and how they affect the argument being made by the author. You should also examine the tools like statistics, examples or citing of an authority to analyze the author’s reasoning for writing the paper. The other points one could address in their analysis paper are does the author address the opposition’s view point and does he/she attempts to refute it. Many instructors do not want you to express your opinion about the subject discussed in the paper. You can only give your opinion on how well the author did to convince the reader.  However, depending on your class level and your instructor it might be different and you might be allowed to express your opinion on the subject matter and tell whether you agree or disagree with the author.

What Your Analysis Should Address

Examine the summary elements described above in order to look for their meaning in the following contexts:

• Relationships, trends, patterns
• Roles of people, places, objects, situations
• Consequences or results of events, decisions and processes
• Causes and their effects
• Advantages and disadvantages/ gains and losses
• Strengths and weaknesses

Adapted from: The Writing Center . College of the Sequoias

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How to Summarize an Article

Last Updated: February 23, 2023 Fact Checked

This article was co-authored by Richard Perkins and by wikiHow staff writer, Danielle Blinka, MA, MPA . Richard Perkins is a Writing Coach, Academic English Coordinator, and the Founder of PLC Learning Center. With over 24 years of education experience, he gives teachers tools to teach writing to students and works with elementary to university level students to become proficient, confident writers. Richard is a fellow at the National Writing Project. As a teacher leader and consultant at California State University Long Beach's Global Education Project, Mr. Perkins creates and presents teacher workshops that integrate the U.N.'s 17 Sustainable Development Goals in the K-12 curriculum. He holds a BA in Communications and TV from The University of Southern California and an MEd from California State University Dominguez Hills. There are 8 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 743,696 times.

You might summarize an article as part of an assignment or to better understand the author’s ideas. An article summary provides an overview of the author’s thesis, purpose, and main ideas. Before you start your summary, read the article several times and make notes in the margins. Then, write a first draft that summarizes the article effectively. Finally, get feedback on your article and make revisions to finalize it.

Reading the Article

• If you have any questions, ask your instructor to get clarification.

• Highlight or underline the thesis, research question, or purpose.
• Mark the supporting points.
• Highlight the section headings.
• Note the method of study, if there is one.
• Highlight the findings, conclusions, or results.

• If possible, read it aloud to help you process the information.
• Reading the article several times will help you get a better understanding of the ideas. It’s difficult to fully understand an article on a first reading.

• It’s okay to write short phrases and fragments rather than full sentences.

Tip: Making notes in your own words will help you avoid plagiarism once you sit down to write your summary.

• You might write, “Lopez asserts that homework helps students retain more knowledge based on exam scores and self-reporting.”

Drafting a Summary

• You might say, “Inez Lopez is a former high school educator who now teaches curriculum planning as a researching professor. Her article entitled “Homework Smarts: Why Kids Need Homework” discusses why students benefit from regular homework assignments. Lopez also differentiates between effective homework and busywork, which helps educators change their lessons for the better.”

• For instance, you’d write, “Lopez argues homework is necessary to support in-class instruction because students retain more information, the class covers more curriculum, and students get more one-on-one attention in class.”

• A short summary is 1 page or shorter. For a short summary, you’ll write either 1 long paragraph or an introduction, a body paragraph, and a conclusion.
• Write, “According to Lopez, students who complete homework assignments for their core classes perform better academically.”

Tip: Summaries are usually about 1/3 of the length of the original article. You’ll only write a short summary if your original article was no longer than 3 pages long.

• If your summary is longer than 1 page, it’s considered a longer summary.
• You might write, “In her study, Lopez compared 2 different classrooms at the same high school, one that had homework and one that didn’t. Lopez asserts that students who completed homework assignments performed better academically.”

• You might write, “To support her claims, Lopez explains that students who did their homework scored 40% higher on exams, participated in class at a higher rate than students who weren’t assigned homework, and completed academic units 30% faster than classes that didn’t do homework.”

• For instance, you’d write, “In her research, Lopez studied two classes at the same high school. Both classes had a similar demographic and socioeconomic makeup and were provided the same academic supports. The control classroom did not receive homework, while the experimental classroom did. Lopez tracked the students’ homework completion rate, assignment scores, class participation, and progress through the curricula. Additionally, she conducted short student surveys after each exam.”

• You could write, “Lopez collected data such as student scores, number of incidences of class participation, and rate of lesson progression. Additionally, she asked students to rate their confidence, understanding of the material, and readiness to move on to the next unit on a survey after each exam. Based on her data, Lopez concluded that students progress as much as 30% faster if they complete daily homework assignments. To improve academic performance, Lopez recommends that teachers in core subjects assign homework every night.”

• Write something like, “According to Lopez, students are able to retain information and progress rapidly if they’re required to do homework. Her work provides teachers with a tool to promote academic success and advice on how to use homework effectively to help students.”

Making Your Summary Effective

• Your summary doesn’t need to be exact in length. As long as it’s about 1/3 of the article’s length, it should be sufficient.

Variation: If your assignment sheet lists a different length, always do as your instructor asks. For instance, your instructor might give you a word count goal of 1,500 words. If this is the case, follow their instructions.

