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• How to Write Recommendations in Research | Examples & Tips

How to Write Recommendations in Research | Examples & Tips

Published on September 15, 2022 by Tegan George . Revised on July 18, 2023.

Recommendations in research are a crucial component of your discussion section and the conclusion of your thesis , dissertation , or research paper .

As you conduct your research and analyze the data you collected , perhaps there are ideas or results that don’t quite fit the scope of your research topic. Or, maybe your results suggest that there are further implications of your results or the causal relationships between previously-studied variables than covered in extant research.

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Table of contents

What should recommendations look like, building your research recommendation, how should your recommendations be written, recommendation in research example, other interesting articles, frequently asked questions about recommendations.

Recommendations for future research should be:

• Concrete and specific
• Supported with a clear rationale
• Directly connected to your research

Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.

Relatedly, when making these recommendations, avoid:

• Undermining your own work, but rather offer suggestions on how future studies can build upon it
• Suggesting recommendations actually needed to complete your argument, but rather ensure that your research stands alone on its own merits
• Using recommendations as a place for self-criticism, but rather as a natural extension point for your work

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There are many different ways to frame recommendations, but the easiest is perhaps to follow the formula of research question   conclusion  recommendation. Here’s an example.

Conclusion An important condition for controlling many social skills is mastering language. If children have a better command of language, they can express themselves better and are better able to understand their peers. Opportunities to practice social skills are thus dependent on the development of language skills.

As a rule of thumb, try to limit yourself to only the most relevant future recommendations: ones that stem directly from your work. While you can have multiple recommendations for each research conclusion, it is also acceptable to have one recommendation that is connected to more than one conclusion.

These recommendations should be targeted at your audience, specifically toward peers or colleagues in your field that work on similar subjects to your paper or dissertation topic . They can flow directly from any limitations you found while conducting your work, offering concrete and actionable possibilities for how future research can build on anything that your own work was unable to address at the time of your writing.

See below for a full research recommendation example that you can use as a template to write your own.

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If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

For a stronger dissertation conclusion , avoid including:

• Important evidence or analysis that wasn’t mentioned in the discussion section and results section
• Generic concluding phrases (e.g. “In conclusion …”)
• Weak statements that undermine your argument (e.g., “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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Home » Research Recommendations – Examples and Writing Guide

Research Recommendations – Examples and Writing Guide

Table of Contents

Research Recommendations

Definition:

Research recommendations refer to suggestions or advice given to someone who is looking to conduct research on a specific topic or area. These recommendations may include suggestions for research methods, data collection techniques, sources of information, and other factors that can help to ensure that the research is conducted in a rigorous and effective manner. Research recommendations may be provided by experts in the field, such as professors, researchers, or consultants, and are intended to help guide the researcher towards the most appropriate and effective approach to their research project.

Parts of Research Recommendations

Research recommendations can vary depending on the specific project or area of research, but typically they will include some or all of the following parts:

• Research question or objective : This is the overarching goal or purpose of the research project.
• Research methods : This includes the specific techniques and strategies that will be used to collect and analyze data. The methods will depend on the research question and the type of data being collected.
• Data collection: This refers to the process of gathering information or data that will be used to answer the research question. This can involve a range of different methods, including surveys, interviews, observations, or experiments.
• Data analysis : This involves the process of examining and interpreting the data that has been collected. This can involve statistical analysis, qualitative analysis, or a combination of both.
• Results and conclusions: This section summarizes the findings of the research and presents any conclusions or recommendations based on those findings.
• Limitations and future research: This section discusses any limitations of the study and suggests areas for future research that could build on the findings of the current project.

How to Write Research Recommendations

Writing research recommendations involves providing specific suggestions or advice to a researcher on how to conduct their study. Here are some steps to consider when writing research recommendations:

• Understand the research question: Before writing research recommendations, it is important to have a clear understanding of the research question and the objectives of the study. This will help to ensure that the recommendations are relevant and appropriate.
• Consider the research methods: Consider the most appropriate research methods that could be used to collect and analyze data that will address the research question. Identify the strengths and weaknesses of the different methods and how they might apply to the specific research question.
• Provide specific recommendations: Provide specific and actionable recommendations that the researcher can implement in their study. This can include recommendations related to sample size, data collection techniques, research instruments, data analysis methods, or other relevant factors.
• Justify recommendations : Justify why each recommendation is being made and how it will help to address the research question or objective. It is important to provide a clear rationale for each recommendation to help the researcher understand why it is important.
• Consider limitations and ethical considerations : Consider any limitations or potential ethical considerations that may arise in conducting the research. Provide recommendations for addressing these issues or mitigating their impact.
• Summarize recommendations: Provide a summary of the recommendations at the end of the report or document, highlighting the most important points and emphasizing how the recommendations will contribute to the overall success of the research project.

Example of Research Recommendations

Example of Research Recommendations sample for students:

• Further investigate the effects of X on Y by conducting a larger-scale randomized controlled trial with a diverse population.
• Explore the relationship between A and B by conducting qualitative interviews with individuals who have experience with both.
• Investigate the long-term effects of intervention C by conducting a follow-up study with participants one year after completion.
• Examine the effectiveness of intervention D in a real-world setting by conducting a field study in a naturalistic environment.
• Compare and contrast the results of this study with those of previous research on the same topic to identify any discrepancies or inconsistencies in the findings.
• Expand upon the limitations of this study by addressing potential confounding variables and conducting further analyses to control for them.
• Investigate the relationship between E and F by conducting a meta-analysis of existing literature on the topic.
• Explore the potential moderating effects of variable G on the relationship between H and I by conducting subgroup analyses.
• Identify potential areas for future research based on the gaps in current literature and the findings of this study.
• Conduct a replication study to validate the results of this study and further establish the generalizability of the findings.

Applications of Research Recommendations

Research recommendations are important as they provide guidance on how to improve or solve a problem. The applications of research recommendations are numerous and can be used in various fields. Some of the applications of research recommendations include:

• Policy-making: Research recommendations can be used to develop policies that address specific issues. For example, recommendations from research on climate change can be used to develop policies that reduce carbon emissions and promote sustainability.
• Program development: Research recommendations can guide the development of programs that address specific issues. For example, recommendations from research on education can be used to develop programs that improve student achievement.
• Product development : Research recommendations can guide the development of products that meet specific needs. For example, recommendations from research on consumer behavior can be used to develop products that appeal to consumers.
• Marketing strategies: Research recommendations can be used to develop effective marketing strategies. For example, recommendations from research on target audiences can be used to develop marketing strategies that effectively reach specific demographic groups.
• Medical practice : Research recommendations can guide medical practitioners in providing the best possible care to patients. For example, recommendations from research on treatments for specific conditions can be used to improve patient outcomes.
• Scientific research: Research recommendations can guide future research in a specific field. For example, recommendations from research on a specific disease can be used to guide future research on treatments and cures for that disease.

Purpose of Research Recommendations

The purpose of research recommendations is to provide guidance on how to improve or solve a problem based on the findings of research. Research recommendations are typically made at the end of a research study and are based on the conclusions drawn from the research data. The purpose of research recommendations is to provide actionable advice to individuals or organizations that can help them make informed decisions, develop effective strategies, or implement changes that address the issues identified in the research.

The main purpose of research recommendations is to facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings. Recommendations can help bridge the gap between research and practice by providing specific actions that can be taken based on the research results. By providing clear and actionable recommendations, researchers can help ensure that their findings are put into practice, leading to improvements in various fields, such as healthcare, education, business, and public policy.

Characteristics of Research Recommendations

Research recommendations are a key component of research studies and are intended to provide practical guidance on how to apply research findings to real-world problems. The following are some of the key characteristics of research recommendations:

• Actionable : Research recommendations should be specific and actionable, providing clear guidance on what actions should be taken to address the problem identified in the research.
• Evidence-based: Research recommendations should be based on the findings of the research study, supported by the data collected and analyzed.
• Contextual: Research recommendations should be tailored to the specific context in which they will be implemented, taking into account the unique circumstances and constraints of the situation.
• Feasible : Research recommendations should be realistic and feasible, taking into account the available resources, time constraints, and other factors that may impact their implementation.
• Prioritized: Research recommendations should be prioritized based on their potential impact and feasibility, with the most important recommendations given the highest priority.
• Communicated effectively: Research recommendations should be communicated clearly and effectively, using language that is understandable to the target audience.
• Evaluated : Research recommendations should be evaluated to determine their effectiveness in addressing the problem identified in the research, and to identify opportunities for improvement.

Advantages of Research Recommendations

Research recommendations have several advantages, including:

• Providing practical guidance: Research recommendations provide practical guidance on how to apply research findings to real-world problems, helping to bridge the gap between research and practice.
• Improving decision-making: Research recommendations help decision-makers make informed decisions based on the findings of research, leading to better outcomes and improved performance.
• Enhancing accountability : Research recommendations can help enhance accountability by providing clear guidance on what actions should be taken, and by providing a basis for evaluating progress and outcomes.
• Informing policy development : Research recommendations can inform the development of policies that are evidence-based and tailored to the specific needs of a given situation.
• Enhancing knowledge transfer: Research recommendations help facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings.
• Encouraging further research : Research recommendations can help identify gaps in knowledge and areas for further research, encouraging continued exploration and discovery.
• Promoting innovation: Research recommendations can help identify innovative solutions to complex problems, leading to new ideas and approaches.

Limitations of Research Recommendations

While research recommendations have several advantages, there are also some limitations to consider. These limitations include:

• Context-specific: Research recommendations may be context-specific and may not be applicable in all situations. Recommendations developed in one context may not be suitable for another context, requiring adaptation or modification.
• I mplementation challenges: Implementation of research recommendations may face challenges, such as lack of resources, resistance to change, or lack of buy-in from stakeholders.
• Limited scope: Research recommendations may be limited in scope, focusing only on a specific issue or aspect of a problem, while other important factors may be overlooked.
• Uncertainty : Research recommendations may be uncertain, particularly when the research findings are inconclusive or when the recommendations are based on limited data.
• Bias : Research recommendations may be influenced by researcher bias or conflicts of interest, leading to recommendations that are not in the best interests of stakeholders.
• Timing : Research recommendations may be time-sensitive, requiring timely action to be effective. Delayed action may result in missed opportunities or reduced effectiveness.
• Lack of evaluation: Research recommendations may not be evaluated to determine their effectiveness or impact, making it difficult to assess whether they are successful or not.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Ultimate Guide to Crafting Impactful Recommendations in Research

Are you ready to take your research to the next level? Crafting impactful recommendations is the key to unlocking the full potential of your study. By providing clear, actionable suggestions based on your findings, you can bridge the gap between research and real-world application.

In this ultimate guide, we'll show you how to write recommendations that make a difference in your research report or paper.

You'll learn how to craft specific, actionable recommendations that connect seamlessly with your research findings. Whether you're a student, writer, teacher, or journalist, this guide will help you master the art of writing recommendations in research. Let's get started and make your research count!

Understanding the Purpose of Recommendations

Recommendations in research serve as a vital bridge between your findings and their real-world applications. They provide specific, action-oriented suggestions to guide future studies and decision-making processes. Let's dive into the key purposes of crafting effective recommendations:

Guiding Future Research

Research recommendations play a crucial role in steering scholars and researchers towards promising avenues of exploration. By highlighting gaps in current knowledge and proposing new research questions, recommendations help advance the field and drive innovation.

Influencing Decision-Making

Well-crafted recommendations have the power to shape policies, programs, and strategies across various domains, such as:

• Policy-making
• Product development
• Marketing strategies
• Medical practice

By providing clear, evidence-based suggestions, recommendations facilitate informed decision-making and improve outcomes.

Connecting Research to Practice

Recommendations act as a conduit for transferring knowledge from researchers to practitioners, policymakers, and stakeholders. They bridge the gap between academic findings and their practical applications, ensuring that research insights are effectively translated into real-world solutions.

Enhancing Research Impact

By crafting impactful recommendations, you can amplify the reach and influence of your research, attracting attention from peers, funding agencies, and decision-makers.

Addressing Limitations

Recommendations provide an opportunity to acknowledge and address the limitations of your study. By suggesting concrete and actionable possibilities for future research, you demonstrate a thorough understanding of your work's scope and potential areas for improvement.

Identifying Areas for Future Research

Discovering research gaps is a crucial step in crafting impactful recommendations. It involves reviewing existing studies and identifying unanswered questions or problems that warrant further investigation. Here are some strategies to help you identify areas for future research:

Explore Research Limitations

Take a close look at the limitations section of relevant studies. These limitations often provide valuable insights into potential areas for future research. Consider how addressing these limitations could enhance our understanding of the topic at hand.

Critically Analyze Discussion and Future Research Sections

When reading articles, pay special attention to the discussion and future research sections. These sections often highlight gaps in the current knowledge base and propose avenues for further exploration. Take note of any recurring themes or unanswered questions that emerge across multiple studies.

Utilize Targeted Search Terms

To streamline your search for research gaps, use targeted search terms such as "literature gap" or "future research" in combination with your subject keywords. This approach can help you quickly identify articles that explicitly discuss areas for future investigation.

Seek Guidance from Experts

Don't hesitate to reach out to your research advisor or other experts in your field. Their wealth of knowledge and experience can provide valuable insights into potential research gaps and emerging trends.

By employing these strategies, you'll be well-equipped to identify research gaps and craft recommendations that push the boundaries of current knowledge. Remember, the goal is to refine your research questions and focus your efforts on areas where more understanding is needed.

Structuring Your Recommendations

When it comes to structuring your recommendations, it's essential to keep them concise, organized, and tailored to your audience. Here are some key tips to help you craft impactful recommendations:

Prioritize and Organize

• Limit your recommendations to the most relevant and targeted suggestions for your peers or colleagues in the field.
• Place your recommendations at the end of the report, as they are often top of mind for readers.
• Write your recommendations in order of priority, with the most important ones for decision-makers coming first.

Use a Clear and Actionable Format

• Write recommendations in a clear, concise manner using actionable words derived from the data analyzed in your research.
• Use bullet points instead of long paragraphs for clarity and readability.
• Ensure that your recommendations are specific, measurable, attainable, relevant, and timely (SMART).

Connect Recommendations to Research

By following this simple formula, you can ensure that your recommendations are directly connected to your research and supported by a clear rationale.

Tailor to Your Audience

• Consider the needs and interests of your target audience when crafting your recommendations.
• Explain how your recommendations can solve the issues explored in your research.
• Acknowledge any limitations or constraints of your study that may impact the implementation of your recommendations.

Avoid Common Pitfalls

• Don't undermine your own work by suggesting incomplete or unnecessary recommendations.
• Avoid using recommendations as a place for self-criticism or introducing new information not covered in your research.
• Ensure that your recommendations are achievable and comprehensive, offering practical solutions for the issues considered in your paper.

By structuring your recommendations effectively, you can enhance the reliability and validity of your research findings, provide valuable strategies and suggestions for future research, and deliver impactful solutions to real-world problems.

Crafting Actionable and Specific Recommendations

Crafting actionable and specific recommendations is the key to ensuring your research findings have a real-world impact. Here are some essential tips to keep in mind:

Embrace Flexibility and Feasibility

Your recommendations should be open to discussion and new information, rather than being set in stone. Consider the following:

• Be realistic and considerate of your team's capabilities when making recommendations.
• Prioritize recommendations based on impact and reach, but be prepared to adjust based on team effort levels.
• Focus on solutions that require the fewest changes first, adopting an MVP (Minimum Viable Product) approach.

Provide Detailed and Justified Recommendations

To avoid vagueness and misinterpretation, ensure your recommendations are:

• Detailed, including photos, videos, or screenshots whenever possible.
• Justified based on research findings, providing alternatives when findings don't align with expectations or business goals.

Use this formula when writing recommendations:

Observed problem/pain point/unmet need + consequence + potential solution

Adopt a Solution-Oriented Approach

Foster Collaboration and Participation

• Promote staff education on current research and create strategies to encourage adoption of promising clinical protocols.
• Include representatives from the treatment community in the development of the research initiative and the review of proposals.
• Require active, early, and permanent participation of treatment staff in the development, implementation, and interpretation of the study.

Tailor Recommendations to the Opportunity

When writing recommendations for a specific opportunity or program:

• Highlight the strengths and qualifications of the researcher.
• Provide specific examples of their work and accomplishments.
• Explain how their research has contributed to the field.
• Emphasize the researcher's potential for future success and their unique contributions.

By following these guidelines, you'll craft actionable and specific recommendations that drive meaningful change and showcase the value of your research.

Connecting Recommendations with Research Findings

Connecting your recommendations with research findings is crucial for ensuring the credibility and impact of your suggestions. Here's how you can seamlessly link your recommendations to the evidence uncovered in your study:

Grounding Recommendations in Research

Your recommendations should be firmly rooted in the data and insights gathered during your research process. Avoid including measures or suggestions that were not discussed or supported by your study findings. This approach ensures that your recommendations are evidence-based and directly relevant to the research at hand.

Highlighting the Significance of Collaboration

Research collaborations offer a wealth of benefits that can enhance an agency's competitive position. Consider the following factors when discussing the importance of collaboration in your recommendations:

• Organizational Development: Participation in research collaborations depends on an agency's stage of development, compatibility with its mission and culture, and financial stability.
• Trust-Building: Long-term collaboration success often hinges on a history of increasing involvement and trust between partners.
• Infrastructure: A permanent infrastructure that facilitates long-term development is key to successful collaborative programs.

Emphasizing Commitment and Participation

Fostering Quality Improvement and Organizational Learning

In your recommendations, highlight the importance of enhancing quality improvement strategies and fostering organizational learning. Show sensitivity to the needs and constraints of community-based programs, as this understanding is crucial for effective collaboration and implementation.

Addressing Limitations and Implications

If not already addressed in the discussion section, your recommendations should mention the limitations of the study and their implications. Examples of limitations include:

• Sample size or composition
• Participant attrition
• Study duration

By acknowledging these limitations, you demonstrate a comprehensive understanding of your research and its potential impact.

By connecting your recommendations with research findings, you provide a solid foundation for your suggestions, emphasize the significance of collaboration, and showcase the potential for future research and practical applications.

Crafting impactful recommendations is a vital skill for any researcher looking to bridge the gap between their findings and real-world applications. By understanding the purpose of recommendations, identifying areas for future research, structuring your suggestions effectively, and connecting them to your research findings, you can unlock the full potential of your study. Remember to prioritize actionable, specific, and evidence-based recommendations that foster collaboration and drive meaningful change.

As you embark on your research journey, embrace the power of well-crafted recommendations to amplify the impact of your work. By following the guidelines outlined in this ultimate guide, you'll be well-equipped to write recommendations that resonate with your audience, inspire further investigation, and contribute to the advancement of your field. So go forth, make your research count, and let your recommendations be the catalyst for positive change.

Q: What are the steps to formulating recommendations in research? A: To formulate recommendations in research, you should first gain a thorough understanding of the research question. Review the existing literature to inform your recommendations and consider the research methods that were used. Identify which data collection techniques were employed and propose suitable data analysis methods. It's also essential to consider any limitations and ethical considerations of your research. Justify your recommendations clearly and finally, provide a summary of your recommendations.

Q: Why are recommendations significant in research studies? A: Recommendations play a crucial role in research as they form a key part of the analysis phase. They provide specific suggestions for interventions or strategies that address the problems and limitations discovered during the study. Recommendations are a direct response to the main findings derived from data collection and analysis, and they can guide future actions or research.

Q: Can you outline the seven steps involved in writing a research paper? A: Certainly. The seven steps to writing an excellent research paper include:

• Allowing yourself sufficient time to complete the paper.
• Defining the scope of your essay and crafting a clear thesis statement.
• Conducting a thorough yet focused search for relevant research materials.
• Reading the research materials carefully and taking detailed notes.
• Writing your paper based on the information you've gathered and analyzed.
• Editing your paper to ensure clarity, coherence, and correctness.
• Submitting your paper following the guidelines provided.

Q: What tips can help make a research paper more effective? A: To enhance the effectiveness of a research paper, plan for the extensive process ahead and understand your audience. Decide on the structure your research writing will take and describe your methodology clearly. Write in a straightforward and clear manner, avoiding the use of clichés or overly complex language.

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Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of exploration. In an era marked by rapid technological advancements and an ever-expanding knowledge base, refining the process of generating research recommendations becomes imperative.

But, what is a research recommendation?

Research recommendations are suggestions or advice provided to researchers to guide their study on a specific topic . They are typically given by experts in the field. Research recommendations are more action-oriented and provide specific guidance for decision-makers, unlike implications that are broader and focus on the broader significance and consequences of the research findings. However, both are crucial components of a research study.

Difference Between Research Recommendations and Implication

Although research recommendations and implications are distinct components of a research study, they are closely related. The differences between them are as follows:

Types of Research Recommendations

Recommendations in research can take various forms, which are as follows:

These recommendations aim to assist researchers in navigating the vast landscape of academic knowledge.

Let us dive deeper to know about its key components and the steps to write an impactful research recommendation.

Key Components of Research Recommendations

The key components of research recommendations include defining the research question or objective, specifying research methods, outlining data collection and analysis processes, presenting results and conclusions, addressing limitations, and suggesting areas for future research. Here are some characteristics of research recommendations:

Research recommendations offer various advantages and play a crucial role in ensuring that research findings contribute to positive outcomes in various fields. However, they also have few limitations which highlights the significance of a well-crafted research recommendation in offering the promised advantages.

The importance of research recommendations ranges in various fields, influencing policy-making, program development, product development, marketing strategies, medical practice, and scientific research. Their purpose is to transfer knowledge from researchers to practitioners, policymakers, or stakeholders, facilitating informed decision-making and improving outcomes in different domains.

How to Write Research Recommendations?

Research recommendations can be generated through various means, including algorithmic approaches, expert opinions, or collaborative filtering techniques. Here is a step-wise guide to build your understanding on the development of research recommendations.

1. Understand the Research Question:

Understand the research question and objectives before writing recommendations. Also, ensure that your recommendations are relevant and directly address the goals of the study.

2. Review Existing Literature:

Familiarize yourself with relevant existing literature to help you identify gaps , and offer informed recommendations that contribute to the existing body of research.

3. Consider Research Methods:

Evaluate the appropriateness of different research methods in addressing the research question. Also, consider the nature of the data, the study design, and the specific objectives.

4. Identify Data Collection Techniques:

Gather dataset from diverse authentic sources. Include information such as keywords, abstracts, authors, publication dates, and citation metrics to provide a rich foundation for analysis.

5. Propose Data Analysis Methods:

Suggest appropriate data analysis methods based on the type of data collected. Consider whether statistical analysis, qualitative analysis, or a mixed-methods approach is most suitable.

6. Consider Limitations and Ethical Considerations:

Acknowledge any limitations and potential ethical considerations of the study. Furthermore, address these limitations or mitigate ethical concerns to ensure responsible research.

7. Justify Recommendations:

Explain how your recommendation contributes to addressing the research question or objective. Provide a strong rationale to help researchers understand the importance of following your suggestions.

8. Summarize Recommendations:

Provide a concise summary at the end of the report to emphasize how following these recommendations will contribute to the overall success of the research project.

By following these steps, you can create research recommendations that are actionable and contribute meaningfully to the success of the research project.

Download now to unlock some tips to improve your journey of writing research recommendations.

Example of a Research Recommendation

Here is an example of a research recommendation based on a hypothetical research to improve your understanding.

Research Recommendation: Enhancing Student Learning through Integrated Learning Platforms

Background:

The research study investigated the impact of an integrated learning platform on student learning outcomes in high school mathematics classes. The findings revealed a statistically significant improvement in student performance and engagement when compared to traditional teaching methods.

Recommendation:

In light of the research findings, it is recommended that educational institutions consider adopting and integrating the identified learning platform into their mathematics curriculum. The following specific recommendations are provided:

• Implementation of the Integrated Learning Platform:

Schools are encouraged to adopt the integrated learning platform in mathematics classrooms, ensuring proper training for teachers on its effective utilization.

• Professional Development for Educators:

Develop and implement professional programs to train educators in the effective use of the integrated learning platform to address any challenges teachers may face during the transition.

• Monitoring and Evaluation:

Establish a monitoring and evaluation system to track the impact of the integrated learning platform on student performance over time.

• Resource Allocation:

Allocate sufficient resources, both financial and technical, to support the widespread implementation of the integrated learning platform.

By implementing these recommendations, educational institutions can harness the potential of the integrated learning platform and enhance student learning experiences and academic achievements in mathematics.

This example covers the components of a research recommendation, providing specific actions based on the research findings, identifying the target audience, and outlining practical steps for implementation.

Using AI in Research Recommendation Writing

Enhancing research recommendations is an ongoing endeavor that requires the integration of cutting-edge technologies, collaborative efforts, and ethical considerations. By embracing data-driven approaches and leveraging advanced technologies, the research community can create more effective and personalized recommendation systems. However, it is accompanied by several limitations. Therefore, it is essential to approach the use of AI in research with a critical mindset, and complement its capabilities with human expertise and judgment.

Here are some limitations of integrating AI in writing research recommendation and some ways on how to counter them.

1. Data Bias

AI systems rely heavily on data for training. If the training data is biased or incomplete, the AI model may produce biased results or recommendations.

How to tackle: Audit regularly the model’s performance to identify any discrepancies and adjust the training data and algorithms accordingly.

2. Lack of Understanding of Context:

AI models may struggle to understand the nuanced context of a particular research problem. They may misinterpret information, leading to inaccurate recommendations.

How to tackle: Use AI to characterize research articles and topics. Employ them to extract features like keywords, authorship patterns and content-based details.

3. Ethical Considerations:

AI models might stereotype certain concepts or generate recommendations that could have negative consequences for certain individuals or groups.

How to tackle: Incorporate user feedback mechanisms to reduce redundancies. Establish an ethics review process for AI models in research recommendation writing.

4. Lack of Creativity and Intuition:

AI may struggle with tasks that require a deep understanding of the underlying principles or the ability to think outside the box.

How to tackle: Hybrid approaches can be employed by integrating AI in data analysis and identifying patterns for accelerating the data interpretation process.

5. Interpretability:

Many AI models, especially complex deep learning models, lack transparency on how the model arrived at a particular recommendation.

How to tackle: Implement models like decision trees or linear models. Provide clear explanation of the model architecture, training process, and decision-making criteria.

6. Dynamic Nature of Research:

Research fields are dynamic, and new information is constantly emerging. AI models may struggle to keep up with the rapidly changing landscape and may not be able to adapt to new developments.

How to tackle: Establish a feedback loop for continuous improvement. Regularly update the recommendation system based on user feedback and emerging research trends.

The integration of AI in research recommendation writing holds great promise for advancing knowledge and streamlining the research process. However, navigating these concerns is pivotal in ensuring the responsible deployment of these technologies. Researchers need to understand the use of responsible use of AI in research and must be aware of the ethical considerations.

Exploring research recommendations plays a critical role in shaping the trajectory of scientific inquiry. It serves as a compass, guiding researchers toward more robust methodologies, collaborative endeavors, and innovative approaches. Embracing these suggestions not only enhances the quality of individual studies but also contributes to the collective advancement of human understanding.

Frequently Asked Questions

The purpose of recommendations in research is to provide practical and actionable suggestions based on the study's findings, guiding future actions, policies, or interventions in a specific field or context. Recommendations bridges the gap between research outcomes and their real-world application.

To make a research recommendation, analyze your findings, identify key insights, and propose specific, evidence-based actions. Include the relevance of the recommendations to the study's objectives and provide practical steps for implementation.

Begin a recommendation by succinctly summarizing the key findings of the research. Clearly state the purpose of the recommendation and its intended impact. Use a direct and actionable language to convey the suggested course of action.

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How To Write Recommendations In A Research Study

Published by Alvin Nicolas at July 12th, 2024 , Revised On July 12, 2024

The ultimate goal of any research process is not just to gather knowledge, but to use that knowledge to make a positive impact. This is where recommendations come in.  A well-written recommendations section in your research study translates your findings into actionable steps and guides future research on the topic. 

This blog is your ultimate guide to understanding how to write recommendations in a research study. But before that, let’s see what is recommendation in research. 

What Is Recommendation In Research 

In a research study, the recommendation section refers to a suggested course of action based on the findings of your research . It acts as a bridge between the knowledge you gained and its practical implications. 

Recommendations take your research results and propose concrete steps on how to use them to address a problem or improve a situation. Moreover, you can suggest new avenues and guide future research in building upon your work. This will improve the credibility of your research. For studies that include real-world implications, recommendations are a great way to provide evidence-based suggestions for policymakers or practitioners to consider. 

Difference Between Research Recommendations and Implication

Research recommendations and implications often confuse researchers. They cannot easily differentiate between the two. Here is how they are different. 

Where To Add Recommendations 

Recommendations are mostly part of your conclusion and discussion sections. If you are writing a practical dissertation , you can include a separate section for your recommendations. 

Types of Research Recommendations

There are different forms of recommendations in research. Some of them include the following. 

How To Construct The Recommendations Section

There are different ways in which different scholars write the recommendations section. A general observation is a research question → conclusion → recommendation.

The following example will help you understand this better.

Research Question

How can the education of mothers impact the social skills of kindergarten children?

The role of mothers is a significant contributor towards the social skills of children. From an early age, kids tend to observe how their mother interacts with others and follow in her footsteps initially. Therefore, mothers should be educated and interact with good demeanour if they want their children to have excellent social skills.

Recommendation

The study revealed that a mother’s education plays an important role in building the social skills of children on kindergarten level. Future research could explore how the same continues in junior school level children.

How To Write Recommendations In Research

Now that you are familiar with the definition and types, here is a step-by-step guide on how to write a recommendation in research.

Step 1: Revisit Your Research Goals

Before doing anything else, you have to remind yourself of the objectives that you set out to achieve in your research. It allows you to match your recommendations directly to your research questions and see if you made any contribution to your goals.

Step 2: Analyse Your Findings

You have to examine your data and identify your key results. This analysis forms the foundation for your recommendations. Look for patterns and unexpected findings that might suggest new areas for other researchers to explore.

Step 3: Consider The Research Methods

Ask these questions from yourself: were the research methods effective? Is there any other way that would have been better to perform this research, or were there any limitations associated with the research methods?

Step 4: Prioritise Recommendations

You might have a lot of recommendations in mind, but all are not equal. You have to consider the impact and feasibility of each suggestion. Prioritise these recommendations, while remaining realistic about implementation.

Step 5: Write Actionable Statements

Do not be vague when crafting statements. Instead, you have to use clear and concise language that outlines specific actions. For example, if you want to say “improve education practices,” you could write “implement a teacher training program” for better clarity.

Step 6: Provide Evidence

You cannot just make suggestions out of thin air, and have to ground them in the evidence you have gathered through your research. Moreover, cite relevant data or findings from your study or previous literature to support your recommendations.

Step 7: Address Challenges

There are always some limitations related to the research at hand. As a researcher, it is your duty to highlight and address any challenges faced or what might occur in the future.

Tips For Writing The Perfect Recommendation In Research

Use these tips to write the perfect recommendation in your research.

• Be Concise – Write recommendations in a clear and concise language. Use one sentence statements to look more professional.
• Be Logical & Coherent – You can use lists and headings according to the requirements of your university.
• Tailor According To Your Readers – You have to aim your recommendations to a specific audience and colleagues in the field of study.
• Provide Specific Suggestions – Offer specific measures and solutions to the issues, and focus on actionable suggestions.
• Match Recommendations To Your Conclusion – You have to align your recommendations with your conclusion.
• Consider Limitations – Use critical thinking to see how limitations may impact the feasibility of your solutions.
• End With A Summary – You have to add a small conclusion to highlight suggestions and their impact.

Example Of Recommendation In Research

Context of the study:

This research studies how effective e-learning platforms are for adult language learners compared to traditional classroom instruction. The findings suggest that e-learning platforms can be just as effective as traditional classrooms in improving language proficiency.

Research Recommendation Sample

Language educators can incorporate e-learning tools into existing curriculums to provide learners with more flexibility. Additionally, they can develop training programs for educators on how to integrate e-learning platforms into their teaching practices.

E-learning platform developers should focus on e-learning platforms that are interactive and cater to different learning styles. They can also invest in features that promote learner autonomy and self-directed learning.

Future researchers can further explore the long-term effects of e-learning on language acquisition to provide insights into whether e-learning can support sustained language development.

Frequently Asked Questions

How to write recommendations in a research paper.

• Revisit your research goals
• Analyse your findings 
• Consider the research methods 
• Prioritise recommendations 
• Write actionable statements 
• Provide evidence 
• Address challenges

How to present recommendations in research?

• Be concise 
• Write logical and coherent 
• Match recommendations to conclusion 
• Ensure your recommendations are achievable

What to write in recommendation in research?

Your recommendation has to be concrete and specific and support the research with a clear rationale. Moreover, it should be connected directly to your research. Your recommendations, however, should not undermine your own work or use self-criticism. 

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• How to Write Recommendations in Research | Examples & Tips

How to Write Recommendations in Research | Examples & Tips

Published on 15 September 2022 by Tegan George .

Recommendations in research are a crucial component of your discussion section and the conclusion of your thesis , dissertation , or research paper .

As you conduct your research and analyse the data you collected , perhaps there are ideas or results that don’t quite fit the scope of your research topic . Or, maybe your results suggest that there are further implications of your results or the causal relationships between previously-studied variables than covered in extant research.

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Table of contents

What should recommendations look like, building your research recommendation, how should your recommendations be written, recommendation in research example, frequently asked questions about recommendations.

Recommendations for future research should be:

• Concrete and specific
• Supported with a clear rationale
• Directly connected to your research

Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.

Relatedly, when making these recommendations, avoid:

• Undermining your own work, but rather offer suggestions on how future studies can build upon it
• Suggesting recommendations actually needed to complete your argument, but rather ensure that your research stands alone on its own merits
• Using recommendations as a place for self-criticism, but rather as a natural extension point for your work

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There are many different ways to frame recommendations, but the easiest is perhaps to follow the formula of research question   conclusion  recommendation. Here’s an example.

Conclusion An important condition for controlling many social skills is mastering language. If children have a better command of language, they can express themselves better and are better able to understand their peers. Opportunities to practice social skills are thus dependent on the development of language skills.

As a rule of thumb, try to limit yourself to only the most relevant future recommendations: ones that stem directly from your work. While you can have multiple recommendations for each research conclusion, it is also acceptable to have one recommendation that is connected to more than one conclusion.

These recommendations should be targeted at your audience, specifically toward peers or colleagues in your field that work on similar topics to yours. They can flow directly from any limitations you found while conducting your work, offering concrete and actionable possibilities for how future research can build on anything that your own work was unable to address at the time of your writing.

See below for a full research recommendation example that you can use as a template to write your own.

The current study can be interpreted as a first step in the research on COPD speech characteristics. However, the results of this study should be treated with caution due to the small sample size and the lack of details regarding the participants’ characteristics.

Future research could further examine the differences in speech characteristics between exacerbated COPD patients, stable COPD patients, and healthy controls. It could also contribute to a deeper understanding of the acoustic measurements suitable for e-health measurements.

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While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

For a stronger dissertation conclusion , avoid including:

• Generic concluding phrases (e.g. “In conclusion…”)
• Weak statements that undermine your argument (e.g. “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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George, T. (2022, September 15). How to Write Recommendations in Research | Examples & Tips. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/thesis-dissertation/research-recommendations/

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

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Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

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• Polly Brown ([email protected]) , publishing manager 1 ,
• Klara Brunnhuber , clinical editor 1 ,
• Kalipso Chalkidou , associate director, research and development 2 ,
• Iain Chalmers , director 3 ,
• Mike Clarke , director 4 ,
• Mark Fenton , editor 3 ,
• Carol Forbes , reviews manager 5 ,
• Julie Glanville , associate director/information service manager 5 ,
• Nicholas J Hicks , consultant in public health medicine 6 ,
• Janet Moody , identification and prioritisation manager 6 ,
• Sara Twaddle , director 7 ,
• Hazim Timimi , systems developer 8 ,
• Pamela Young , senior programme manager 6
• 1 BMJ Publishing Group, London WC1H 9JR,
• 2 National Institute for Health and Clinical Excellence, London WC1V 6NA,
• 3 Database of Uncertainties about the Effects of Treatments, James Lind Alliance Secretariat, James Lind Initiative, Oxford OX2 7LG,
• 4 UK Cochrane Centre, Oxford OX2 7LG,
• 5 Centre for Reviews and Dissemination, University of York, York YO10 5DD,
• 6 National Coordinating Centre for Health Technology Assessment, University of Southampton, Southampton SO16 7PX,
• 7 Scottish Intercollegiate Guidelines Network, Edinburgh EH2 1EN,
• 8 Update Software, Oxford OX2 7LG
• Correspondence to: PBrown
• Accepted 22 September 2006

“More research is needed” is a conclusion that fits most systematic reviews. But authors need to be more specific about what exactly is required

Long awaited reports of new research, systematic reviews, and clinical guidelines are too often a disappointing anticlimax for those wishing to use them to direct future research. After many months or years of effort and intellectual energy put into these projects, authors miss the opportunity to identify unanswered questions and outstanding gaps in the evidence. Most reports contain only a less than helpful, general research recommendation. This means that the potential value of these recommendations is lost.