• You’d write, "Lopez believes," "Lopez finds that," and "Lopez argues." It’s also okay to use pronouns. You might write, “She goes on to say,” “She further asserts,” or “She refutes this idea.”

Warning: Copying phrases or sentences from the original article is plagiarism. If you’re summarizing the article as part of an assignment, you will likely lose credit if you don’t restate the ideas in your own words.

Finalizing Your Summary

• For instance, ask your classmate, a writing tutor, or your teacher to give you feedback.

• You may want to do several rounds of revisions depending on the purpose of your assignment. If you’re writing this summary for a grade, make sure your final product is your best work.

• Ask someone else to proofread your paper for you if you can. Then, make changes if they spot any errors.

• Don’t include any of your own ideas, analysis, or opinions in a summary. Focus solely on the original author’s ideas.

Expert Q&A

• Make sure you follow all of your instructor’s directions so you get full credit. Thanks Helpful 0 Not Helpful 0

• Make sure you put all of the author’s ideas in your own words so you don’t accidentally plagiarize. Thanks Helpful 0 Not Helpful 1

Sample Summaries

You Might Also Like

• ↑ https://libguides.randolph.edu/summaries
• ↑ Richard Perkins. Writing Coach & Academic English Coordinator. Expert Interview. 1 September 2021.
• ↑ https://www.kibin.com/essay-writing-blog/how-to-summarize-an-article-the-smart-way/
• ↑ https://www.trentu.ca/academicskills/how-guides/how-write-university/how-approach-any-assignment/writing-article-summaries
• ↑ https://www.scribbr.com/dissertation/introduction-structure/
• ↑ https://otis.libguides.com/mla_citations/in-text
• ↑ https://writingcenter.unc.edu/tips-and-tools/revising-drafts/
• ↑ https://owl.purdue.edu/owl/general_writing/the_writing_process/proofreading/steps_for_revising.html

About This Article

To summarize an article, start by introducing the article title and the author’s full name so the reader knows what you’re referring to. Then, give a brief overview and explanation of the topic of the article, which will either be the author’s argument or the main premise of their research. Next, outline the points they use to back up their research, but avoid direct quotations to keep your summary brief. Finally, state the author’s conclusions before going back to make sure everything you stated in your summary matches up with the original article. For tips on how to use author tags to avoid plagiarizing in a summary, read on! Did this summary help you? Yes No

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How to Summarize a Research Article

Research articles use a standard format to clearly communicate information about an experiment. A research article usually has seven major sections: Title, Abstract,

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In this study, a qualitative analysis of 276 first-year Japanese university science major responses to plagiarism to deconstruct prevailing generalizations regarding the incidence of plagiarism by Japanese university students. These students were enrolled in a compulsory yearlong English academic writing course. While utilizing a contextualized incident, rather than generalized statements, to gain a more thorough understanding of students’ perceptions of plagiarism, this study also seeks to address the current imbalances in English-language analyses on plagiarism in Japan which fail to incorporate Japanese-language sources and studies. In contrast to previous research based on hypotheses of cultural conditioning which assume students are ambivalent towards plagiarism, the students surveyed displayed a complex awareness of the educational and societal issues that frame occurrences of plagiarism and a desire to receive effective training in academic writing techniques, similar to their native English-speaking counterparts. Finally, the paper makes suggestions for pedagogy that empowers students with tools to critically navigate the dominant academic world.

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Sasima Charubusp

Plagiarism has become a critical issue in the academic context around the world including Thailand. Plagiarism is generally considered a form of cheating, and some studies of Thai students suggested that this problem is rooted in Thai cultural patterns, which accept cheating as a common practice (Young 2013). Generally, three plagiarism avoidance approaches have been taught: techniques for direct quotation, paraphrasing and summarizing. However, especially paraphrasing and summarizing are subtle skills, mastery of which requires excellent literacy skills and extended practice, but in actual classes, these techniques are always presented as an abridged part of an EAP course. Apparently, such courses do not prevent plagiarism; students always continue to be penalized for copying phrases from sources because they still cannot paraphrase and summarize properly. Some plagiarism detection software packages are also used as punitive tools, which has the effect of blaming students for the problem instead of helping them learn to avoid it (William 2002). In fact, it is unreasonable to expect control of such demanding techniques after a brief lecture and limited practice. Moreover, plagiarism by EFL students may actually be unintended, and the cause may go beyond deliberate dishonesty (Sivell 2013). It could involve other related cognitive factors such as a limited level of language proficiency, lack of clear instruction, insufficient practice, and unawareness of the regulations around correct techniques for writing from sources. This study examines how Thai students and teachers perceived plagiarism, what characteristics of each group hinder plagiarism avoidance, and how being reproached for academic dishonesty affects students’ motivation for language learning.

European Scientific Journal ESJ

In academic writing, students need to incorporate outside sources. Incorporated sources involve summarizing, paraphrasing, and quoting. Depending on why the source is being used, one method may be more appropriate than another. All three methods of incorporating sources require citations. Sources can be utilized for various purposes: presenting facts or statistics, serving as evidence to support an argument, lending authority to an argument or idea, i.e., authoritative discourse, providing examples and illustrations, stating an opposing viewpoint, and so on. For every source, students must determine the purpose of that source and how to best incorporate it into their writing. Both aspects are crucial to effectively integrating sources throughout the paper. Effective integrating of the sources in the EFL writing is a demanding task. Therefore, the aim of the study is to find out the difficulties and challenges the students face while using outside sources in academic writing. In achieving the aim of this study, twenty five third-year students were given an assignment to write an essay in which they had to incorporate an outside source. The study was conducted in the first semester in the academic year 2016-2017. Discussion of findings, implications and recommendations were presented after analyzing the students' works.