Current recommendations

In 2005, representatives of organisations commissioning and summarising research, including the BMJ Publishing Group, the Centre for Reviews and Dissemination, the National Coordinating Centre for Health Technology Assessment, the National Institute for Health and Clinical Excellence, the Scottish Intercollegiate Guidelines Network, and the UK Cochrane Centre, met as members of the development group for the Database of Uncertainties about the Effects of Treatments (see bmj.com for details on all participating organisations). Our aim was to discuss the state of research recommendations within our organisations and to develop guidelines for improving the presentation of proposals for further research. All organisations had found weaknesses in the way researchers and authors of systematic reviews and clinical guidelines stated the need for further research. As part of the project, a member of the Centre for Reviews and Dissemination under-took a rapid literature search to identify information on research recommendation models, which found some individual methods but no group initiatives to attempt to standardise recommendations.

Suggested format for research recommendations on the effects of treatments

Core elements.

E Evidence (What is the current state of the evidence?)

P Population (What is …

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What are Implications and Recommendations in Research? How to Write It, with Examples

Highly cited research articles often contain both implications and recommendations , but there is often some confusion around the difference between implications and recommendations in research. Implications of a study are the impact your research makes in your chosen area; they discuss how the findings of the study may be important to justify further exploration of your research topic. Research recommendations suggest future actions or subsequent steps supported by your research findings. It helps to improve your field of research or cross-disciplinary fields through future research or provides frameworks for decision-makers or policymakers. Recommendations are the action plan you propose based on the outcome.

In this article, we aim to simplify these concepts for researchers by providing key insights on the following:  

• what are implications in research 
• what is recommendation in research 
• differences between implications and recommendations 
• how to write implications in research 
• how to write recommendation in research 
• sample recommendation in research 

Table of Contents

What are implications in research

The implications in research explain what the findings of the study mean to researchers or to certain subgroups or populations beyond the basic interpretation of results. Even if your findings fail to bring radical or disruptive changes to existing ways of doing things, they might have important implications for future research studies. For example, your proposed method for operating remote-controlled robots could be more precise, efficient, or cheaper than existing methods, or the remote-controlled robot could be used in other application areas. This could enable more researchers to study a specific problem or open up new research opportunities.   

Implications in research inform how the findings, drawn from your results, may be important for and impact policy, practice, theory, and subsequent research. Implications may be theoretical or practical. 1  

• Practical implications are potential values of the study with practical or real outcomes . Determining the practical implications of several solutions can aid in identifying optimal solution results. For example, clinical research or research on classroom learning mostly has practical implications in research . If you developed a new teaching method, the implication would be how teachers can use that method based on your findings.  
• Theoretical implications in research constitute additions to existing theories or establish new theories. These types of implications in research characterize the ability of research to influence society in apparent ways. It is, at most, an educated guess (theoretical) about the possible implication of action and need not be as absolute as practical implications in research . If your study supported the tested theory, the theoretical implication would be that the theory can explain the investigated phenomenon. Else, your study may serve as a basis for modifying the theory. Theories may be partially supported as well, implying further study of the theory or necessary modifications are required.  

What are recommendations in research?

Recommendations in research can be considered an important segment of the analysis phase. Recommendations allow you to suggest specific interventions or strategies to address the issues and constraints identified through your study. It responds to key findings arrived at through data collection and analysis. A process of prioritization can help you narrow down important findings for which recommendations are developed.  

Recommendations in research examples

Recommendations in research may vary depending on the purpose or beneficiary as seen in the table below.  

Table: Recommendations in research examples based on purpose and beneficiary  

If you’re wondering how to make recommendations in research . You can use the simple  recommendation in research example below as a handy template.  

Table: Sample recommendation in research template  

Basic differences between implications and recommendations in research

Implications and recommendations in research are two important aspects of a research paper or your thesis or dissertation. Implications discuss the importance of the research findings, while recommendations offer specific actions to solve a problem. So, the basic difference between the two is in their function and the questions asked to achieve it. The following table highlights the main differences between implications and recommendations in research .  

Table: Differences between implications and recommendations in research  

Where do implications go in your research paper

Because the implications and recommendations of the research are based on study findings, both are usually written after the completion of a study. There is no specific section dedicated to implications in research ; they are usually integrated into the discussion section adding evidence as to why the results are meaningful and what they add to the field. Implications can be written after summarizing your main findings and before the recommendations and conclusion.   

Implications can also be presented in the conclusion section after a short summary of the study results.   

How to write implications in research

Implication means something that is inferred. The implications of your research are derived from the importance of your work and how it will impact future research. It is based on how previous studies have advanced your field and how your study can add to that.   

When figuring out how to write implications in research , a good strategy is to separate it into the different types of implications in research , such as social, political, technological, policy-related, or others. As mentioned earlier, the most frequently used are the theoretical and practical implications.   

Next, you need to ask, “Who will benefit the most from reading my paper?” Is it policymakers, physicians, the public, or other researchers? Once you know your target population, explain how your findings can help them.  

The implication section can include a paragraph or two that asserts the practical or managerial implications and links it to the study findings. A discussion can then follow, demonstrating that the findings can be practically implemented or how they will benefit a specific audience. The writer is given a specific degree of freedom when writing research implications , depending on the type of implication in research you want to discuss: practical or theoretical. Each is discussed differently, using different words or in separate sections. The implications can be based on how the findings in your study are similar or dissimilar to that in previous studies. Your study may reaffirm or disprove the results of other studies, which has important implications in research . You can also suggest future research directions in the light of your findings or require further research to confirm your findings, which are all crucial implications. Most importantly, ensure the implications in research are specific and that your tone reflects the strength of your findings without exaggerating your results.   

Implications in research can begin with the following specific sentence structures:  

• These findings suggest that…
• These results build on existing body of evidence of…
• These results should be considered when…
• While previous research focused on x, our results show that y…

What should recommendations in research look like?

Recommendations for future research should be:  

• Directly related to your research question or findings  
• Concrete and specific  
• Supported by a clear reasoning  

The recommendations in research can be based on the following factors:  

1. Beneficiary: A paper’s research contribution may be aimed at single or multiple beneficiaries, based on which recommendations can vary. For instance, if your research is about the quality of care in hospitals, the research recommendation to different beneficiaries might be as follows:  

• Nursing staff: Staff should undergo training to enhance their understanding of what quality of care entails.  
• Health science educators: Educators must design training modules that address quality-related issues in the hospital.  
• Hospital management: Develop policies that will increase staff participation in training related to health science.  

2. Limitations: The best way to figure out what to include in your research recommendations is to understand the limitations of your study. It could be based on factors that you have overlooked or could not consider in your present study. Accordingly, the researcher can recommend that other researchers approach the problem from a different perspective, dimension, or methodology. For example, research into the quality of care in hospitals can be based on quantitative data. The researcher can then recommend a qualitative study of factors influencing the quality of care, or they can suggest investigating the problem from the perspective of patients rather than the healthcare providers.   

3. Theory or Practice: Your recommendations in research could be implementation-oriented or further research-oriented.   

4. Your research: Research recommendations can be based on your topic, research objectives, literature review, and analysis, or evidence collected. For example, if your data points to the role of faculty involvement in developing effective programs, recommendations in research can include developing policies to increase faculty participation. Take a look at the evidence-based recommendation in research example s provided below.   

Table: Example of evidence-based research recommendation  

Avoid making the following mistakes when writing research recommendations :  

• Don’t undermine your own work: Recommendations in research should offer suggestions on how future studies can be built upon the current study as a natural extension of your work and not as an entirely new field of research.  
• Support your study arguments: Ensure that your research findings stand alone on their own merits to showcase the strength of your research paper.   

How to write recommendations in research

When writing research recommendations , your focus should be on highlighting what additional work can be done in that field. It gives direction to researchers, industries, or governments about changes or developments possible in this field. For example, recommendations in research can include practical and obtainable strategies offering suggestions to academia to address problems. It can also be a framework that helps government agencies in developing strategic or long-term plans for timely actions against disasters or aid nation-building.  

There are a few SMART 2 things to remember when writing recommendations in research. Your recommendations must be: 

• S pecific: Clearly state how challenges can be addressed for better outcomes and include an action plan that shows what can be achieved. 
• M easurable: Use verbs denoting measurable outcomes, such as identify, analyze, design, compute, assess, evaluate, revise, plan, etc., to strengthen recommendations in research .   
• A ttainable: Recommendations should offer a solution-oriented approach to problem-solving and must be written in a way that is easy to follow.  
• R elevant: Research recommendations should be reasonable, realistic, and result-based. Make sure to suggest future possibilities for your research field.  
• T imely: Time-based or time-sensitive recommendations in research help divide the action plan into long-term or short-term (immediate) goals. A timeline can also inform potential readers of what developments should occur over time.  

If you are wondering how many words to include in your research recommendation , a general rule of thumb would be to set aside 5% of the total word count for writing research recommendations . Finally, when writing the research implications and recommendations , stick to the facts and avoid overstating or over-generalizing the study findings. Both should be supported by evidence gathered through your data analysis.  

References:  

• Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings.  Psychological bulletin ,  124 (2), 262.
• Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives.  Manag Rev ,  70 (11), 35-36.

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Making Recommendations

• First Online: 23 January 2020

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• Guy Gratton 4  

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It is common, and sometimes essential, that an engineering report contains recommendations, which are normally immediately after the conclusions. Recommendations may either be for further work (e.g. additional research into a topic identified in the project) or for action (e.g. to rectify problems with a piece of equipment evaluated during the project). Recommendations should normally be classified according to how important it is that they are carried out, with various terms and scales available for this purpose; two of which are explained here.

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Gratton, P., Gratton, G. (2020). Making Recommendations. In: Achieving Success with the Engineering Dissertation. Springer, Cham. https://doi.org/10.1007/978-3-030-33192-4_12

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Recommendation in Research

Ai generator.

A recommendation in research refers to the advice or suggestions provided by researchers at the conclusion of their study, aimed at addressing the gaps identified, enhancing future research , and applying findings in practical contexts. Recommendations are crucial as they guide stakeholders, including policymakers, practitioners, and fellow researchers, on how to utilize the research outcomes effectively. These suggestions are typically based on the evidence gathered during the study and are intended to improve practices, inform decision-making, and inspire further investigations to build on the existing knowledge.

What is Recommendation in Research?

A recommendation in research is a suggestion or course of action proposed by researchers based on their study’s findings. It aims to address identified gaps, enhance future research, and apply results in practical scenarios. Recommendations guide stakeholders, such as policymakers and fellow researchers, on utilizing the research effectively to improve practices, inform decisions, and inspire further studies.

Examples of Recommendations in Research

• Implement Comprehensive Training Programs : Ensure that employees receive ongoing training to keep up with technological advancements.
• Increase Funding for Renewable Energy Projects : Allocate more resources to develop sustainable energy solutions.
• Promote Interdisciplinary Research : Encourage collaboration across various fields to address complex global issues.
• Adopt Advanced Data Analytics : Utilize cutting-edge data analysis techniques to improve decision-making processes.
• Enhance Public Awareness Campaigns : Develop strategies to educate the public on critical health issues.
• Strengthen Cybersecurity Measures : Implement robust security protocols to protect sensitive information.
• Encourage Community Involvement : Foster greater community participation in local governance.
• Develop Inclusive Policies : Create policies that address the needs of diverse populations.
• Optimize Supply Chain Management : Improve logistics and supply chain efficiency to reduce costs.
• Support Mental Health Initiatives : Increase support for mental health programs and services.

Recommendation for Students in Research

Research is a crucial component of academic and professional development. Here are some key recommendations for students engaged in research to ensure success and meaningful contributions to their field:

1. Choose a Relevant and Interesting Topic

• Personal Interest: Select a topic that genuinely interests you.
• Relevance: Ensure the topic is relevant to your field of study.
• Scope: Make sure the topic is neither too broad nor too narrow.

2. Conduct a Thorough Literature Review

• Background Research: Review existing literature to understand the current state of knowledge.
• Identify Gaps: Identify gaps in the existing research that your study can address.
• Theoretical Framework: Build a strong theoretical foundation for your research.

3. Develop a Clear Research Plan

• Objectives: Define clear and achievable research objectives.
• Methodology: Choose appropriate research methods and techniques.
• Timeline: Create a realistic timeline with milestones for completing each stage of the research.

4. Use Reliable and Valid Sources

• Academic Journals: Prefer peer-reviewed journals for sourcing information.
• Primary Sources: Whenever possible, use primary sources to gather data.
• Citation Management: Use citation management tools to organize your references.

5. Ensure Ethical Conduct

• Informed Consent: Obtain informed consent from participants if your research involves human subjects.
• Data Privacy: Ensure the confidentiality and privacy of your data.
• Integrity: Maintain honesty and transparency in your research process.

6. Develop Strong Analytical Skills

• Critical Thinking: Develop the ability to critically analyze data and sources.
• Statistical Analysis: Gain proficiency in statistical methods if your research involves quantitative data.
• Qualitative Analysis: Learn methods for analyzing qualitative data, such as thematic analysis.

7. Seek Feedback and Collaboration

• Mentorship: Seek guidance from your research advisor or mentor regularly.
• Peer Review: Engage with peers for feedback and constructive criticism.
• Collaboration: Collaborate with other researchers to enhance the quality of your study.

8. Maintain Clear and Consistent Documentation

• Research Journal: Keep a detailed journal of your research process, observations, and reflections.
• Data Management: Organize your data systematically for easy retrieval and analysis.
• Progress Reports: Regularly update your progress and adjust your plan as needed.

9. Communicate Your Findings Effectively

• Writing Skills: Develop strong academic writing skills to present your findings clearly.
• Presentations: Learn to create and deliver effective presentations of your research.
• Publication: Aim to publish your research in reputable academic journals or conferences.

10. Stay Updated and Continue Learning

• Current Trends: Stay updated with the latest developments in your field.
• Professional Development: Attend workshops, seminars, and conferences to enhance your knowledge and skills.
• Networking: Build a professional network with other researchers and professionals in your field.

Types of Recommendation in Research

Recommendations in research are crucial as they provide actionable insights based on the study’s findings. Here are the primary types of recommendations commonly found in research:

1. Practical Recommendations

Practical recommendations offer actionable advice that can be implemented in real-world settings. These are particularly useful for practitioners and policymakers.

• Implementation Strategies: Suggest ways to apply research findings in practice.
• Policy Changes: Recommend modifications to existing policies or the creation of new policies.
• Best Practices: Identify effective practices and procedures based on research results.

2. Theoretical Recommendations

Theoretical recommendations are aimed at advancing academic knowledge and understanding. They often suggest directions for future research or adjustments to existing theories.

• Theory Development: Propose new theories or modifications to existing ones based on research findings.
• Conceptual Frameworks: Suggest new conceptual models or frameworks.
• Research Hypotheses: Recommend specific hypotheses for future testing.

3. Methodological Recommendations

Methodological recommendations focus on the research process itself. They offer suggestions for improving research design, data collection, and analysis techniques.

• Research Design: Advise on more effective or innovative research designs.
• Data Collection Methods: Recommend better or alternative methods for data collection.
• Analytical Techniques: Suggest advanced or more appropriate analytical techniques.

4. Policy Recommendations

Policy recommendations are directed towards governmental or organizational bodies. They aim to influence policy-making processes based on research evidence.

• Legislative Changes: Recommend changes to laws or regulations.
• Organizational Policies: Suggest adjustments to organizational policies and procedures.
• Public Health Initiatives: Propose new public health strategies or interventions.

5. Educational Recommendations

Educational recommendations are targeted at educational institutions, educators, and curriculum developers. They aim to improve educational practices and outcomes.

• Curriculum Development: Suggest changes or additions to curricula.
• Teaching Methods: Recommend effective teaching strategies and methods.
• Educational Programs: Propose new programs or enhancements to existing ones.

Recommendation for Future Researchers

Future researchers can benefit from insights and guidance to enhance the quality and impact of their studies. Here are some key recommendations:

1. Explore Unanswered Questions

• Identify Gaps: Focus on gaps highlighted in previous research to build on existing knowledge.
• New Areas: Investigate emerging areas or under-researched topics within your field.

2. Improve Methodological Rigor

• Innovative Methods: Incorporate innovative research methodologies and techniques.
• Replication Studies: Conduct replication studies to verify and validate findings from prior research.
• Mixed Methods: Utilize mixed methods approaches to provide a comprehensive understanding of the research problem.

3. Ensure Ethical Conduct

• Ethical Guidelines: Adhere to ethical guidelines and standards throughout the research process.
• Informed Consent: Ensure that participants provide informed consent and understand their rights.
• Data Privacy: Protect the confidentiality and privacy of participants’ data.

4. Enhance Data Quality

• Robust Data Collection: Use robust data collection methods to ensure accuracy and reliability.
• Triangulation: Employ triangulation by using multiple data sources or methods to strengthen findings.
• Longitudinal Studies: Consider conducting longitudinal studies to observe changes over time.

5. Collaborate and Network

• Interdisciplinary Collaboration: Work with researchers from different disciplines to gain diverse perspectives.
• International Partnerships: Form partnerships with international researchers to broaden the scope and impact of your study.
• Professional Networks: Join professional organizations and attend conferences to stay updated and connected.

What is the Purpose of Recommendation in Research

Recommendations in research are essential for guiding future actions based on the study’s findings. Here are the main purposes of including recommendations in research:

1. Guiding Future Research

• Identify Gaps: Point out areas where more research is needed.
• Suggest Topics: Recommend specific topics or questions for future studies.
• Encourage Validation: Suggest replicating the study in different settings to confirm results.

2. Informing Policy and Practice

• Policy Changes: Provide evidence-based suggestions for improving or creating policies.
• Best Practices: Offer practical advice for professionals to improve their work.
• Implementation: Suggest ways to apply the research findings in real-world situations.