Rebekka Tunombili

ahmet benzer , Sümeyye Konuk , ZEYNEB ÖREN , Ayşegül SEFER

Summarizing is restating the most important ideas from an original text briefly. Students often need summary writing skill along the education life since it provides understanding and remembering the reading material. This study aims to apply book summary writing strategy which is based on in-class implementations, and to develop the students book summary writing skill with education. With this aim, to determine students' book summary writing skill and analyze the development of their book summary writing skill, researchers have developed a book summary writing strategy and also a rubric to evaluate the written summaries. While developing the book summary writing strategy, researchers have conducted the study with 44 university students studying at Turkish language teaching department in the third grade. The study lasted for 11 weeks, and the education period is implemented as one week education and one week summary writing implementation. The strategy and rubric have been updated with the students' views, feedbacks and researchers' notes during the education process. In the study, of the qualitative research methods, grounded theory was used. At the end of the study, it is stated that students have been successful in writing a book summary, tagging and taking notes, isolating from trivial details and also, they could write the summaries in a shorter time. The study findings revealed the usable book summary writing strategy and the rubric for book summary evaluation.

University of the Western Cape,[Online]

Nelleke Bak

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Tiny but mighty: The Phi-3 small language models with big potential

• Sally Beatty

Sometimes the best way to solve a complex problem is to take a page from a children’s book. That’s the lesson Microsoft researchers learned by figuring out how to pack more punch into a much smaller package.

Last year, after spending his workday thinking through potential solutions to machine learning riddles, Microsoft’s Ronen Eldan was reading bedtime stories to his daughter when he thought to himself, “how did she learn this word? How does she know how to connect these words?” 

That led the Microsoft Research machine learning expert to wonder how much an AI model could learn using only words a 4-year-old could understand – and ultimately to an innovative training approach that’s produced a new class of more capable small language models that promises to make AI more accessible to more people.

Large language models (LLMs) have created exciting new opportunities to be more productive and creative using AI.  But their size means they can require significant computing resources to operate. 

While those models will still be the gold standard for solving many types of complex tasks, Microsoft has been developing a series of small language models (SLMs) that offer many of the same capabilities found in LLMs but are smaller in size and are trained on smaller amounts of data.

The company announced today the Phi-3 family of open models , the most capable and cost-effective small language models available. Phi-3 models outperform models of the same size and next size up across a variety of benchmarks that evaluate language, coding and math capabilities, thanks to training innovations developed by Microsoft researchers.

Microsoft is now making the first in that family of more powerful small language models publicly available: Phi-3-mini , measuring 3.8 billion parameters, which performs better than models twice its size, the company said.

Starting today, it will be available in the Microsoft Azure AI Model Catalog and on Hugging Face , a platform for machine learning models, as well as Ollama , a lightweight framework for running models on a local machine. It will also be available as an NVIDIA NIM  microservice with a standard API interface that can be deployed anywhere. 

Microsoft also announced additional models to the Phi-3 family are coming soon to offer more choice across quality and cost. Phi-3-small (7 billion parameters) and Phi-3-medium (14 billion parameters) will be available in the Azure AI Model Catalog and other model gardens shortly. 

Small language models are designed to perform well for simpler tasks, are more accessible and easier to use for organizations with limited resources and they can be more easily fine-tuned to meet specific needs. 

“What we’re going to start to see is not a shift from large to small, but a shift from a singular category of models to a portfolio of models where customers get the ability to make a decision on what is the best model for their scenario,” said Sonali Yadav, principal product manager for Generative AI at Microsoft.

“Some customers may only need small models, some will need big models and many are going to want to combine both in a variety of ways,” said Luis Vargas, vice president of AI at Microsoft.

Choosing the right language model depends on an organization’s specific needs, the complexity of the task and available resources. Small language models are well suited for organizations looking to build applications that can run locally on a device (as opposed to the cloud) and where a task doesn’t require extensive reasoning or a quick response is needed.

Large language models are more suited for applications that need orchestration of complex tasks involving advanced reasoning, data analysis and understanding of context.  

Small language models also offer potential solutions for regulated industries and sectors that encounter situations where they need high quality results but want to keep data on their own premises, said Yadav. 

Vargas and Yadav are particularly excited about the opportunities to place more capable SLMs on smartphones and other mobile devices that operate “at the edge,” not connected to the cloud. (Think of car computers, PCs without Wi-Fi, traffic systems, smart sensors on a factory floor, remote cameras or devices that monitor environmental compliance.) By keeping data within the device, users can “minimize latency and maximize privacy,” said Vargas. 

Latency refers to the delay that can occur when LLMs communicate with the cloud to retrieve information used to generate answers to users prompts. In some instances, high-quality answers are worth waiting for while in other scenarios speed is more important to user satisfaction.

Because SLMs can work offline, more people will be able to put AI to work in ways that haven’t previously been possible, Vargas said. 

For instance, SLMs could also be put to use in rural areas that lack cell service. Consider a farmer inspecting crops who finds signs of disease on a leaf or branch. Using a SLM with visual capability, the farmer could take a picture of the crop at issue and get immediate recommendations on how to treat pests or disease.  

“If you are in a part of the world that doesn’t have a good network,” said Vargas, “you are still going to be able to have AI experiences on your device.”    

The role of high-quality data  

Just as the name implies, compared to LLMs, SLMs are tiny, at least by AI standards. Phi-3-mini has “only” 3.8 billion parameters – a unit of measure that refers to the algorithmic knobs on a model that help determine its output. By contrast, the biggest large language models are many orders of magnitude larger.

The huge advances in generative AI ushered in by large language models were largely thought to be enabled by their sheer size. But the Microsoft team was able to develop small language models that can deliver outsized results in a tiny package. This breakthrough was enabled by a highly selective approach to training data – which is where children’s books come into play.

To date, the standard way to train large language models has been to use massive amounts of data from the internet. This was thought to be the only way to meet this type of model’s huge appetite for content, which it needs to “learn” to understand the nuances of language and generate intelligent answers to user prompts. But Microsoft researchers had a different idea.

“Instead of training on just raw web data, why don’t you look for data which is of extremely high quality?” asked Sebastien Bubeck, Microsoft vice president of generative AI research who has led the company’s efforts to develop more capable small language models. But where to focus?

Inspired by Eldan’s nightly reading ritual with his daughter, Microsoft researchers decided to create a discrete dataset starting with 3,000 words – including a roughly equal number of nouns, verbs and adjectives. Then they asked a large language model to create a children’s story using one noun, one verb and one adjective from the list – a prompt they repeated millions of times over several days, generating millions of tiny children’s stories.

They dubbed the resulting dataset “TinyStories” and used it to train very small language models of around 10 million parameters. To their surprise, when prompted to create its own stories, the small language model trained on TinyStories generated fluent narratives with perfect grammar.

Next, they took their experiment up a grade, so to speak. This time a bigger group of researchers used carefully selected publicly-available data that was filtered based on educational value and content quality to train Phi-1. After collecting publicly available information into an initial dataset, they used a prompting and seeding formula inspired by the one used for TinyStories, but took it one step further and made it more sophisticated, so that it would capture a wider scope of data. To ensure high quality, they repeatedly filtered the resulting content before feeding it back into a LLM for further synthesizing. In this way, over several weeks, they built up a corpus of data large enough to train a more capable SLM.

“A lot of care goes into producing these synthetic data,” Bubeck said, referring to data generated by AI, “looking over it, making sure it makes sense, filtering it out. We don’t take everything that we produce.” They dubbed this dataset “CodeTextbook.” 

The researchers further enhanced the dataset by approaching data selection like a teacher breaking down difficult concepts for a student. “Because it’s reading from textbook-like material, from quality documents that explain things very, very well,” said Bubeck, “you make the task of the language model to read and understand this material much easier.”

Distinguishing between high- and low-quality information isn’t difficult for a human, but sorting through more than a terabyte of data that Microsoft researchers determined they would need to train their SLM would be impossible without help from a LLM. 

“The power of the current generation of large language models is really an enabler that we didn’t have before in terms of synthetic data generation,” said Ece Kamar, a Microsoft vice president who leads the Microsoft Research AI Frontiers Lab, where the new training approach was developed. 

Starting with carefully selected data helps reduce the likelihood of models returning unwanted or inappropriate responses, but it’s not sufficient to guard against all potential safety challenges. As with all generative AI model releases, Microsoft’s product and responsible AI teams used a multi-layered approach to manage and mitigate risks in developing Phi-3 models.

For instance, after initial training they provided additional examples and feedback on how the models should ideally respond, which builds in an additional safety layer and helps the model generate high-quality results. Each model also undergoes assessment, testing and manual red-teaming, in which experts identify and address potential vulnerabilities.

Finally, developers using the Phi-3 model family can also take advantage of a suite of tools available in Azure AI  to help them build safer and more trustworthy applications.  

Choosing the right-size language model for the right task

But even small language models trained on high quality data have limitations. They are not designed for in-depth knowledge retrieval, where large language models excel due to their greater capacity and training using much larger data sets.

LLMs are better than SLMs at complex reasoning over large amounts of information due to their size and processing power. That’s a function that could be relevant for drug discovery, for example, by helping to pore through vast stores of scientific papers, analyze complex patterns and understand interactions between genes, proteins or chemicals. 

“Anything that involves things like planning where you have a task, and the task is complicated enough that you need to figure out how to partition that task into a set of sub tasks, and sometimes sub-sub tasks, and then execute through all of those to come with a final answer … are really going to be in the domain of large models for a while,” said Vargas.

Based on ongoing conversations with customers, Vargas and Yadav expect to see some companies “offloading” some tasks to small models if the task is not too complex. 