3. Enhancing Academic Knowledge

• Theoretical Contributions: Help develop or refine theories based on the research findings.
• Stimulate Discussion: Encourage further academic debate and inquiry.

4. Improving Research Methods

• Methodology: Recommend better or alternative research methods.
• Data Collection: Suggest more effective ways to gather data.
• Analysis Techniques: Propose improved methods for analyzing data.

5. Solving Practical Problems

• Actionable Solutions: Offer practical solutions to problems identified in the research.
• Resource Allocation: Guide organizations on how to use resources more effectively.
• Strategic Planning: Assist in planning future actions based on the research insights.

How to Write Research Recommendations?

Writing research recommendations involves providing actionable advice based on the findings of your study. Here are steps and tips to help you write effective research recommendations:

1. Review Your Findings

• Summarize Key Findings: Begin by summarizing the most important findings of your research.
• Highlight Significant Results: Focus on results that have significant implications for future research, policy, or practice.

2. Align Recommendations with Objectives

• Reflect on Objectives: Ensure that your recommendations align with the original objectives of your study.
• Address Research Questions: Directly address the research questions or hypotheses you set out to explore.

3. Be Specific and Actionable

• Concrete Actions: Provide specific actions that stakeholders can take.
• Clear Guidance: Offer clear and practical steps rather than vague suggestions.

4. Prioritize Recommendations

• Importance: Rank recommendations based on their importance and feasibility.
• Immediate vs. Long-Term: Distinguish between recommendations that can be implemented immediately and those that are long-term.

5. Consider Different Audiences

• Tailor Recommendations: Adapt recommendations to different audiences such as policymakers, practitioners, researchers, or the general public.
• Relevant Language: Use language and terms that are relevant and understandable to each audience.

6. Support with Evidence

• Link to Findings: Base your recommendations on the evidence from your research.
• Cite Data: Use data and examples from your study to justify each recommendation.

7. Address Limitations

• Acknowledge Constraints: Recognize any limitations in your study and how they might affect your recommendations.
• Suggest Improvements: Provide suggestions for how future research can address these limitations.

8. Highlight Benefits

• Positive Outcomes: Emphasize the potential benefits of implementing your recommendations.
• Impact: Discuss the impact your recommendations could have on the field, policy, or practice.

9. Be Realistic

• Feasibility: Ensure that your recommendations are realistic and achievable.
• Resources: Consider the resources required to implement your recommendations and whether they are available.

10. Review and Revise

• Proofread: Carefully review your recommendations for clarity, coherence, and correctness.
• Feedback: Seek feedback from peers or advisors to refine your recommendations.

FAQ’s

Why are recommendations important in research.

Recommendations provide practical applications of research findings, guiding stakeholders in implementing changes or further investigations.

How do you write a good research recommendation?

A good research recommendation is specific, actionable, and directly linked to the study’s conclusions and data.

What should be included in a research recommendation?

Include the action to be taken, the rationale behind it, and its expected impact or benefits.

Can recommendations suggest further research?

Yes, recommendations often suggest areas for further study to address limitations or explore new questions.

How should recommendations be structured in a research paper?

Recommendations should follow the conclusion section, clearly numbered or bullet-pointed for easy reading.

What is the difference between conclusions and recommendations?

Conclusions summarize the findings, while recommendations propose actions based on those findings.

Who benefits from research recommendations?

Policymakers, practitioners, researchers, and other stakeholders can benefit from research recommendations.

How many recommendations should a research paper have?

The number of recommendations varies but should be concise and focused, usually between three to five key suggestions.

Can recommendations be generalized to other contexts?

Recommendations should be context-specific but can sometimes be adapted for broader application.

What language should be used in writing recommendations?

Use clear, precise, and direct language to ensure recommendations are easily understood and actionable.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Implications or Recommendations in Research: What's the Difference?

• Peer Review

High-quality research articles that get many citations contain both implications and recommendations. Implications are the impact your research makes, whereas recommendations are specific actions that can then be taken based on your findings, such as for more research or for policymaking.

Updated on August 23, 2022

That seems clear enough, but the two are commonly confused.

This confusion is especially true if you come from a so-called high-context culture in which information is often implied based on the situation, as in many Asian cultures. High-context cultures are different from low-context cultures where information is more direct and explicit (as in North America and many European cultures).

Let's set these two straight in a low-context way; i.e., we'll be specific and direct! This is the best way to be in English academic writing because you're writing for the world.

Implications and recommendations in a research article

The standard format of STEM research articles is what's called IMRaD:

• Introduction
• Discussion/conclusions

Some journals call for a separate conclusions section, while others have the conclusions as the last part of the discussion. You'll write these four (or five) sections in the same sequence, though, no matter the journal.

The discussion section is typically where you restate your results and how well they confirmed your hypotheses. Give readers the answer to the questions for which they're looking to you for an answer.

At this point, many researchers assume their paper is finished. After all, aren't the results the most important part? As you might have guessed, no, you're not quite done yet.

The discussion/conclusions section is where to say what happened and what should now happen

The discussion/conclusions section of every good scientific article should contain the implications and recommendations.

The implications, first of all, are the impact your results have on your specific field. A high-impact, highly cited article will also broaden the scope here and provide implications to other fields. This is what makes research cross-disciplinary.

Recommendations, however, are suggestions to improve your field based on your results.

These two aspects help the reader understand your broader content: How and why your work is important to the world. They also tell the reader what can be changed in the future based on your results.

These aspects are what editors are looking for when selecting papers for peer review.

Implications and recommendations are, thus, written at the end of the discussion section, and before the concluding paragraph. They help to “wrap up” your paper. Once your reader understands what you found, the next logical step is what those results mean and what should come next.

Then they can take the baton, in the form of your work, and run with it. That gets you cited and extends your impact!

The order of implications and recommendations also matters. Both are written after you've summarized your main findings in the discussion section. Then, those results are interpreted based on ongoing work in the field. After this, the implications are stated, followed by the recommendations.

Writing an academic research paper is a bit like running a race. Finish strong, with your most important conclusion (recommendation) at the end. Leave readers with an understanding of your work's importance. Avoid generic, obvious phrases like "more research is needed to fully address this issue." Be specific.

The main differences between implications and recommendations (table)

Now let's dig a bit deeper into actually how to write these parts.

What are implications?

Research implications tell us how and why your results are important for the field at large. They help answer the question of “what does it mean?” Implications tell us how your work contributes to your field and what it adds to it. They're used when you want to tell your peers why your research is important for ongoing theory, practice, policymaking, and for future research.

Crucially, your implications must be evidence-based. This means they must be derived from the results in the paper.

Implications are written after you've summarized your main findings in the discussion section. They come before the recommendations and before the concluding paragraph. There is no specific section dedicated to implications. They must be integrated into your discussion so that the reader understands why the results are meaningful and what they add to the field.

A good strategy is to separate your implications into types. Implications can be social, political, technological, related to policies, or others, depending on your topic. The most frequently used types are theoretical and practical. Theoretical implications relate to how your findings connect to other theories or ideas in your field, while practical implications are related to what we can do with the results.

Key features of implications

• State the impact your research makes
• Helps us understand why your results are important
• Must be evidence-based
• Written in the discussion, before recommendations
• Can be theoretical, practical, or other (social, political, etc.)

Examples of implications

Let's take a look at some examples of research results below with their implications.

The result : one study found that learning items over time improves memory more than cramming material in a bunch of information at once .

The implications : This result suggests memory is better when studying is spread out over time, which could be due to memory consolidation processes.

The result : an intervention study found that mindfulness helps improve mental health if you have anxiety.

The implications : This result has implications for the role of executive functions on anxiety.

The result : a study found that musical learning helps language learning in children .

The implications : these findings suggest that language and music may work together to aid development.

What are recommendations?

As noted above, explaining how your results contribute to the real world is an important part of a successful article.

Likewise, stating how your findings can be used to improve something in future research is equally important. This brings us to the recommendations.

Research recommendations are suggestions and solutions you give for certain situations based on your results. Once the reader understands what your results mean with the implications, the next question they need to know is "what's next?"

Recommendations are calls to action on ways certain things in the field can be improved in the future based on your results. Recommendations are used when you want to convey that something different should be done based on what your analyses revealed.

Similar to implications, recommendations are also evidence-based. This means that your recommendations to the field must be drawn directly from your results.

The goal of the recommendations is to make clear, specific, and realistic suggestions to future researchers before they conduct a similar experiment. No matter what area your research is in, there will always be further research to do. Try to think about what would be helpful for other researchers to know before starting their work.

Recommendations are also written in the discussion section. They come after the implications and before the concluding paragraphs. Similar to the implications, there is usually no specific section dedicated to the recommendations. However, depending on how many solutions you want to suggest to the field, they may be written as a subsection.

Key features of recommendations

• Statements about what can be done differently in the field based on your findings
• Must be realistic and specific
• Written in the discussion, after implications and before conclusions
• Related to both your field and, preferably, a wider context to the research

Examples of recommendations

Here are some research results and their recommendations.

A meta-analysis found that actively recalling material from your memory is better than simply re-reading it .

• The recommendation: Based on these findings, teachers and other educators should encourage students to practice active recall strategies.

A medical intervention found that daily exercise helps prevent cardiovascular disease .

• The recommendation: Based on these results, physicians are recommended to encourage patients to exercise and walk regularly. Also recommended is to encourage more walking through public health offices in communities.

A study found that many research articles do not contain the sample sizes needed to statistically confirm their findings .

The recommendation: To improve the current state of the field, researchers should consider doing power analysis based on their experiment's design.

What else is important about implications and recommendations?

When writing recommendations and implications, be careful not to overstate the impact of your results. It can be tempting for researchers to inflate the importance of their findings and make grandiose statements about what their work means.

Remember that implications and recommendations must be coming directly from your results. Therefore, they must be straightforward, realistic, and plausible.

Another good thing to remember is to make sure the implications and recommendations are stated clearly and separately. Do not attach them to the endings of other paragraphs just to add them in. Use similar example phrases as those listed in the table when starting your sentences to clearly indicate when it's an implication and when it's a recommendation.

When your peers, or brand-new readers, read your paper, they shouldn't have to hunt through your discussion to find the implications and recommendations. They should be clear, visible, and understandable on their own.

That'll get you cited more, and you'll make a greater contribution to your area of science while extending the life and impact of your work.

The AJE Team

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Conducting Biosocial Surveys: Collecting, Storing, Accessing, and Protecting Biospecimens and Biodata (2010)

Chapter: 5 findings, conclusions, and recommendations, 5 findings, conclusions, and recommendations.

A s the preceding chapters have made clear, incorporating biological specimens into social science surveys holds great scientific potential, but also adds a variety of complications to the tasks of both individual researchers and institutions. These complications arise in a number of areas, including collecting, storing, using, and distributing biospecimens; sharing data while protecting privacy; obtaining informed consent from participants; and engaging with Institutional Review Boards (IRBs). Any effort to make such research easier and more effective will need to address the issues in these areas.

In considering its recommendations, the panel found it useful to think of two categories: (1) recommendations that apply to individual investigators, and (2) recommendations that are addressed to the National Institute on Aging (NIA) or other institutions, particularly funding agencies. Researchers who wish to collect biological specimens with social science data will need to develop new skills in a variety of areas, such as the logistics of specimen storage and management, the development of more diverse informed consent forms, and ways of dealing with the disclosure risks associated with sharing biogenetic data. At the same time, NIA and other funding agencies must provide researchers the tools they need to succeed. These tools include such things as biorepositories for maintaining and distributing specimens, better guidance on informed consent policies, and better ways to share data without risking confidentiality.

TAKING ADVANTAGE OF EXISTING EXPERTISE

Although working with biological specimens will be new and unfamiliar to many social scientists, it is an area in which biomedical researchers have a great deal of expertise and experience. Many existing documents describe recommended procedures and laboratory practices for the handling of biospecimens. These documents provide an excellent starting point for any social scientist who is interested in adding biospecimens to survey research.

Recommendation 1: Social scientists who are planning to add biological specimens to their survey research should familiarize themselves with existing best practices for the collection, storage, use, and distribution of biospecimens. First and foremost, the design of the protocol for collec tion must ensure the safety of both participants and survey staff (data and specimen collectors and handlers).

Although existing best-practice documents were not developed with social science surveys in mind, their guidelines have been field-tested and approved by numerous IRBs and ethical oversight committees. The most useful best-practice documents are updated frequently to reflect growing knowledge and changing opinions about the best ways to collect, store, use, and distribute biological specimens. At the same time, however, many issues arising from the inclusion of biospecimens in social science surveys are not fully addressed in the best-practice documents intended for biomedical researchers. For guidance on these issues, it will be necessary to seek out information aimed more specifically at researchers at the intersection of social science and biomedicine.

COLLECTING, STORING, USING, AND DISTRIBUTING BIOSPECIMENS

As described in Chapter 2 , the collection, storage, use, and distribution of biospecimens and biodata are tasks that are likely to be unfamiliar to many social scientists and that raise a number of issues with which even specialists are still grappling. For example, which biospecimens in a repository should be shared, given that in most cases the amount of each specimen is limited? And given that the available technology for cost-efficient analysis of biospecimens, particularly genetic analysis, is rapidly improving, how much of any specimen should be used for immediate research and analysis, and how much should be stored for analysis at a later date? Collecting, storing, using, and distributing biological specimens also present significant practical and financial challenges for social scientists. Many of the questions they must address, such as exactly what should be held, where it should be held, and what should be shared or distributed, have not yet been resolved.

Developing Data Sharing Plans

An important decision concerns who has access to any leftover biospecimens. This is a problem more for biospecimens than for biodata because in most cases, biospecimens can be exhausted. Should access be determined according to the principle of first funded, first served? Should there be a formal application process for reviewing the scientific merits of a particular investigation? For studies that involve international collaboration, should foreign investigators have access? And how exactly should these decisions be made? Recognizing that some proposed analyses may lie beyond the competence of the original investigators, as well as the possibility that principal investigators may have a conflict of interest in deciding how to use any remaining biospecimens, one option is for a principal investigator to assemble a small scientific committee to judge the merits of each application, including the relevance of the proposed study to the parent study and the capacities of the investigators. Such committees should publish their review criteria to help prospective applicants. A potential problem with such an approach, however, is that many projects may not have adequate funding to carry out such tasks.

Recommendation 2: Early in the planning process, principal investigators who will be collecting biospecimens as part of a social science survey should develop a complete data sharing plan.

This plan should spell out the criteria for allowing other researchers to use (and therefore deplete) the available stock of biospecimens, as well as to gain access to any data derived therefrom. To avoid any appearance of self-interest, a project might empower an external advisory board to make decisions about access to its data. The data sharing plan should also include provisions for the storage and retrieval of biospecimens and clarify how the succession of responsibility for and control of the biospecimens will be handled at the conclusion of the project.

Recommendation 3: NIA (or preferably the National Institutes of Health [NIH]) should publish guidelines for principal investigators containing a list of points that need to be considered for an acceptable data sharing plan. In addition to staff review, Scientific Review Panels should read and comment on all proposed data sharing plans. In much the same way as an unacceptable human subjects plan, an inadequate data sharing plan should hold up an otherwise acceptable proposal.

Supporting Social Scientists in the Storage of Biospecimens

The panel believes that many social scientists who decide to add the collection of biospecimens to their surveys may be ill equipped to provide for the storage and distribution of the specimens.

Conclusion: The issues related to the storage and distribution of biospecimens are too complex and involve too many hidden costs to assume that social scientists without suitable knowledge, experience, and resources can handle them without assistance.

Investigators should therefore have the option of delegating the storage and distribution of biospecimens collected as part of social science surveys to a centralized biorepository. Depending on the circumstances, a project might choose to utilize such a facility for immediate use, long-term or archival storage, or not at all.

Recommendation 4: NIA and other relevant funding agencies should support at least one central facility for the storage and distribution of biospecimens collected as part of the research they support.

PROTECTING PRIVACY AND CONFIDENTIALITY: SHARING DIGITAL REPRESENTATIONS OF BIOLOGICAL AND SOCIAL DATA

Several different types of data must be kept confidential: survey data, data derived from biospecimens, and all administrative and operational data. In the discussion of protecting confidentiality and privacy, this report has focused on biodata, but the panel believes it is important to protect all the data collected from survey participants. For many participants, for example, data on wealth, earnings, or sexual behavior can be as or more sensitive than genetic data.

Conclusion: Although biodata tend to receive more attention in discussions of privacy and confidentiality, social science and operational data can be sensitive in their own right and deserve similar attention in such discussions.

Protecting the participants in a social science survey that collects biospecimens requires securing the data, but data are most valuable when they are made available to researchers as widely as possible. Thus there is an inherent tension between the desire to protect the privacy of the participants and the desire to derive as much scientific value from the data as possible, particularly since the costs of data collection and analysis are so high. The following recommendations regarding confidentiality are made in the spirit of balancing these equally important needs.

Genomic data present a particular challenge. Several researchers have demonstrated that it is possible to identify individuals with even modest amounts of such data. When combined with social science data, genomic data may pose an even greater risk to confidentiality. It is difficult to know how much or which genomic data, when combined with social science data, could become critical identifiers in the future. Although the problem is most significant with genomic data, similar challenges can arise with other kinds of data derived from biospecimens.

Conclusion: Unrestricted distribution of genetic and other biodata risks violating promises of confidentiality made to research participants.

There are two basic approaches to protecting confidentiality: restricting data and restricting access. Restricting data—for example, by stripping individual and spatial identifiers and modifying the data to make it difficult or impossible to trace them back to their source—usually makes it possible to release social science data widely. In the case of biodata, however, there is no answer to how little data is required to make a participant uniquely identifiable. Consequently, any release of biodata must be carefully managed to protect confidentiality.

Recommendation 5: No individual-level data containing uniquely identify ing variables, such as genomic data, should be publicly released without explicit informed consent.

Recommendation 6: Genomic data and other individual-level data con taining uniquely identifying variables that are stored or in active use by investigators on their institutional or personal computers should be encrypted at all times.

Even if specific identifying variables, such as names and addresses, are stripped from data, it is still often possible to identify the individuals associated with the data by other means, such as using the variables that remain (age, sex, marital status, family income, etc.) to zero in on possible candidates. In the case of biodata that do not uniquely identify individuals and can change with time, such as blood pressure and physical measurements, it may be possible to share the data with no more protection than stripping identifying variables. Even these data, however, if known to intruders, can increase identification disclosure risk when combined with enough other data. With sufficient characteristics to match, intruders can uniquely identify individuals in shared data if given access to another data source that contains the same information plus identifiers.