For instance, a business could use Phi-3 to summarize the main points of a long document or extract relevant insights and industry trends from market research reports. Another organization might use Phi-3 to generate copy, helping create content for marketing or sales teams such as product descriptions or social media posts. Or, a company might use Phi-3 to power a support chatbot to answer customers’ basic questions about their plan, or service upgrades.    

Internally, Microsoft is already using suites of models, where large language models play the role of router, to direct certain queries that require less computing power to small language models, while tackling other more complex requests itself.

“The claim here is not that SLMs are going to substitute or replace large language models,” said Kamar. Instead, SLMs “are uniquely positioned for computation on the edge, computation on the device, computations where you don’t need to go to the cloud to get things done. That’s why it is important for us to understand the strengths and weaknesses of this model portfolio.”

And size carries important advantages. There’s still a gap between small language models and the level of intelligence that you can get from the big models on the cloud, said Bubeck. “And maybe there will always be a gap because you know – the big models are going to keep making progress.”

Related links:

• Read more: Introducing Phi-3, redefining what’s possible with SLMs
• Learn more: Azure AI
• Read more: Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Top image: Sebastien Bubeck, Microsoft vice president of Generative AI research who has led the company’s efforts to develop more capable small language models. (Photo by Dan DeLong for Microsoft)

• Open access
• Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
• Daudi Jjingo 10 ,
• Katherine Littler 9 ,
• Daniela Paolotti 11 &
• Effy Vayena 12  

BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Peer Review reports

Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Lay summary, skeletal stem and progenitor cells in bone development and repair.

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Dana Trompet, Seppe Melis, Andrei S Chagin, Christa Maes, Skeletal stem and progenitor cells in bone development and repair, Journal of Bone and Mineral Research , 2024;, zjae069, https://doi.org/10.1093/jbmr/zjae069

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Bone development, growth, and repair are complex processes involving various cell types and interactions, with central roles played by skeletal stem and progenitor cells. Recent research brought new insights into the skeletal precursor populations that mediate intramembranous and endochondral bone development. Later in life, many of the cellular and molecular mechanisms determining development are reactivated upon fracture, with powerful trauma-induced signaling cues triggering a variety of postnatal skeletal stem/progenitor cells (SSPCs) residing near the bone defect. Interestingly, in this injury context, the current evidence suggests that the fates of both SSPCs and differentiated skeletal cells can be considerably flexible and dynamic, and that multiple cell sources can be activated to operate as functional progenitors generating chondrocytes and/or osteoblasts. The combined implementation of in vivo lineage tracing, cell surface marker-based selection, single-cell molecular analyses, and high-resolution in situ imaging has strongly improved our insights into the diversity and roles of developmental and reparative stem/progenitor subsets, while also unveiling the complexity of their dynamics, hierarchies, and relationships. Albeit incompletely understood at present, findings supporting lineage flexibility and possibly plasticity among sources of osteogenic cells challenge the classical dogma of a single primitive, self-renewing, multipotent stem cell driving bone tissue formation and regeneration from the apex of a hierarchical and strictly unidirectional differentiation tree. We here review the state of the field and the newest discoveries in the origin, identity, and fates of skeletal progenitor cells during bone development and growth, discuss the contributions of adult SSPC populations to fracture repair, and reflect on the dynamism and relationships among skeletal precursors and differentiated cell lineages. Further research directed at unraveling the heterogeneity and capacities of SSPCs, as well as the regulatory cues determining their fate and functioning, will offer vital new options for clinical translation toward compromised fracture healing and bone regenerative medicine.

Skeletal progenitor cells are crucial for bone development and growth, as they provide the cellular building blocks (chondrocytes and osteoblasts) that form the cartilage and bone tissues that the skeleton is composed of. In adult life, the occurrence of a bone fracture reactivates similar tissue-forming mechanisms, starting with the trauma triggering various postnatal skeletal stem/progenitor cells (SSPCs) residing near the bone defect to divide and migrate. These cells subsequently generate functional fracture-repairing cells by differentiating into mature chondrocytes and/or osteoblasts. In recent years, the combined use of various advanced research approaches and new techniques has strongly improved our insights into the origin, identity, fates, and roles of developmental and reparative skeletal stem cells and progenitor subsets. Concomitantly, this research also unveiled considerable complexity in their dynamics, diversity, hierarchies, and relationships, which is incompletely understood at present. In this review, we discuss the state of the field and the newest discoveries in the identity and roles of skeletal stem and progenitor cells mediating bone development, growth, and repair. Further research on these cell populations, including determining their exact nature, fate, and functioning, and how they can be harvested and regulated, is critical to develop new treatments for non-healing fractures.

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Doctors and researchers have proposed various theories to try to explain these trends. One is that greater social acceptance of trans people has enabled people to seek these therapies. Another is that teenagers are being influenced by the popularity of searching and experimenting around identity. A third is that the rise of teen mental health issues may be contributing to gender dysphoria. In her report, Cass is skeptical of broad generalizations in the absence of clear evidence; these are individual children and adolescents who take their own routes to who they are.

Some activists and medical practitioners on the left have come to see the surge in requests for medical transitioning as a piece of the new civil rights issue of our time — offering recognition to people of all gender identities. Transition through medical interventions was embraced by providers in the United States and Europe after a pair of small Dutch studies showed that such treatment improved patients’ well-being. But a 2022 Reuters investigation found that some American clinics were quite aggressive with treatment: None of the 18 U.S. clinics that Reuters looked at performed long assessments on their patients, and some prescribed puberty blockers on the first visit.

Unfortunately, some researchers who questioned the Dutch approach were viciously attacked. This year, Sallie Baxendale, a professor of clinical neuropsychology at the University College London, published a review of studies looking at the impact of puberty blockers on brain development and concluded that “critical questions” about the therapy remain unanswered. She was immediately attacked. She recently told The Guardian, “I’ve been accused of being an anti-trans activist, and that now comes up on Google and is never going to go away.”

As Cass writes in her report, “The toxicity of the debate is exceptional.” She continues, “There are few other areas of health care where professionals are so afraid to openly discuss their views, where people are vilified on social media and where name-calling echoes the worst bullying behavior.”

Cass focused on Britain, but her description of the intellectual and political climate is just as applicable to the U.S., where brutality on the left has been matched by brutality on the right, with crude legislation that doesn’t acknowledge the well-being of the young people in question. In 24 states Republicans have passed laws banning these therapies, sometimes threatening doctors with prison time if they prescribe the treatment they think is best for their patients.

The battle lines on this issue are an extreme case, but they are not unfamiliar. On issue after issue, zealous minorities bully and intimidate the reasonable majority. Often, those who see nuance decide it’s best to just keep their heads down. The rage-filled minority rules.

Cass showed enormous courage in walking into this maelstrom. She did it in the face of practitioners who refused to cooperate and thus denied her information that could have helped inform her report. As an editorial in The BMJ puts it, “Despite encouragement from N.H.S. England,” the “necessary cooperation” was not forthcoming. “Professionals withholding data from a national inquiry seems hard to imagine, but it is what happened.”

Cass’s report does not contain even a hint of rancor, just a generous open-mindedness and empathy for all involved. Time and again in her report, she returns to the young people and the parents directly involved, on all sides of the issue. She clearly spent a lot of time meeting with them. She writes, “One of the great pleasures of the review has been getting to meet and talk to so many interesting people.”

The report’s greatest strength is its epistemic humility. Cass is continually asking, “What do we really know?” She is carefully examining the various studies — which are high quality, which are not. She is down in the academic weeds.

She notes that the quality of the research in this field is poor. The current treatments are “built on shaky foundations,” she writes in The BMJ. Practitioners have raced ahead with therapies when we don’t know what the effects will be. As Cass tells The BMJ, “I can’t think of another area of pediatric care where we give young people a potentially irreversible treatment and have no idea what happens to them in adulthood.”

She writes in her report, “The option to provide masculinizing/feminizing hormones from age 16 is available, but the review would recommend extreme caution.” She does not issue a blanket, one-size-fits-all recommendation, but her core conclusion is this: “For most young people, a medical pathway will not be the best way to manage their gender-related distress.” She realizes that this conclusion will not please many of the young people she has come to know, but this is where the evidence has taken her.

You can agree or disagree with this or that part of the report, and maybe the evidence will look different in 10 years, but I ask you to examine the integrity with which Cass did her work in such a treacherous environment.

In 1877 a British philosopher and mathematician named William Kingdon Clifford published an essay called “ The Ethics of Belief .” In it he argued that if a shipowner ignored evidence that his craft had problems and sent the ship to sea having convinced himself it was safe, then of course we would blame him if the ship went down and all aboard were lost. To have a belief is to bear responsibility, and one thus has a moral responsibility to dig arduously into the evidence, avoid ideological thinking and take into account self-serving biases. “It is wrong always, everywhere, and for anyone, to believe anything upon insufficient evidence,” Clifford wrote. A belief, he continued, is a public possession. If too many people believe things without evidence, “the danger to society is not merely that it should believe wrong things, though that is great enough; but that it should become credulous, and lose the habit of testing things and inquiring into them; for then it must sink back into savagery.”

Since the Trump years, this habit of not consulting the evidence has become the underlying crisis in so many realms. People segregate into intellectually cohesive teams, which are always dumber than intellectually diverse teams. Issues are settled by intimidation, not evidence. Our natural human tendency is to be too confident in our knowledge, too quick to ignore contrary evidence. But these days it has become acceptable to luxuriate in those epistemic shortcomings, not to struggle against them. See, for example, the modern Republican Party.

Recently it’s been encouraging to see cases in which the evidence has won out. Many universities have acknowledged that the SAT is a better predictor of college success than high school grades and have reinstated it. Some corporations have come to understand that while diversity, equity and inclusion are essential goals, the current programs often empirically fail to serve those goals and need to be reformed. I’m hoping that Hilary Cass is modeling a kind of behavior that will be replicated across academia, in the other professions and across the body politic more generally and thus save us from spiraling into an epistemological doom loop.

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David Brooks has been a columnist with The Times since 2003. He is the author, most recently,  of “How to Know a Person: The Art of Seeing Others Deeply and Being Deeply Seen.” @ nytdavidbrooks

Bureau of the Budget and Management Research

You are here, fiscal 2025 executive summary available online, wednesday may 1st, 2024.