Conclusion: Even nonunique biodata, if combined with social science data, may pose a serious risk of reidentification.

In the case of high-dimensional genomic data, standard disclosure limitation techniques, such as data perturbation, are not effective with respect to preserving the utility of the data because they involve such extreme alterations that they would severely distort analyses aimed at determining gene–gene and gene–environment interactions. Standard disclosure limitation methods could be used to generate public-use data sets that would enable low-dimensional analyses involving genes, for example, one gene at a time. However, with several such public releases, it may be possible for a key match to be used to construct a data set with higher-dimensional genomic data.

Conclusion: At present, no data restriction strategy has been demonstrated to protect confidentiality while preserving the usefulness of the data for drawing inferences involving high-dimensional interactions among genomic and social science variables, which are increasingly the target of research. Providing public-use genomic data requires such intense data masking to protect confidentiality that it would distort the high-dimensional analyses that could result in ground-breaking research progress.

Recommendation 7: Both rich genomic data acquired for research and sensitive and potentially identifiable social science data that do not change (or change very little) with time should be shared only under restricted circumstances, such as licensing and (actual or virtual) data enclaves.

As discussed in Chapter 3 , the four basic ways to restrict access to data are licensing, remote execution centers, data enclaves, and virtual data enclaves. Each has its advantages and disadvantages. 1 Licensing, for example, is the least restrictive for a researcher in terms of access to the data, but the licensing process itself can be lengthy and burdensome. Thus it would be useful if the licensing process could be facilitated.

Recommendation 8: NIA (or preferably NIH) should develop new stan dards and procedures for licensing confidential data in ways that will maximize timely access while maintaining security and that can be used by data repositories and by projects that distribute data.

Ways to improve the other approaches to restricted access are needed as well. For example, improving the convenience and availability of virtual data enclaves could increase the use of combined social science and biodata without

a significant increase in risk to confidentiality. The panel notes that much of the discussion of the confidentiality risk posed by the various approaches is theoretical; no one has a clear idea of just what disclosure risks are associated with the various ways of sharing data. It is important to learn more about these disclosure risks for a variety of reasons—determining how to minimize the risks, for instance, or knowing which approaches to sharing data pose the least risk. It would also be useful to be able to describe disclosure risks more accurately to survey participants.

Recommendation 9: NIA and other funding agencies should assess the strength of confidentiality protections through periodic expert audits of confidentiality and computer security. Willingness to participate in such audits should be a condition for receipt of NIA support. Beyond enforce ment, the purpose of such audits would be to identify challenges and solutions.

Evaluating risks and applying protection methods, whether they involve restricted access or restricted data, is a complex process requiring expertise in disclosure protection methods that exceeds what individual principal investigators and their institutions usually possess. Currently, not enough is known to be able to represent these risks either fully or accurately. The NIH requirement for data sharing necessitates a large investment of resources to anticipate which variables are potentially available to intruders and to alter data in ways that reduce disclosure risks while maintaining the utility of the data. Such resources are better spent by principal investigators on collecting and analyzing the data.

Recommendation 10: NIH should consider funding Centers of Excellence to explore new ways of protecting digital representations of data and to assist principal investigators wishing to share data with others. NIH should also support research on disclosure risks and limitations.

Principal investigators could send digital data to these centers, which would organize and manage any restricted access or restricted data policies or provide advisory services to investigators. NIH would maintain the authority to penalize those who violated any confidentiality agreements, for example, by denying them or their home institution NIH funding. Models for these centers include the Inter-university Consortium for Political and Social Research (ICPSR) and its projects supported by NIH and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the UK data sharing archive. The centers would alleviate the burden of data sharing as mandated of principal investigators by NIH and place it in expert hands. However, excellence in the design of data access and control systems

is likely to require intimate knowledge of each specific data resource, so data producers should be involved in the systems’ development.

INFORMED CONSENT

As described in Chapter 4 , informed consent is a complex subject involving many issues that are still being debated; the growing power of genetic analysis techniques and bioinformatics has only added to this complexity. Given the rapid pace of advances in scientific knowledge and in the technology used to analyze biological materials, it is impossible to predict what information might be gleaned from biological specimens just a few years hence; accordingly, it is impossible, even in theory, to talk about perfectly informed consent. The best one can hope for is relatively well-informed consent from a study’s participants, but knowing precisely what that means is difficult. Determining the scope of informed consent adds another layer of complexity. Will new analyses be covered under the existing consent, for example? There are no clear guidelines on such questions, yet specific details on the scope of consent will likely affect an IRB’s reaction to a study proposal.

What Individual Researchers Need to Know and Do Regarding Informed Consent

To be sure, there is a wide range of views about the practicality of providing adequate protection to participants while proceeding with the scientific enterprise, from assertions that it is simply not possible to provide adequate protection to offers of numerous procedural safeguards but no iron-clad guarantees. This report takes the latter position—that investigators should do their best to communicate adequately and accurately with participants, to provide procedural safeguards to the extent possible, and not to promise what is not possible. 2 Social science researchers need to know that adding the collection of biospecimens to social science surveys changes the nature of informed consent. Informed consent for a traditional social science survey may entail little more than reading a short script over the phone and asking whether the participant is willing to continue; obtaining informed consent for the collection and use of biospecimens and biodata is generally a much more involved process.

Conclusion: Social scientists should be made aware that the process of obtaining informed consent for the use of biospecimens and biodata typically differs from social science norms.

If participants are to provide truly informed consent to taking part in any study, they must be given a certain minimum amount of information. They should be told, for example, what the purpose of the study is, how it is to be carried out, and what participants’ roles are. In addition, because of the unique risks associated with providing biospecimens, participants in a social science survey that involves the collection of such specimens should be provided with other types of information as well. In particular, they should be given detail on the storage and use of the specimens that relates to those risks and can assist them in determining whether to take part in the study.

Recommendation 11: In designing a consent form for the collection of biospecimens, in addition to those elements that are common to social science and biomedical research, investigators should ensure that certain other information is provided to participants:

how long researchers intend to retain their biospecimens and the genomic and other biodata that may be derived from them;

both the risks associated with genomic data and the limits of what they can reveal;

which other researchers will have access to their specimens, to the data derived therefrom, and to information collected in a survey questionnaire;

the limits on researchers’ ability to maintain confidentiality;

any potential limits on participants’ ability to withdraw their speci mens or data from the research;

the penalties 3 that may be imposed on researchers for various types of breaches of confidentiality; and

what plans have been put in place to return to them any medically relevant findings.

Researchers who fail to properly plan for and handle all of these issues before proceeding with a study are in essence compromising assurances under informed consent. The literature on informed consent emphasizes the importance of ensuring that participants understand reasonably well what they are consenting to. This understanding cannot be taken for granted, particularly as it pertains to the use of biological specimens and the data derived therefrom.

While it is not possible to guarantee that participants have a complete understanding of the scientific uses of their specimens or all the possible risks of their participation, they should be able to make a relatively well-informed decision about whether to take part in the study. Thus the ability of various participants to understand the research and the informed consent process must be considered. Even impaired individuals may be able to participate in research if their interests are protected and they can do so only through proxy consent. 4

Recommendation 12: NIA should locate and publicize positive examples of the documentation of consent processes for the collection of biospeci mens. In particular, these examples should take into account the special needs of certain individuals, such as those with sensory problems and the cognitively impaired.

Participants in a biosocial survey are likely to have different levels of comfort concerning how their biospecimens and data will be used. Some may be willing to provide only answers to questions, for example, while others may both answer questions and provide specimens. Among those who provide specimens, some may be willing for the specimens to be used only for the current study, while others may consent to their use in future studies. One effective way to deal with these different comfort levels is to offer a tiered approach to consent that allows participants to determine just how their specimens and data will be used. Tiers might include participating in the survey, providing specimens for genetic and/or nongenetic analysis in a particular study, and allowing the specimens and data to be stored for future uses (genetic and/or nongenetic). For those participants who are willing to have their specimens and data used in future studies, researchers should tell them what sort of approval will be obtained for such use. For example, an IRB may demand reconsent, in which case participants may have to be contacted again before their specimens and data can be used. Ideally, researchers should design their consent forms to avoid the possibility that an IRB will demand a costly or infeasible reconsent process.

Recommendation 13: Researchers should consider adopting a tiered approach to obtaining consent. Participants who are willing to have their specimens and data used in future studies should be informed about the process that will be used to obtain approval for such uses.

What Institutions Should Do Regarding Informed Consent

Because the details of informed consent vary from study to study, individual investigators must bear ultimate responsibility for determining the details of informed consent for any particular study. Thus researchers must understand the various issues and concerns surrounding informed consent and be prepared to make decisions about the appropriate approach for their research in consultation with staff of survey organizations. These decisions should be addressed in the training of survey interviewers. As noted above, however, the issues surrounding informed consent are complex and not completely resolved, and researchers have few options for learning about informed consent as it applies to social science studies that collect biospecimens. Thus it makes sense for agencies funding this research, the Office for Human Research Protection (OHRP), or other appropriate organizations (for example, Public Responsibility in Medicine and Research [PRIM&R]) to provide opportunities for such learning, taking into account the fact that the issues arising in biosocial research do not arise in the standard informed consent situations encountered in social science research. It should also be made clear that the researchers’ institution is usually deemed (e.g., in the courts) to bear much of the responsibility for informed consent.

Recommendation 14: NIA, OHRP, and other appropriate organizations should sponsor training programs, create training modules, and hold informational workshops on informed consent for investigators, staff of survey organizations, including field staff, administrators, and mem bers of IRBs who oversee surveys that collect social science data and biospecimens.

The Return of Medically Relevant Information

An issue related to informed consent is how much information to provide to survey participants once their biological specimens have been analyzed and in particular, how to deal with medically relevant information that may arise from the analysis. What, for example, should a researcher do if a survey participant is found to have a genetic disease that does not appear until later in life? Should the participant be notified? Should participants be asked as part of the initial interview whether they wish to be notified about such a discovery? At this time, there are no generally agreed-upon answers to such questions, but researchers should expect to have to deal with these issues as they analyze the data derived from biological specimens.

Recommendation 15: NIH should direct investigators to formulate a plan in advance concerning the return of any medically relevant findings to

survey participants and to implement that plan in the design and conduct of their informed consent procedures.

INSTITUTIONAL REVIEW BOARDS

Investigators seeking IRB approval for biosocial research face a number of challenges. Few IRBs are familiar with both social and biological science; thus, investigators may find themselves trying to justify standard social science protocols to a biologically oriented IRB or explaining standard biological protocols to an IRB that is used to dealing with social science—or sometimes both. Researchers can expect these obstacles, which arise from the interdisciplinary nature of their work, to be exacerbated by a number of other factors that are characteristic of IRBs in general (see Chapter 4 ).

Recommendation 16: In institutions that have separate biomedical and social science IRBs, mechanisms should be created for sharing expertise during the review of biosocial protocols. 5

What Individual Researchers Need to Do Regarding IRBs

Because the collection of biospecimens as part of social science surveys is still relatively unfamiliar to many IRBs, researchers planning such a study can expect their interactions with the IRB overseeing the research to involve a certain learning curve. The IRB may need extra time to become familiar and comfortable with the proposed practices of the survey, and conversely, the researchers will need time to learn what the IRB will require. Thus it will be advantageous if researchers conducting such studies plan from the beginning to devote additional time to working with their IRBs.

Recommendation 17: Investigators considering collecting biospecimens as part of a social science survey should consult with their IRBs early and often.

What Research Agencies Should Do Regarding IRBs

One way to improve the IRB process would be to give members of IRBs an opportunity to learn more about biosocial research and the risks it entails.

This could be done by individual institutions, but it would be more effective if a national funding agency took the lead (see Recommendation 14).

It is the panel’s hope that its recommendations will support the incorporation of social science and biological data into empirical models, allowing researchers to better document the linkages among social, behavioral, and biological processes that affect health and other measures of well-being while avoiding or minimizing many of the challenges that may arise. Implementing these recommendations will require the combined efforts of both individual investigators and the agencies that support them.

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Recent years have seen a growing tendency for social scientists to collect biological specimens such as blood, urine, and saliva as part of large-scale household surveys. By combining biological and social data, scientists are opening up new fields of inquiry and are able for the first time to address many new questions and connections. But including biospecimens in social surveys also adds a great deal of complexity and cost to the investigator's task. Along with the usual concerns about informed consent, privacy issues, and the best ways to collect, store, and share data, researchers now face a variety of issues that are much less familiar or that appear in a new light.

In particular, collecting and storing human biological materials for use in social science research raises additional legal, ethical, and social issues, as well as practical issues related to the storage, retrieval, and sharing of data. For example, acquiring biological data and linking them to social science databases requires a more complex informed consent process, the development of a biorepository, the establishment of data sharing policies, and the creation of a process for deciding how the data are going to be shared and used for secondary analysis—all of which add cost to a survey and require additional time and attention from the investigators. These issues also are likely to be unfamiliar to social scientists who have not worked with biological specimens in the past. Adding to the attraction of collecting biospecimens but also to the complexity of sharing and protecting the data is the fact that this is an era of incredibly rapid gains in our understanding of complex biological and physiological phenomena. Thus the tradeoffs between the risks and opportunities of expanding access to research data are constantly changing.

Conducting Biosocial Surveys offers findings and recommendations concerning the best approaches to the collection, storage, use, and sharing of biospecimens gathered in social science surveys and the digital representations of biological data derived therefrom. It is aimed at researchers interested in carrying out such surveys, their institutions, and their funding agencies.

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Article Contents

What does it mean for a recommendation to be evidence-based.

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Robert L Schmidt, Rachel E Factor, What Does it Mean for a Recommendation to be Evidence-Based?, Laboratory Medicine , Volume 50, Issue 1, February 2019, Pages 5–7, https://doi.org/10.1093/labmed/lmy071

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Clinical laboratories are under increasing pressure to demonstrate value. 1 Traditionally, value has been defined in terms of performance measures that are under the direct control of the laboratory, such as analytical performance, cost-efficiency, and operational performance (eg, turnaround time). However, laboratories can have a significant influence over the entire diagnostic process, from preanalytical test selection to postanalytical test interpretation, all of which can have a significant impact on value. For that reason, there is increasing scrutiny on the role of the laboratory role in the diagnostic process.

Recent studies have shown that there is wide variation in diagnostic practice, which is potentially harmful. This variation can be minimized if laboratories follow evidence-based recommendations. 2 , 3 , 4

What do we mean by evidence-based recommendations and how do we arrive at them? The term evidence-based refers to a decision process following a theoretic framework that determines relevant outcomes, prioritizes their importance, estimates the probability that each outcome will occur, estimates the costs and benefits of each outcome, evaluates whether the benefits of an intervention outweigh its harms and, finally, uses this information as a rationale for 1 or more recommendation(s) regarding the intervention. All the steps in the process should be systematic and transparent so that they can be evaluated.

Evidence-based decision making is not strictly algorithmic and recognizes that each situation is unique. Evidence-based recommendations provide a starting point that can be modified by the clinical context. Thus, evidence-based decisions must incorporate patient preferences and clinical judgment.

There are many systems for producing evidence-based recommendations, which has led to confusion about the meaning of the term evidence-based . Recently, the GRADE (Grading of Recommendations Assessment, Development and Evaluation) working group developed a systematic and explicit approach for developing evidence-based guidelines 5 that has been widely adopted. Our discussion will focus on the GRADE approach.

Although tests are used to provide information for a wide range of purposes (eg, diagnosis, prognosis, screening, and monitoring), the GRADE process is flexible and can be applied to developing recommendations for all of these purposes. Also, the GRADE process is applicable to all medical interventions and is not limited to clinical laboratory tests.

The first step in the process is to formulate the problem, usually as a question, and identify the important outcomes. 6 Clinical problems can be described using the PICO (population, intervention, comparison, outcome) framework modified for testing (population, index test, comparator or reference test, and outcome). This step is critical because it focuses the task and determines the type of evidence that is required.

The second step is to gather evidence. Data gathering should be systematic, comprehensive, and reproducible, following the well-established procedures for a systematic review.

The next step evaluates the quality of the evidence, which often depends on study type. Filtered evidence (systematic reviews and meta-analysis) is generally considered to be of higher-quality than unfiltered evidence (individual studies). Among individual studies, controlled studies are considered to contribute stronger evidence than observational studies which, in turn, are considered stronger than expert opinion, case studies, and narrative reviews. Randomized controlled trials (RCTs) are more reliable than observational studies, which, in turn, are more reliable than cohort and case-control studies. The hierarchy of evidence is primarily based on susceptibility to bias (eg, spectrum bias, operational bias, selection bias). Systematic reviews of RCTs are at the top of the hierarchy because they are least susceptible to bias. However, the design and execution of each study can influence susceptibility to bias, so having a method to critically appraise a study is important.

Scoring tools are now available (eg, QUADAS-2 [Quality Assessment of Diagnostic Accuracy Studies] for diagnostic accuracy studies) to guide the critical appraisal of many types of studies; however, these tools require that studies fully report the methods by which the results were obtained. This need has led to the development of reporting guidelines (eg, STARD [Standards for Reporting of Diagnostic Accuracy Studies] for diagnostic accuracy studies and REMARK [Reporting Recommendations for Tumor Marker Prognostic Studies] for prognostic biomarker studies) that list items that should be reported, to allow for critical appraisal. A full list of reporting guidelines is available at the EQUATOR website. 7

Many journals now require authors to demonstrate that they have followed an appropriate reporting guideline. Still, some disciplines have been slow to adopt reporting guidelines, and adherence is often poor even when journals require them. 8-10

Quality refers to the certainty of evidence. High-quality evidence is unlikely to be changed, whereas lower-quality evidence may change with future research findings or may not be applicable in different settings. Quality is initially based on the study type and modified with additional considerations. Quality is ranked lower if there are threats to bias, inconsistency between studies, indirectness, imprecision, or publication bias.

Indirectness refers to the applicability of the available evidence to the clinical question and is assessed by determining the relevance of the PICO parameters to the clinical problem at hand. 11 Quality is ranked higher if there is a large effect size.