The Fiscal 2025 Executive Summary of the Board of Estimates Recommendations  is now available online. The recommended budget totals $4.2 billion, including $3.5  billion for operating  expenditures and $732.5  million for investment in capital projects. The recommended General  Fund budget is $2.3 billion, an increase of 5.9% from the Fiscal 2024 Adopted Budget. 

The budget will be introduced to the City Council on Monday, May 6 at 5 pm and budget hearings will begin Thursday, May 23 at 9 am. Details on the City Council budget hearings can be found on their website .

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Weekly Housing Trends View—Data Week Ending April 27, 2024

Our research team releases monthly housing trends reports . These reports break down inventory metrics like the number of active listings and the pace of the market. In addition, we continue to give readers more timely weekly updates, an effort that began in response to the rapid changes in the economy and housing as a result of the COVID-19 pandemic. Generally, you can look forward to a Weekly Housing Trends View, weekly housing data on Thursdays, and monthly video updates from our economists. Here’s what the housing market looked like over the past week.

What this week’s data means

While the drop in the median list price offers hope for prospective homebuyers, the recent increase in mortgage rates may dampen spirits. Small but meaningful gains in affordability made since the year began have been eroded by this new uptick. Consequently, homes are now staying on the market slightly longer compared with the same period last year, indicating a declining interest in home purchases.

Many potential buyers are opting to postpone their plans in anticipation of lower mortgage rates. However, such a scenario seems unlikely to happen soon, as the Federal Reserve Bank believes the restrictive monetary policy needs more time to work.

Meanwhile, sellers are still putting homes up for sale at a higher rate than last year. Yet, if mortgage rates continue to climb, sellers might reconsider their plans, especially considering that nearly three-quarters of them are also in the market to buy a home .

Key findings

• The median listing price slipped, dipping 0.5% compared with last year

This past week, the prices for homes on the market continued to slip, though at a lower rate. As more sellers enter the housing market, especially in the South, there has been a shift in the types of homes available.

Smaller, more affordable listings are taking the pressure off of the overall median price, which is good news for shoppers looking for these types of homes. This could have contributed to the jump in first-time homebuying in March . However, on a per-square-foot basis, home prices are still rising ( by 3.8% versus last year in April ). 

• New listings—a measure of sellers putting homes up for sale—were up this week, by 10.4% from one year ago

Since February, the number of homes newly listed for sale has surpassed the year-ago pace by double digits with the exception of a few weeks around this year’s spring holidays.

As reported in the Realtor.com April housing report , newly listed homes trailed behind every prior year except for the pandemic-induced starting point of 2020 and the record low of 2023.

Although the number of homes for sale remains low, it is growing, prompting sellers to think about how to best help their property stand out. In markets with more electric-vehicle drivers, we see a similarly larger share of sellers marketing EV-friendly features of for-sale homes .

• Active inventory increased, with for-sale homes 33.3% above year-ago levels

For the 25th straight week, there were more homes listed for sale versus the prior year, giving homebuyers more options. As mortgage rates have climbed to new 2024 highs ,  we could see sellers adjust their plans, since nearly three-quarters of potential sellers also plan to buy a home . However, the long buildup to listing—80% have been thinking about selling for 1 to 3 years—could mean that this year’s sellers are less deterred by market fluctuations. 

• Homes spent 1 extra day on the market compared with this time last year

For the first time in nearly eight months, the typical home spent a slightly longer time on the market than the same week one year prior. The surge in mortgage rates, which exceeded 7% in April, is prompting potential buyers to delay their purchasing decisions. This reduced demand is contributing to a less competitive market, leading homes to spend more time on the market.

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Virtual summit executive summary – spring 2024.

Last Updated: May 1, 2024 | Read Time: 14 min

This year’s Spring HR & Compliance Virtual Summit, held April 17 & 18, highlighted best practices for measuring manager effectiveness, ADA accommodations, employment law developments, employee engagement tips, and so much more. Our speakers shared insights with nearly 10,000 HR leaders over the two-day event.

Did you miss any of the live sessions? Don’t worry, we’ve got you covered! Check out our executive summary for top takeaways.

Trends and Best Practices for Measuring Manager Effectiveness

In this session, Andrea Derler and Thera Martens covered the essential metrics you should be measuring, including those readily available in Paycor, to gauge the effectiveness of your managers. Insights included:

• A Gartner study found only 27% of HR leaders believe their organizations’ managers are ready to lead through change.
• Only 38% of individual contributors want to become managers; the other 62% want to stay ICs (Visier).
• The 5 suggested manager metrics to track are: Manager engagement score; High performer resignation rate; Promotions rate; Objectives; Absence days per FTE.
• What effective managers do well: Hire well and onboard longer; Plan compensation intelligently; Actively retain top talent

Key Quote :

“It’s easy to get lost in the sea of metrics, and practices. But let’s never lose sight of the end goal: Helping managers be effective, or become more effective in their management of their business and their people.”

Charges, Claims, and Accommodations – Processes Matter!

Julie Pugh, Esq. led an engaging discussion that explained administrative charges and how to appropriately handle such claims with finesse. Highlights included:

• The pros and cons of using outside counsel, what to do with witness statements, whether or not to go to mediation, and what potential outcomes may be.
• An overview of the EEOC’s recent flurry of disability discrimination claims and the processes HR should follow when an accommodation request arises.
• The EEOC’s LEAD (Leadership for the Employment of Americans with Disabilities) initiative.