The GRADE system rates the quality of a body of evidence for a specific outcome using 4 categories (very low, low, moderate, and high). The grading process is transparent and, to the extent possible, standardized and objective. In general, different decision makers should reach similar conclusions regarding the quality of evidence for an outcome. See several publications for a more detailed explanation of the GRADE process for evaluation of evidence. 5 , 11-14 In particular, we draw attention to 2 articles that focus on strength of evidence for diagnostic recommendations. 15 , 16

The strength of recommendation depends on the balance between desirable and undesirable outcomes. This assessment depends on relative effect sizes, preferences of patients, and the confidence in the estimates of effect sizes and preferences. GRADE provides a systematic and transparent method for assessing the strength of recommendations.

Most publications in laboratory medicine focus on relatively simple outcomes related to analytical effectiveness and clinical performance. 17 However, laboratory guidelines would benefit from familiarity with the GRADE process, to focus evidence on important outcomes. Because they are focused on outcomes, evidence-based recommendations provide a foundation on which laboratories can deliver value.

Abbreviations:

Grading of Recommendations Assessment, Development and Evaluation

population, intervention, comparison, outcome

randomized controlled trial

Quality Assessment of Diagnostic Accuracy Studies

Standards for Reporting of Diagnostic Accuracy Studies

Reporting Recommendations for Tumor Marker Prognostic Studies

Dr Schmidt is an Associate Professor of Pathology at the University of Utah School of Medicine in Salt Lake City. He received his MD, MS in Clinical Epidemiology and his graduate diploma in Biostatistics from the University of Sydney in Australia, an MBA from the University of Chicago in Illinois, and a PhD in Operations Management from the University of Virginia in Charlottesville. He also earned an MS in biochemical engineering from the Massachusetts Institute of Technology in Cambridge and a graduate diploma in Pharmaceutical Medicine from the University of New South Wales in Sydney, Australia. He is board certified in Clinical Pathology and Clinical Informatics. Dr Schmidt is Director of the Center for Effective Medical Testing, which performs studies on cost-effectiveness and evidence-based evaluation of diagnostic testing. He is also Medical Director of Quality Optimization at ARUP Laboratories in Salt Lake City, Utah. His research and clinical activities focus on statistical and economic analysis of diagnostic testing and laboratory operations.

Dr Factor is an Associate Professor of Pathology, the Director of Breast Pathology, and the Co-Director of the Cytopathology Fellowship Program at the University of Utah School of Medicine in Salt Lake City. She received her MHSc degree from Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland, and her MD from the Albert Einstein College of Medicine in Bronx, New York, followed by residency and fellowships at Brigham and Women’s Hospital in Boston, Massachusetts. Dr Factor is board certified in Anatomic Pathology and Cytopathology, and is a member of the College of American Pathology, the United States and Canadian Academy of Pathology, and the American Society for Clinical Pathology. Her research interests include the biology and prevention of breast cancer.

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Schmidt RL , LoPresti JS , McDermott MT , Zick SM , Straseski JA . Does reverse triiodothyronine testing have clinical utility? an analysis of practice variation based on order data from a national reference laboratory . Thyroid 2018 ; 28 ( 7 ): 242 – 248 .

Guyatt G , Oxman AD , Akl EA , et al.  GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables . J Clin Epidemiol . 2011 ; 64 ( 4 ): 383 – 394 .

Guyatt GH , Oxman AD , Kunz R , et al.  GRADE guidelines: 2. Framing the question and deciding on important outcomes . J Clin Epidemiol . 2011 ; 64 ( 4 ): 395 – 400 .

EQUATOR network . http://www.equator-network.org/ . Accessed October 26, 2018 .

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Caron JE , March JK , Cohen MB , Schmidt RL . A survey of the prevalence and impact of reporting guideline endorsement in pathology journals . Am J Clin Pathol . 2017 ; 148 ( 4 ): 314 – 322 .

Hopewell S , Boutron I , Altman DG , Ravaud P . Deficiencies in the publication and reporting of the results of systematic reviews presented at scientific medical conferences . J Clin Epidemiol . 2015 ; 68 ( 12 ): 1488 – 1495 .

Turner L , Shamseer L , Altman DG , Schulz KF , Moher D . Does use of the CONSORT statement impact the completeness of reporting of randomised controlled trials published in medical journals? A Cochrane review . Syst Rev . 2012 ; 1 : 60 .

Guyatt GH , Oxman AD , Kunz R , et al.  ; GRADE Working Group . GRADE guidelines: 8. Rating the quality of evidence–indirectness . J Clin Epidemiol . 2011 ; 64 ( 12 ): 1303 – 1310 .

Guyatt GH , Oxman AD , Sultan S , et al.  ; GRADE Working Group . GRADE guidelines: 9. Rating up the quality of evidence . J Clin Epidemiol . 2011 ; 64 ( 12 ): 1311 – 1316 .

Guyatt GH , Oxman AD , Kunz R , et al.  ; GRADE Working Group . GRADE guidelines: 7. Rating the quality of evidence–inconsistency . J Clin Epidemiol . 2011 ; 64 ( 12 ): 1294 – 1302 .

Schünemann HJ , Oxman AD , Brozek J , et al.  GRADE: assessing the quality of evidence for diagnostic recommendations . ACP J Club . 2008 ; 149 ( 6 ): 2 .

Schünemann HJ , Oxman AD , Brozek J , et al.  GRADE: assessing the quality of evidence for diagnostic recommendations . Evid Based Med . 2008 ; 13 ( 6 ): 162 – 163 .

Trenti T , Schünemann HJ , Plebani M . Developing GRADE outcome-based recommendations about diagnostic tests: a key role in laboratory medicine policies . Clin Chem Lab Med . 2016 ; 54 ( 4 ): 535 – 543 .

Horvath AR , Lord SJ , StJohn A , et al.  ; Test Evaluation Working Group of the European Federation of Clinical Chemistry Laboratory Medicine . From biomarkers to medical tests: the changing landscape of test evaluation . Clin Chim Acta . 2014 ; 427 : 49 – 57 .

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• Open access
• Published: 08 February 2022

Six practical recommendations for improved implementation outcomes reporting

• Rebecca Lengnick-Hall   ORCID: orcid.org/0000-0002-7900-0585 1 ,
• Donald R. Gerke 2 ,
• Enola K. Proctor 3 ,
• Alicia C. Bunger 4 ,
• Rebecca J. Phillips 4 ,
• Jared K. Martin 5 &
• Julia C. Swanson 1  

Implementation Science volume  17 , Article number:  16 ( 2022 ) Cite this article

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Implementation outcomes research spans an exciting mix of fields, disciplines, and geographical space. Although the number of studies that cite the 2011 taxonomy has expanded considerably, the problem of harmony in describing outcomes persists. This paper revisits that problem by focusing on the clarity of reporting outcomes in studies that examine them. Published recommendations for improved reporting and specification have proven to be an important step in enhancing the rigor of implementation research. We articulate reporting problems in the current implementation outcomes literature and describe six practical recommendations that address them.

Recommendations

Our first recommendation is to clearly state each implementation outcome and provide a definition that the study will consistently use. This includes providing an explanation if using the taxonomy in a new way or merging terms. Our second recommendation is to specify how each implementation outcome will be analyzed relative to other constructs. Our third recommendation is to specify “the thing” that each implementation outcome will be measured in relation to. This is especially important if you are concurrently studying interventions and strategies, or if you are studying interventions and strategies that have multiple components. Our fourth recommendation is to report who will provide data and the level at which data will be collected for each implementation outcome, and to report what kind of data will be collected and used to assess each implementation outcome. Our fifth recommendation is to state the number of time points and frequency at which each outcome will be measured. Our sixth recommendation is to state the unit of observation and the level of analysis for each implementation outcome.

This paper advances implementation outcomes research in two ways. First, we illustrate elements of the 2011 research agenda with concrete examples drawn from a wide swath of current literature. Second, we provide six pragmatic recommendations for improved reporting. These recommendations are accompanied by an audit worksheet and a list of exemplar articles that researchers can use when designing, conducting, and assessing implementation outcomes studies.

Peer Review reports

Contributions to the literature

Rigorous and consistent reporting of implementation outcomes is necessary for synthesizing insights about the effectiveness of implementation strategies across studies conducted in diverse contexts.

It is also necessary for building strong theory about implementation mechanisms.

This manuscript describes common reporting problems identified in the implementation outcomes literature.

We offer six recommendations for improving implementation outcome reporting. We also provide two tools (an audit worksheet and a list of exemplar articles) to help readers put these recommendations into practice.

Implementation researchers study the process of transitioning evidence-based interventions from controlled research environments to real-world practice settings [ 1 ]. A primary focus of implementation science is the study of implementation outcomes, defined as “the effects of deliberate and purposive actions to implement new interventions” [ 2 ]. Implementation outcomes are used to evaluate implementation success and processes and are often employed as intermediate outcomes in studies of intervention effectiveness or quality [ 3 ]. To clarify and standardize terminology and to promote increased rigor in implementation science, a 2011 paper [ 3 ] put forward a research agenda and a taxonomy of eight discrete implementation outcomes: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability.

Acceptability is the perception among stakeholders that a given treatment, service, practice, or innovation is agreeable, palatable, or satisfactory [ 3 ]. Adoption is the intent, initial decision, or action to try or employ an innovation or evidence-based practice; also referred to as uptake [ 3 ]. Appropriateness is the perceived fit, relevance, or compatibility of an innovation or evidence-based practice for a given practice setting, provider, or consumer; and/or perceived fit of the innovation to address a particular issue or problem [ 3 ]. Feasibility is the extent to which a new treatment or an innovation can be successfully used or carried out in a given setting [ 3 ]. Fidelity is the degree to which an intervention was implemented as it was prescribed in the original protocol or as was intended by program developers [ 3 ]. Implementation cost is the cost impact of an implementation effort [ 3 ]. Penetration is the integration or saturation of an intervention within a service setting and its subsystems; calculated as a ratio of those to whom the intervention is delivered divided by the number of eligible or potential recipients [ 3 ]. Last, sustainability is the extent to which a newly implemented treatment is maintained or institutionalized within a service setting’s ongoing, stable operations [ 3 ].

Implementation outcomes research spans an exciting mix of fields, disciplines, and geographical space. In the last 10 years, the 2011 paper was cited over 1600 times in Web of Science. Implementation outcomes research can be found in primary and hospital care [ 4 , 5 ], behavioral health [ 6 ], child welfare and parenting [ 7 , 8 ], HIV prevention and care [ 9 , 10 ], school-based services [ 11 , 12 ], and other settings. Moreover, the taxonomy proposed in the 2011 paper is included in funding announcements that guide implementation research design. One such example is the US National Institute of Health’s PAR-19-276 Dissemination and Implementation Research in Health [ 13 ], which has 17 participating institutes and specifies that the inclusion of implementation outcomes is an important component to include in funding proposals. Though the number of studies that cite the 2011 taxonomy has expanded considerably, the problem of harmony in describing outcomes persists. This paper revisits that problem by focusing on the clarity of reporting outcomes in studies that examine them.

Published recommendations for improved reporting and specification have proven to be an important step in enhancing the rigor of implementation research. The Standards for Reporting Implementation Studies (StaRI), for example, was developed in 2017 and includes 27 checklist items that disentangle the implementation strategy from the intervention [ 14 , 15 ]. The StaRI is intentionally open to the range of research designs and methods that implementation researchers use [ 14 , 15 ]. This contrasts with more narrowly focused reporting guidelines, like the Consolidated Standards of Reporting Trials for randomized control trials [ 16 , 17 , 18 ] and the Standard Protocol Items: Recommendations for Interventional Trials for clinical trial protocols [ 19 ]. Other reporting guidelines used in implementation research focus on “the thing” [ 20 ] being implemented. Examples include the Workgroup for Intervention Development and Evaluation Research recommendations [ 21 , 22 ] and the Template for Intervention Description and Replication checklist [ 23 ]. As Rudd and colleagues aptly explained, reporting guidelines are important because they improve the rigor and value of implementation research by supporting replication, research synthesis, and dissemination; this increases the speed at which practitioners can use empirical findings [ 24 ].

Paper goals

As part of a scoping review aimed at describing how the field conceptualized, measured, and advanced theory around implementation outcomes since 2011 [ 25 ], we identified 358 empirical studies published in peer-reviewed journals that cited the original paper and assessed one or more implementation outcomes. We found that while anchored by the conceptual definitions from the 2011 taxonomy, these studies varied substantially in how implementation outcomes were reported. This is a problem because variations limit the synthesis of knowledge and theory generation across studies, interventions, and settings. This also echoes concerns raised in the implementation outcomes research agenda set forth in 2011 [ 3 ]. The aims of this paper are to articulate reporting problems in the current implementation outcomes literature and describe six practical recommendations that address them. We also present an audit worksheet that readers can use to plan and assess their own work and a list of exemplar articles that readers can use as a reference. We hope that these tools can help build capacity among implementation researchers, and support reviewers and editors who are positioned to offer constructive feedback to grant writers and authors in service of advancing a harmonized literature on implementation outcomes.

How we identified reporting problems in the implementation outcomes literature

To identify current reporting problems and generate meaningful recommendations, we leveraged elements of the data charting stage of our scoping review methodology [ 25 ]. During data charting, team members trained in implementation research reviewed full-text articles to identify the implementation outcomes examined, measurement approaches, and other study details. However, when these study features were too unclear for basic data charting as this stage got underway, team members brought these issues to the whole team to discuss. This process led to significant refinements in the data charting form, and stronger consensus within the team about how to chart these studies considering the wide outcomes reporting variations. Yet, the team continued to encounter data charting difficulties and members requested additional consultation from the protocol authors for about 8% of the articles reviewed. This reflected a substantial number of articles with reporting issues that went beyond the expert team’s consensus building discussions and data charting form iterations. Most of the challenges discussed in the one-on-one consultation meetings via email or video conference (which were documented) were related to insufficient detail reported about the implementation outcomes.

To synthesize these reporting issues systematically across the coders, we held weekly meetings among protocol authors and conducted a full team meeting during the data charting process (after charting about 250 articles). During the weekly protocol author meetings, we discussed consultation issues and decisions that were made during the past week and began to identify themes and patterns related to reporting problems that were emerging across studies and team members. During our full team meeting, the first author presented an initial list of reporting problems and asked each person ( n = 7) to elaborate, add to the list with their own examples, and reflect upon the list’s alignment with their own coding experience. Detailed meeting notes and the Zoom chat transcript were retained to inform these recommendations.

Current problems and recommendations for improvement

We next present our list of six identified reporting problems and proposed recommendations to prevent them in future work. To accompany this, we created two tools. Additional file 1 is an audit worksheet that the reader can use to assess adherence to our proposed recommendations or to plan out the inclusion of implementation outcomes in potential work. In Additional file 2 , we provide exemplar articles that the reader may use as a guide and to generate ideas.

Recommendation 1: consistent term use

The 2011 paper noted widespread inconsistency in terminology for implementation outcomes and called for consistent use of language in describing key constructs [ 3 ]. Our review revealed that this problem prevails and can appear in the literature in three specific ways. One way was reporting different outcomes in different manuscript sections. In one article, for example, the stated study goal in the Introduction was to assess fidelity and sustainment. However, the authors only reported on fidelity in the “Methods” and “Results” section, never addressing results pertaining to sustainment. Whether these authors failed to distinguish between fidelity and sustainment conceptually and operationally, or whether the paper simply failed to address sustainment, the effect is the same: lack of clarity about the specific outcome being addressed. Inconsistent terminology prevents readers from knowing what construct was assessed and what exactly was learned—both of which prevent the accrual of information across studies.

Another way that this problem appeared was using terms from the 2011 taxonomy in a new way and without explanation. While the original taxonomy invited the identification and study of additional implementation outcomes, interchanged use of terms perpetuates confusion and impedes synthesis across studies [ 16 ]. Examples included an article where authors reported that they were assessing fidelity but called it uptake and an article where the definition of feasibility included the term acceptability. In both cases, an explanation as to why the outcome terms were applied in this way (while still citing the 2011 taxonomy) was absent.

Third, we found confusing instances of studies that merged implementation outcomes in the analysis and interpretation of results without explanation. For example, in one article, fidelity and acceptability were combined and called feasibility. In another, acceptability, feasibility, and appropriateness were combined into a variable called value. The 2011 research agenda described how implementation outcomes could be used in a “portfolio” of factors that explain implementation success [ 3 ]. For example, implementation success could be conceptualized as a combination of treatment effectiveness, acceptability, and sustainability [ 3 ]. However, understanding the role of implementation outcomes in mechanisms of change—including how we get to “implementation success” and what it looks like—requires precision in outcomes measurement and reporting. Until we have a stronger knowledge base, our field needs concepts to be disentangled rather than merged, absent compelling theory or evidence for combining. To address these reporting problems, our first recommendation is to clearly state each implementation outcome and provide an operational definition that the study will use. Ensure consistent use of outcomes terms and operational definitions across manuscript sections and provide an explanation if using the taxonomy in a new way or merging terms.

Recommendation 2: role in analysis

Another reporting problem is lack of specificity around how the outcome was measured relative to other constructs. This problem appeared as poor or unclear alignment between outcomes-related aims, research questions and/or hypotheses, and the reported results. One example of this was an article that aimed to examine fidelity, adoption, and cost across multiple phases of implementation. However, the authors assessed barriers to adoption instead of actual adoption and used the terms fidelity, engagement, and adoption interchangeably when reporting results on the intervention and implementation strategies. This made it difficult to assess the roles that different implementation outcomes played in the study. In another article, the authors stated that their qualitative interview guide “provided insight into” acceptability, adoption, and appropriateness of the practice of interest. However, the “Results” section did not include any information about these implementation outcomes, and they were not mentioned again until the discussion of future directions.

Our second recommendation is to specify how each implementation outcome will be or was analyzed relative to other constructs. Readers can draw upon the categories that we observed during data charting. For example, an implementation outcome may be treated as an independent, dependent, mediating, moderating, or descriptive variable. Correlations may be assessed between an implementation outcome and another implementation outcome or a contextual variable. An implementation outcome may be treated as a predictor of system or clinical outcomes, or as an outcome of a planned implementation strategy. Manuscripts that succinctly list research questions or study aims—detailing the outcome variables measured and their role in analyses—are easier to identify in literature searches, easier to digest, and contribute to the accrual of information about the attainment and effects of specific implementation outcomes.

Recommendation 3: referent

The next problem that we observed is difficulty identifying what “thing” [ 20 ] the implementation outcome is referring to. For example, in one article that examined both an intervention and an implementation strategy, the aims referred to feasibility and acceptability of the intervention. However, the “Results” section only reported on intervention acceptability and the “Discussion” section mentioned that acceptability and feasibility of the intervention using the implementation strategy were assessed. This example illustrates how study conclusions can be confusing when the implementation outcome referent is unclear. Another study compared different training approaches for promoting fidelity within a process improvement study. However, we were unable to discern whether fidelity was referring to the process improvement model, the training approaches, or both. As a result, it was difficult to assess which body of fidelity literature these findings pertained to.

As such, our third recommendation is to specify “the thing” [ 20 ] that each implementation outcome will be measured in relation to . This requires a thorough review of all manuscript sections and can be especially important if you are concurrently studying interventions and strategies (e.g., in a hybrid study [ 26 ]), or if you are studying interventions and strategies that have multiple components of interest. Coding options for “the thing” in our scoping review included screening, assessment, or diagnostic procedures (e.g., X-rays), one manualized treatment, program, or intervention (e.g., trauma-focused cognitive behavioral therapy), or multiple manualized interventions that are simultaneously implemented. We also observed that “the thing” may refer to research evidence or guidelines. It could be an administrative intervention (e.g., billing system, care coordination strategy, supervision approach), a policy, technology (e.g., health information technology, health app, data system), a form of outcome monitoring (e.g., measurement-based care for individual clients), data systems, indicators, or monitoring systems. Finally, “the thing” that an outcome is being measured in relation to may be a clinical pathway or service cascade intervention (e.g., screening, referral, treatment type of program).

Recommendation 4: data source and measurement

The fourth reporting problem is lack of detail around how the implementation outcome was measured, including what data were used. For instance, some studies drew upon participant recruitment and retention information to reflect feasibility without describing the way this information was obtained or recorded. The “Methods” section of another article stated that “project records” were used to assess fidelity without providing additional detail. Another example is an article in which the “Measures” section stated that survey items were created by the study team based on the 2011 taxonomy, including feasibility, acceptability, and sustainability. However, appropriateness was the only one that was clearly operationalized in the “Methods” section. Lack of information about data source and measurement limits transparency, the ability to understand the strengths and limitations of different measurement approaches for implementation outcomes, and replication.

To address this, our fourth recommendation is twofold. The first element of our recommendation is to report who provided data and the level at which data were collected for each implementation outcome . The reader can consider the following categories when reporting this information for their studies. Possible options for who reported the data for an implementation outcome include client/patient, individual provider, supervisor/middle manager, administrator/executive leader, policymaker, or another external partner. Possible options for the level at which data were collected include individual/self, team/peers, organization, or larger system environment and community. We found that implementation outcomes studies often drew upon multiple levels of data (see also “Recommendation 6: unit of analysis vs. unit of observation”). Furthermore, the level at which data were collected and the level at which data were reported may not be the same (e.g., individual providers reporting on an organizational level implementation outcome variable). To address this, the second element of our recommendation is to report what type of data was or will be collected and used to assess each implementation outcome . Data may be quantitative, qualitative, or both. Information may be collected from interviews, administrative data, observation, focus groups, checklists, self-reports, case audits, chart or electronic health record reviews, client reports, responses to vignettes, or a validated survey instrument or questionnaire.

Recommendation 5: timing and frequency

Another reporting problem that we encountered is lack of information about the timing and frequency of implementation outcome measurement. A fundamental principle of clear research reporting includes disclosing observation periods, times between observations, and number of times constructs are measured. Yet, our review of implementation outcome research was hampered by lack of such details. For example, in one article, self-assessments and independent assessments of fidelity were compared for a particular intervention. However, in the “Methods” section, fidelity assessments of both types were described as “completed during the last quarter” of a particular year. Without further detail, it was difficult to tell if these were cross-sectional fidelity assessments for unique providers or longitudinal data that tracked the same provider’s fidelity over time. Lack of detail about data collection timeframes limits researchers’ ability to assess the internal validity of study findings and the actual time that it takes to observe change in a given implementation outcome (and at a particular level of analysis). Therefore, our fifth recommendation is to state the number of time points and the frequency at which each outcome was or will be measured . Broad categories that the reader may consider include measuring the implementation outcome once (cross-sectional), twice (pre-post), or longitudinally (three or more time points are assessed). Reporting the phase [ 27 ] or stage [ 28 ] can also help to clarify when during the implementation lifecycle outcomes are observed or are most salient.

Recommendation 6: unit of analysis vs. unit of observation

The last problem we encountered is inconsistent or insufficient specification of the unit of analysis (the unit for which we make inferences about implementation outcomes) and the unit of observation (most basic unit observed to measure the implementation outcome). In multiple instances, studies relied on reports from individual providers or clinicians to make inferences about team or organizational implementation outcomes (e.g., aggregating observations about individual providers’ adoption to understand overall team adoption). However, in some studies, these distinctions between the units of analysis and observation were not clearly drawn, explained, or appropriate. For instance, in a study examining practitioners participating in a quality improvement initiative, the study team assessed group level sustainability by asking individual practitioners to discuss their perceptions of sustainability in interviews. It was not clear how the research team arrived at their conclusions about group level sustainability from individual reports, which limits transparency and replicability. Furthermore, the lack of clarity around units of observation and analysis muddles the causal pathways that we are trying to understand in implementations outcomes research because mechanisms of change may differ among individual, group, organizational, and system levels.

A related issue involved limited explanation as to why units of observations (e.g., individual’s perceptions of appropriateness) can and should be aggregated to reflect higher levels in the analysis (e.g., organizational level appropriateness). Aggregating individual level data to the group, team, or organizational level requires a strong theoretical justification that bottom-up processes exist to create a shared characteristic [ 29 ]. In the example, sufficient theory was needed to demonstrate that appropriateness was an organizational level construct (unit of analysis) and reflected a shared perception of appropriateness among individuals (unit of observation). This type of study also requires an analytic design that allows the researcher to rigorously test this assumption [ 29 ], including sufficient sample sizes to account for between-group effects [ 30 , 31 ]. During our data charting process, lack of clarity in how unit of observation and unit of analysis were distinguished and treated, and why, made it difficult to assess the presence of such considerations.

In response, our sixth recommendation is to state the unit of analysis and unit of observation for each implementation outcome. Observations may be generated by individual clients/patients, individual providers, teams, organizations, or another type of system. However, these observations may be aggregated in some way to reflect an implementation outcome at a higher level (e.g., assessing team adoption based on an aggregation of each individual member’s adoption). We urge the reader to ensure that the level of analysis theoretically and methodologically aligns with who provided data and the level of data collection described in in “Recommendation 4: data source and measurement” section. If conducting multilevel analyses and the units of observation and analysis are different, we also encourage the reader to include theoretical and analytical justification when aggregating implementation outcome data to a higher level of analysis [ 29 ].

Why do we need another set of recommendations for implementation research?

First, we believe that as the field of implementation science grows and matures, researchers should continually hold their work to higher reporting standards so that they can delve deeper in and contribute more specifically to the field. This is especially important because implementation outcomes research often involves multilevel transactional relationships and processes. Second, this paper was not an abstract theoretical exercise. The development of the proposed recommendations—and the realization of the need to do so—was directly informed by our hands-on experience conducting a scoping review [ 25 ]. Third, our recommendations can be used to elaborate upon outcomes-related sections of existing guidelines. For example, our recommendations can be layered onto StaRI components that mention implementation outcomes (checklist item #2 for the “Abstract” section) and outcomes more broadly (checklist items #11 and #12 for the evaluation components of “Methods” section and checklist item #18 for the “Results” section) [ 14 ]. Fourth, our recommendations provide a template for reviewers and editors who want to offer suggestions for improving the study’s contribution to the implementation outcomes knowledge base. Finally, and most importantly, the current lack of implementation outcomes reporting guidelines negatively affects the usability, rigor, and impact of implementation outcomes research.

What are potential challenges of using these recommendations?

First, individuals who conduct implementation outcomes research represent a wide range of professional backgrounds, research training, familiarity with terms, disciplinary standards of rigor, and study contexts. Elements of our recommendations may be more difficult to put into practice depending on the researcher’s background and the nature of “the thing” being assessed. Second, many studies that could contribute to the field of implementation science are designed to practically assist a specific population, rather than to intentionally generalize results for the sake of building the science. Third, following these reporting recommendations could lead to more content and challenges managing word and space limitations.

Our scoping review experience illustrated why implementation outcomes research is difficult to conduct, report—and perhaps—even more difficult to consolidate across studies. There are multiple moving parts that researchers may have to juggle, including more than one referent, multiple stakeholder groups providing data, multiple levels of analysis, and varying rates of observable change over the course of the implementation process. This paper advances the 2011 implementation outcomes taxonomy and research agenda in two ways. First, we bring the 2011 research agenda to life with concrete examples drawn from a wide swath of existing literature. For example, the 2011 research agenda drew our attention to the importance of the consistency of implementation outcomes terminology [ 3 ]. In this paper, we illustrate three specific ways that this can show up in the literature if not appropriately addressed. This makes it easier for implementation researchers to both identify and avoid these issues. Our examples also reflect how the field has changed over the last 10 years. For example, the 2011 research agenda drew our attention to the importance of specifying the referent for rating the outcome [ 3 ]. With the growth of implementation strategy research and hybrid designs in recent years, our examples allowed us to show how messy—and how important—clear referent specification can be in implementation outcomes research. The second way that we advance implementation outcomes research is by offering solutions. We provide six pragmatic recommendations for improved reporting. These are accompanied by an easy-to-use audit worksheet and a list of exemplar articles that researchers, funders, and reviewers can refer to when designing, conducting, and assessing implementation outcomes studies.

Availability of data and materials

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Acknowledgements

This work was supported by a grant from the National Institute of Mental Health (T32MH019960; Lengnick-Hall). Additionally, Dr. Lengnick-Hall is a fellow and Dr. Proctor is core faculty with the Implementation Research Institute (IRI, at the George Warren Brown School of Social Work, Washington University in St. Louis; through an award from the National Institute of Mental Health (R25 MH080916–08).

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Rebecca Lengnick-Hall & Julia C. Swanson

Graduate School of Social Work, University of Denver, Denver, CO, USA

Donald R. Gerke

Shanti Khinduka Distinguished Professor Emerita, The Brown School, Washington University in St. Louis, St. Louis, USA

Enola K. Proctor

College of Social Work, The Ohio State University, Columbus, OH, USA

Alicia C. Bunger & Rebecca J. Phillips

College of Education & Human Ecology, The Ohio State University, Columbus, OH, USA

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Contributions

RLH led manuscript development and contributed substantially to each manuscript section. DRG contributed to the identification of reporting problems and manuscript writing and led the identification of exemplar articles for each recommendation. EKP and ACB assisted with the identification of reporting problems, manuscript editing, and discussion of connections to the 2011 paper. RJP contributed to recommendation development and related manuscript sections. JKM contributed to data extraction and manuscript editing. JCS assisted with data collection and cleaning and editing the manuscript. All authors reviewed several iterations of the manuscript and approved the final version.

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Correspondence to Rebecca Lengnick-Hall .

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Supplementary Information

Additional file 1: table 1..

Audit worksheet organized by manuscript or grant proposal section.

Additional file 2: Table 2.

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Lengnick-Hall, R., Gerke, D.R., Proctor, E.K. et al. Six practical recommendations for improved implementation outcomes reporting. Implementation Sci 17 , 16 (2022). https://doi.org/10.1186/s13012-021-01183-3

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Violence against women and girls: new research makes recommendations for interventions with young people

23 July 2024

A series of recommendations on how to work with young people to reduce and raise awareness of violence against women and girls in education has been published by researchers at Solent University, Southampton, the University of Roehampton and the University of York.

Following the evaluation of Peer Heroes, a pilot scheme delivered by Yellow Door - who developed a peer led project with boys and delivered workshops in a Southampton school - a report makes eight recommendations. Nicci King, Chief Executive Officer at Yellow Door, says:

"Supporting children to explore and challenge attitudes is key to ensuring cultural change in our society so we can further prevent violence against women and girls. We are proud that for over 20 years, our STAR Project has been working with children in schools and other settings to explore healthy relationships.

"Peer Heroes builds on this, as it supports children to understand and tackle negative male stereotypes and attitudes that can contribute to violence against women and girls. The recommendations from Solent University's research will support us to develop this work even further locally and have an influence nationally."

The report and its recommendations put forward new insights into violence against women and girls, as well as how to develop a comprehensive offering for working with young people in this context, including:

• When delivering a project in this area, the relationships between misogynistic attitudes, violence against women and girls, and sexual harm needs to be considered clearly.
• It's essential that boys reflect on their own, as well as their peers', attitudes and behaviours when they are participants in projects of this kind.
• Interventions with boys need to thoughtfully consider the voices and experiences of girls, featuring them prominently throughout a project.

Dr Catherine Phipps, Solent's Senior Lecturer Education and Sociology, says: "It's been a pleasure to work on the evaluation of this important project alongside Yellow Door over the past 18 months. The final report demonstrates the value of universities collaborating with charities, schools, and other key stakeholders, and we are keen to continue this impactful work moving forward."

The report has been presented to local stakeholders in Southampton - including the local authority, colleges, universities and charities - and will be available for use by education providers to inform their approach to education and awareness-raising of violence against women and girls.

Professor Philippa Velija, Deputy Dean Research and PG Student Experience at the University of Roehampton, says:

"The evaluation report highlights the ongoing challenges that young people face in addressing sexual harm in their peer groups, as well as the value of working with young people to help them understand and tackle violence against women and girls. The evaluation also highlights the value of academics partnering with organisations and evaluating projects to consider what works and what can be enhanced in future projects."

Any school or organisation interested in finding out more about the pilot scheme and subsequent report can email [email protected] . 

Full list of recommendations

• Students in the survey and focus groups reported that they were more confident intervening in sexual harassment outside of school, but less confident amongst peers. This is something that future projects should focus on to have impact in schools.
• The design of the intervention and workshops could be structured on key themes, for example gender norms, misogyny, and sexual harm against women and girls. However, there should be flexibility to focus on some of the problematic experiences in the school at the time of the intervention.
• The relationship between misogynistic attitudes, violence against women and girls and sexual harm needs to be considered more clearly in the project.
• There is sensitivity around selecting boys to be part of this intervention. In future, schools and project leaders should look at how best to manage this when they are considering the expansion of the project.
• The intervention may benefit from more clarity around whether it is focusing on violence against women and girls and/or gender-based violence. Research consistently finds men and boys are the main perpetrators of sexual harassment and sexual violence (even against other men/boys). Boys cannot be victims of violence against women and girls, but they can be victims of gender-based violence. At times, the term violence against women and girls was used when gender-based violence was meant.
• With any intervention, the scope and type of output the boys will produce to share in the wider school needs to be considered.
• Although the intervention is about boys’ understanding and education on violence against women and girls, it is about girls’ experiences and this needs to be more thoughtfully considered to ensure their voices and experiences feature more prominently through the project.
• While statistics on violence against women and girls can have an impact and are helpful to contextualise the extent of the issue, it is important that this is seen as existing on a continuum and a consequence of wider gender inequality. Therefore, boys will need to reflect on their own and their peers’ attitudes and behaviours as part of the project.

If you have been affected by the issues covered in this article or the report please visit Yellow Door . 

If you would like to hear more about the Peer Heroes Project or are a school wanting support please contact [email protected]

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BACKGROUND AND PURPOSE: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field.

MATERIALS AND METHODS: We performed a bibliometric analysis of the American Journal of Neuroradiology ; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: “machine learning,” “artificial intelligence,” “radiomics,” “deep learning,” “neural network,” “generative adversarial network,” “object detection,” or “natural language processing.” Articles were screened by 2 independent reviewers, and categorized into statistical modeling (type 1), AI/ML development (type 2), both representing developmental research work but without a direct clinical integration, or end-user application (type 3), which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to type 3 articles being published, we analyzed type 2 articles as they should represent the precursor work leading to type 3.

RESULTS: A total of 182 articles were identified with 79% being nonintegration focused (type 1 n = 53, type 2 n = 90) and 21% ( n = 39) being type 3. The total number of articles published grew roughly 5-fold in the last 5 years, with the nonintegration focused articles mainly driving this growth. Additionally, a minority of type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most (60%) having additional postgraduate degrees.

CONCLUSIONS: AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift toward integrating practical AI/ML solutions in neuroradiology.

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• Published: 09 September 2024

Trends of Toxoplasma gondii and common transfusable venereal infections among blood donors in Menoufia Province, Egypt

• Marwa A. Gouda   ORCID: orcid.org/0000-0003-1723-6792 1 ,
• Sara A. Saied 2 ,
• Walaa Mohamed Omar Ashry 3 ,
• Raafat Abd-Rabow Abd-Eltwab 4 ,
• Mohamed Morshdy Aldesoky 4 ,
• Omnia Ahmed El-dydamoni 5 ,
• Marwa Yousef 6 &
• Mona M. El-Derbawy 7  

Scientific Reports volume  14 , Article number:  20920 ( 2024 ) Cite this article

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• Epidemiology
• Microbiology

Blood transfusion has a hazard of transmission of many pathogens, including Toxoplasma gondii ( T. gondii ) and other venereal infections. It is crucial to conduct epidemiological surveillance to detect the prevalence of these pathogens. The study aimed to assess the seroprevalence of T. gondii and common transfusable venereal infections among healthy blood donors in Menoufia Province, Egypt, and identify associated risk factors. Four hundred twenty individuals were recruited between January and April 2023 for cross-sectional descriptive research from the blood banks of Menoufia University medical hospitals. Collected blood samples were screened for anti- T. gondii IgM and IgG, HBsAg, anti-HCV antibodies, HIV p24 antigen and anti-HIV antibodies, and anti- Treponema pallidum antibodies. 46 (11.0%) and 22 donors (5.2%) individuals tested positive for anti- T. gondii IgG with a 95% CI (8.3–14.6) and IgM with a 95% CI (3.5–8.1), respectively, while one patient (0.2%) was positive for both antibodies. Regarding venereal infections, 12 (2.9%) were positive for HBV, 6 (1.4%) were positive for HCV, 7 (1.7%) were positive for HIV, and none of the tested population showed positivity for syphilis. Female gender, consumption of raw meat, agricultural environment, poor awareness about T. gondii , and blood group type (especially AB and O groups) were identified as independent risk factors for T. gondii infection. The study highlights the importance of testing blood donors for T. gondii and common transfusable venereal illnesses. Starting health education programs and preventative measures, such as suitable meat handling and cleanliness practices, is critical for minimizing the occurrence of these illnesses. Larger-scale additional study is advised to confirm these results and provide guidance for public health initiatives.