“How an employer responds to the administrative charge can have a critical impact on its legal exposure in any subsequent litigation, because the employer cannot later retract factual statements it makes in its response.”

Recent Developments in Employment Law

Attorney Merry Campbell’s session guided attendees on staying up to date on recent changes to federal and local employment law and discussed how to navigate the complex landscape of employment compliance. Takeaways included:

• The EEOC’s proposal to update guidance on workplace harassment to reflect significant court cases, the #MeToo movement, and remote workplace harassment.
• EEOC guidance on HR’s use of artificial intelligence including the fact that employers can be held accountable for third-party designed tools used for making employment decisions.
• FMLA eligibility for remote workers: If an employee works from home but their supervisor or department works out of a corporate office where there at least 50 employees within 75 miles, the employee is covered by the FMLA.

“Most diversity and inclusion policies are designed to create larger and more diverse pools of candidates rather than setting numerical quotas, and such policies are likely okay, but policies that are more rigid, for example, reserving a set percentage of leadership positions for women or minorities, could be more vulnerable.” 

Redefining Layoffs to Ensure Compliance and Compassion

Sarah Rodehorst, Chief Executive Officer of Onwards HR, focused on the importance of conducting employee layoffs with care. Session guidance included:

• How organizations can mitigate risk, reduce costs, and improve equity by simplifying employee separations in compliance with corporate policy and employment laws.
• Actionable strategies for taking compassionate and practical steps to employee separations.
• A framework for an improved separation process that is sensitive to the employee experience.

“During a layoff, it’s important to recognize that exiting employees can be brand ambassadors. You need to make a good final impression because a positive experience can help your brand, but a negative experience could go viral.” 

Exploring Employee Engagement and Retention in the Face of Technological Advancements

As the global talent shortage persists, attracting and retaining employees has become crucial, putting a burden on leaders to better engage and upskill their workforce. Paycor’s Director of Strategy, Alanna Perez, discussed the latest automation and technology trends affecting small and medium-sized businesses (SMBs). Takeaways included:

• While businesses continue to add jobs and Mar’24 employment stronger than expected (adding 303k jobs), the hire rate has dropped since Covid (3.7% vs 4.6%).
• Only 33% of employees are engaged today vs. 36% in 2020. And Gallup reports that there is nearly $1.9 TRILLION lost in productivity from not engaged or actively disengaged employees.
• AI-powered automation will shape in the economy in the coming decades, comparable to the emergence of the internet 30 years ago.

“If your leaders can articulate the mission and vision of the organization to their frontline employees, those employees will understand that the organization cares about them and is looking out for them.”

Mastering Gentelligence®: Understanding & Leveraging Generational Diversity in the Workplace

Megan W. Gerhardt, Ph.D., Professor of Management and Leadership at the Farmer School of Business at Miami University, developed Gentelligence to help organizations change how they approach generational differences. Insights surfaced in this session included:

• Discussion around the barriers that often prevent organizations and individuals from effective intergenerational collaboration.
• An assumption audit that pushed beyond lazy stereotypes to understand generational and age differences as a valuable and complex form of diversity.
• How to develop organizational cultures that normalize intergenerational learning and collaboration.
• How to have smarter intergenerational conversations using power questions.

“Deloitte found that only 6% of organizations ‘strongly agree’ that their leaders are equipped to lead a multigenerational organization.”

HR Diagnostics and Auditing: Compliance and Best Practices for a Stronger Business

This session, presented by Dr. Gia Wiggins, took attendees beyond basic reporting to truly diagnose the health of their HR practices and uncover opportunities for improvement. Topics discussed include:

• The Fundamentals: Mastering the core principles of HR diagnostics and auditing and demystifying the process and its potential benefits.
• Metrics That Matter: Identifying the key HR metrics to track, analyze, and use for pinpointing areas for improvement and highlighting successes.
• Auditing Best Practices: A step-by-step approach to conducting effective HR audits.
• Data-Driven Decision-Making: How to translate HR data into actionable insights that drive strategic HR initiatives and support overall business goals.

“Become a student of compliance and best practice needs for your company. Remediate things that need correction. Prioritize things that cause harm to the employees or company.”

How to Operationalize Culture & Behavior for Your Safety Program

Catherine Mattice, founder of Civility Partners, led an engaging discussion around ensuring your employees feel psychosocially safe. Highlights included:

• Defining and describing psychosocial safety as an inherent and basic human need.
• Dissecting the ways psychosocial safety influences behavior, performance, innovation and learning – and is paramount to inclusion initiatives.
• Identifying how to detect a lack of psychosocial safety in your workforce and construct clear and tangible solutions for improvement.
• Learning how to operationalize culture and behavior for your organization’s safety program.

“There is a strong relationship between psychological and physical safety. People who say they felt psychologically unsafe on the job were 80% more likely to report they had been injured at work, requiring medical attention or missed days of work.”

Even though our Spring Summit has concluded, we’re already starting to plan our next event coming in fall 2024! Stay tuned for more details.

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