Introduction

Blood transfusion is a critical medical procedure vital for patients’ treatment. Every year, millions of people are exposed to avoidable life-threatening risks as a result of hazardous blood transfusions. The major transfusion-transmitted infections are Hepatitis B virus (HBV), Hepatitis C virus (HCV), human immunodeficiency virus (HIV), and syphilis, which pose significant threats to recipient safety 1 .

Toxoplasma gondii is a food-borne zoonotic protozoan parasite capable of infecting all homoeothermic vertebrates; however, felids, which are members of the Felidae family, serve as the definitive hosts for ( T. gondii ) infection, as both the sexual (intestinal) and asexual (tissue) cycles occur simultaneously in these animals (cats), resulting in un-sporulated non-infectious oocyst elimination and excretion 2 .

Oocysts may shed in vast numbers, even though they typically shed within 1–3 weeks. Oocysts sporulate in the environment in one to five days and spread infection. Warmer settings can facilitate sporulation more quickly, which increases the rate at which oocysts are found in the environment 3 . Temperature, humidity, and precipitation patterns all influence the survival and dissemination of T. gondii oocysts in the environment 4 . Warmer temperatures and greater rainfall can help oocysts survive and spread, potentially boosting infection rates in both animal and human populations 5 .

The infection with T. gondii usually appears as mild manifestations observed on exposure in immunocompetent people, such as warmth, tiredness, and cervical lymphadenopathy, which are self-limited; however, pneumonitis and encephalitis are complications of the infection, which is severe in immunocompromised people (such as AIDS patients) and blood recipients (such as those with thalassemia, haemophilia, dialysis patients, organ transplant recipients, and neonatal jaundice) 6 , 7 .

Co-infections can increase the severity of some infectious disorders. It has the potential to affect immune responses, and disease severity, and increase inflammatory cytokines 8 . Since T. gondii is considered one of the most successful parasites on the planet, the T. gondii disease burden has been classified as one of the most significant parasitic disorders. In order to reduce the occurrence of T. gondii infection among humans, it is urgent to understand the current status of this pathogen. Our study aimed to estimate the current situation of T. gondii and other transfusable venereal infections among blood donors in Menoufia Province, reflecting previously unknown regional outlines. Also, the study evaluated possible risk factors linked to T. gondii exposure in the population. Finally, the study intended to propose community-wide methods to raise awareness and prevent T. gondii infection.

Subjects and methods

Ethical approval and consent to participate.

This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the National Liver Disease Institute’s research ethics committee (NLI IRB procedure N. 00,422/2022). All subjects have given informed consent after being informed about the study’s objectives, the importance of participation as part of the community, and any potential adverse side effects of puncture. All subjects gave informed consent after being informed about the study’s objectives, the importance of participation as part of the community, and any potential negative side effects of puncture.

Study design

This cross-sectional descriptive study involved 420 blood donors’ serum samples. Samples were gathered randomly from blood donor volunteers in Menoufia University hospitals’ blood banks between January and April 2023. The inclusion criteria included individuals aged 18 and above who volunteered to participate by giving blood and providing informed permission. Individuals with a history of chronic diseases, recent infections, or who refused to participate were excluded from the study. Menoufia Province is a governorate in northern Egypt near the Nile Delta. Its surface area is about 2,543.03 km 2 , with 4,366,000 people in total, as reported in 2018, and its longitude and latitude are 30.52° N and 30.99° E. The governorate is considered one of Egypt’s regions with the highest population densities and is an important center for liver transplantation at the National Liver Institute.

Sample size estimation

The present sample size was calculated according to Yılmaz et al. (2021) 9 , who revealed 2.3% T. gondii IgM seropositivity at alpha error 0.05 and the power of the study 90%; the estimated sample size was 396 participants. Under the following formula,

e2,where n = sample size, z = standard error with the chosen level of confidence (1.96), p  = proportion detected in the reference study, q = 1 −  p , and e = acceptable sample error (0.05).

Questionnaire

A predesigned questionnaire was taken from each participant. It included:

Socio-demographic data.

Awareness about T. gondii infection: was assessed through a series of questions assessing the fundamental understanding of the disease, the transmission routes, hosts, the role of raw meat consumption in transmission, agricultural-related activities and other suggested risk factors, and possible complications of T. gondii infection, particularly for pregnant women and persons with weakened immune systems. Through 15 questions that were scored as (2, for correct answer; 1, for incomplete answer; and 0, for wrong answer, with a total score of 30; the good awareness level was at a score of 15 or above while the score less than 15 was considered as poor awareness.

Risk factors associated with T. gondii infection: including dealing with cats, agricultural environment-related activity, eating or dealing with raw meat as well as hand washing before eating, it also included other data, including blood group type, and previous blood transfusion.

Blood Sampling

Each person donated three mL of venous blood, centrifuged for five minutes at 3000 rpm to extract the serum and kept at − 20 °C for further laboratory analysis.

Enzyme-linked immunosorbent assay (ELISA)

Serum samples were transferred to the Parasitology Laboratory, Department of Clinical and Molecular Parasitology, National Liver Institute, Menoufia University, Egypt, to detect T. gondii -specific IgM and IgG antibodies. All were analyzed using an ELISA kit that is available commercially (Cat No. SL2055Hu_1 and SL2054Hu-1, SunLong Biotec). The manufacturer’s guidelines were fulfilled for running the analysis. Based on ELISA kits, positive samples were considered at titers above 1 and 3 IU for IgM and IgG, respectively. Negative samples were defined at values below 0.8 and 1 IU for IgM and IgG, respectively. Between the two ranges, a grey zone is reported. The optical density (OD) was measured under a 450 nm wave.

Venereal infection screening

All samples were tested for HBV surface antigen (HBsAg), anti-HCV antibodies, HIV p24 antigen, anti-HIV antibodies, and anti- T. pallidum antibodies. The venereal infection screening was conducted using an immunoassay Cobas e 601 immunoassay analyzer (Roche Diagnostics, Germany), which employs electrochemiluminescence (ELC) technology. The tests used were Elecsys HBSAGII (Cat No. 07251076190), Elecsys AHCVII (Cat No. 06427405190), Elecsys HIV Duo test (Cat No. 07229542190), and Elecsys Syphilis (Cat No. 07251378190), all provided by COBAS (Roche Diagnostics) and performed according to the manufacturer’s instructions.

Statistical analysis

Categorical and quantitative data were analyzed using SPSS (Statistical Package Software for Social Science) version 20.0 (SPSS Inc., Chicago, IL, USA). The prevalence of T. gondii antibodies and positivity to other transfusable venereal infections were assessed through frequency, percentage, and a 95% confidence interval (CI). Comparing positive and negative T. gondii antibody groups regarding qualitative variables by chi-squared test and quantitative normally distributed data was tested by student’s t-test. The study employed multivariate binary logistic regression analysis to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) to determine independent risk factors for T. gondii infection. A p-value of less than 0.05 determined a statistically significant result.

Prevalence of transfusable venereal infections

Regarding the prevalence of transfusable venereal infections, initial screenings (for HBV, HCV, HIV, and syphilis) detected 12 cases (2.9%) positive for HBsAg, six positive cases for anti-HCV antibodies (1.4%), seven cases positive for HIV p24 antigen and anti-HIV antibodies (1.7%), and nonpositive for syphilis (Fig.  1 ,B).

( A ): Prevalence of T. gondii among blood donors ( B ): Prevalence of transfusable venereal diseases.

Seropositivity of T. gondii infection

The ELISA test screened 420 blood samples for T. gondii- specific IgG and IgM antibodies. Out of them, 69 (16.4%) blood donors had anti- T. gondii antibodies in their sera (IgG, IgM, or both) (Fig.  1 , A). Forty-six cases (11%) were IgG-only seropositive, 22 cases (5.2%) were IgM-positive, and one case was positive for both IgG and IgM (0.2%) (Table 1 ).

Demographic characteristics of the studied population

Among the healthy blood donors enlisted in this research, the respondents’ average age was 32.39 ± 10.51 years (with a range of 17–66 years). Ages 21–40 comprised the largest age cohort of blood donors (68.1%). The vast bulk of the subjects (97.1%) were men. Sixty-six-point two percent (66.2%) of the volunteers were highly educated (Table 2 ).

Significant risk factors

Sixty-three (15.4%) of male-positive cases and six (50%) of female-positive cases indicated that the female sex was a major risk factor. Also, dealing with cats, eating rand, dealing with row meat, the agricultural environment, poor awareness about T. gondii infection, and blood groups were significant risk factors for T. gondii infection. Age, residence, educational level, and the presence of other transfusable venereal infections weren’t associated with the T. gondii infection (Table 3 ). There was no significant association between seropositivity for T. gondii and venereal infections (Table 4 ).

Multivariate analysis of independent risk factors associated with T. gondii infection

Multivariate regression analysis revealed that female gender, consumption of raw meat, agriculture environment and poor T. gondii infection awareness were independent risk factors for T. gondii infection with an odds ratio (95% CI): 3.1 (1.81–9.45), 32.62 (13.14–81.0), 4.57 (2.01–10.41), 12.66 (4.53–35.42) for the female gender, consumption of raw meat, agriculture environment, lack of T. gondii infection awareness respectively while for ABO grouping with taking B group as a reference, AB and O groups were independent risk groups with odds ratio (95% CI): 3.26 (1.92–7.84) & 4.58 (2.11–11.47) respectively (Table 5 ).

Understanding the prevalence of T. gondii and venereal infectious pathogens and risk factors among blood donors in Menoufia Province is crucial for public health strategies. This research is an epidemiologic report on seropositivity to T. gondii infection among healthy blood donors in Menoufia blood banks, Egypt. Menoufia Governorate had a low prevalence compared to most worldwide studies. In this research, the authors reported a total seroprevalence of 16.4% (95% CI 13–20.3); IgM-positive cases represented 5.5%, posing a risk of transmitting the infection to blood recipients. By integrating molecular approaches, supplementary serological markers, and direct proof of parasitemia, the hypothesis can be substantially reinforced, leading to a more thorough evaluation of the risk of T. gondii infection by blood transfusion, which is undertaken currently in epidemiological national research funded by STDF aiming to complete the current research.

Globally, according to estimates by Foroutan-Rad et al. 10 , T. gondii infection affects 33% of blood donors worldwide, with rates highest in Africa (46%) and lowest in Asia (29%) 10 . The prevalence rate varies by nation: 6.26% in China 11 , 9.3% in Taiwan 12 , 19.66% in India 13 , 20.5% in Serbia 14 , 25.6% in Turkey 9 , 36% in Portugal 15 , 48.1% in Brazil 16 , and 67.92% in Côte d’Ivoire 17 .

In other African countries, the seroprevalence among tested blood donors was 44.4% in South-West and Central-East Tunisia 18 and 47.7% in Sidi Bel Abbès, West Algeria 19 . The difference in serological methods used across studies is probably the main factor in the difference in reported prevalence of T. gondii infection among different nations.

Compared with previous findings from other Egyptian governorates, the current seroprevalence rates are consistent with those from El-Wadi El Gadded, which had the lowest incidence between 1 and 25% 20 . Earlier studies reported a prevalence between 33.7 and 67.4% of healthy Egyptian blood donors had antibodies to T. gondii infection, comparable to a range of 3–42.5% in the general Egyptian population. Increased seropositivity was seen. in the Lower Egypt bordering governorates of Sharqia and Qalyoubia (38.8% and 27.5% respectively), as well as in the rural Upper Egypt governorate of Beni-Suef (35.2%) 21 . Cairo also had high infection rates (between 30 and 42.5%) 21 . The studied group’s higher level of illness knowledge is probably the reason for the reduced infection prevalence when compared to estimates from throughout the world. These differences point to possible socioeconomic and geographic variables affecting T. gondii exposure in Egypt.

Multivariate regression analysis displayed that contact with cats, consuming raw or undercooked meat, and having agricultural pursuits are significant risk factors for T. gondii seropositivity, demonstrating that both infection routes—ingesting oocysts (soil contamination, contaminated water, and contaminated raw food e.g. salads, vegetables) and tissue cysts found in undercooked meat (a foodborne transmission)—showed up among the blood donors with different educational levels. These findings are supported by earlier studies 9 , 22 . From their results, domestic cats may be related to the exposure of the individuals included in the study to T. gondii . However, it is worth noting that direct contact with cats does not guarantee transmission of the parasite since T. gondii oocysts are eliminated as non-infective. In contrast to the present findings, El-Deeb and their alleles 23 found no statistically significant association between seropositivity concerning contact with domestic cats and meat consumption in Menoufia, Egypt. However, contact with soil was a considerable risk factor, which could be explained by the prevalence of domestic and stray cats, both more susceptible to parasites 23 .

Likewise, in the research done by Mahmoudvand et al. 22 , the prevalence of T. gondii infection in the current study was significantly higher in female donors (95%CI 1.71–17.52) despite the limited number of female participants in our study compared to male donors. Mahmoudvand et al. 22 , attributed this disparity to the female daily exposure to more tissue cysts and oocysts. Handling raw meat and gardening are cultural practices and household activities that may expose women to greater levels of T. gondii . Therefore, validating these findings using a more extensive sample size is necessary. These results were not supported by Hosseini and his/ her colleagues 24 , who did not find gender a significant risk factor.

Seropositivity in this research was higher in rural areas (53.6%) than in urban areas (46.4%); however, the difference was insignificant. This finding contrasts with those reported by some authors 22 , 24 . They hypothesized in their research that the overabundance of cats, inadequate sanitation of the environment, and lax hygiene standards might cause this difference.

The ABO phenotype and RhD antigen were previously associated with pathogenic protozoa of the phylum Apicomplexa. The protective effect of type O blood against severe malaria has been observed, possibly explaining the high prevalence of type O in regions where Plasmodium falciparum is endemic 25 .

Our current research discovered that blood donors carrying the type O blood group had the highest incidence of T. gondii infection and were riskier, with a significant difference between T. gondii and ( P  < 0.001), which is equivalent to the findings reported previously in northern Egypt 26 but different from those reported in Iran, where they found blood group B carriers more susceptible to infection with T. gondii infection 24 . Following the findings of Hosseini et al. research, the level of disease between Rh-positive and negative samples was not different 24 . Despite the association our study found between the blood group and seropositivity, this does not prove that the two are causally related to the onset of illness. Our study’s findings should be seen as preliminary and need more investigation in follow-up studies.

Most positive cases ranged from 21 to 40 years; however, age was not a significant risk factor in our univariate analysis. In the same vein, research done in Ardabil Province, northwestern Iran, demonstrated that most positive cases were aged 31–40 with no significant difference 27 . Unlike the current finding, other authors found that age substantially contributes to infection. Their conclusion was attributed to the cumulative effect of being exposed to the parasite over time 14 .

This research showed a higher prevalence of HBsAg (2.9%), followed by HIV and HCV (1.7% and 1.4%, respectively). Syphilis cases were absent among the studied population. The higher percent of HBsAg compared to other screened transfusion-transmissible infections was consistent with similar reports from a study among blood donors in Bahir Dar, North West, Ethiopia, where HBV was prevalent in 2.8% of cases, followed by HIV and HCV 28 .

Co-infections can worsen the symptoms of some infectious disorders. It can modulate immune responses, exacerbate disease severity, and increase inflammatory cytokines. While this study did not find a substantial prevalence of co-infection between T. gondii and the viral agents tested (HBV, HIV, and HCV), other research suggests that these pathogens may interact. In Egypt, for example, T. gondii co-infection with HBV and HCV was reported 29 . Furthermore, HIV infection may impair the immune system, increasing the risk of reactivating latent T. gondii infection 8 , 11 . T. gondii co-infection with certain viruses must be addressed to prevent, detect, and cure infections. It needs further examination and research.

Conclusion and recommendations

This cross-sectional research investigated the seroprevalence of T. gondii and common transfusable venereal infections across healthy blood donors in Egypt’s central Menoufia blood banks. According to this study, the governorate of Menoufia had a low incidence of T. gondii infection among blood donors.

Therefore, testing for T. gondii infection is required in blood donors to prevent potentially fatal outcomes for blood receivers. Building programs for health education are also required as a suitable strategy for preventing diseases.

Value-added of this research

We addressed the seroprevalence of T. gondii in the studied population, which provides a step for further studies and implementation research on a larger scale to test preventive strategies in the future.

Data availability

This article encompasses all data that was generated or evaluated.The corresponding author will provide any additional inquiries.

Abbreviations

Complete blood picture

Hepatitis B virus

Treponema pallidum

Hepatitis C virus

Human immunodeficiency virus

Enzyme-linked immunosorbent assay

Immunoglobulin G

Immunoglobulin M

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Acknowledgements

This work was created at the National Liver Institute and Faculty of Medicine at Menoufia University in Shebin El Kom City, Egypt. We appreciate the blood donors who took part in our research.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). The research paper, writing, and publication were done without receiving any financial assistance.

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Marwa A. Gouda

Department of Clinical Pathology, National Liver Institute, Menoufia University, Menoufia, Egypt

Sara A. Saied

Department of Medical Microbiology and Immunology, Damietta Faculty of Medicine (Girls), Al-Azhar University, Damietta, Egypt

Walaa Mohamed Omar Ashry

Department of Medical Microbiology and Immunology, Damietta Faculty of Medicine, Al-Azhar University, Damietta, Egypt

Raafat Abd-Rabow Abd-Eltwab & Mohamed Morshdy Aldesoky

Department of Medical Microbiology and Immunology, Faculty of Medicine for Girls (Cairo), Al-Azhar University, Cairo, Egypt

Omnia Ahmed El-dydamoni

Department of Epidemiology and Preventive Medicine, High Institute of Public Health, Alexandria University, Alexandria, Egypt

Marwa Yousef

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All authors contributed to the design and conception of the research. M.A.G., W.M.O.A., R.A.A., M.M.A., O.A.E., M.Y., S.A.S., and M.M.E. gathered and analyzed the data. The first draft of the manuscript was written by M.A.G., and all other authors offered comments on previous drafts. All authors have reviewed and approved the final draft ready for publication and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Gouda, M.A., Saied, S.A., Ashry, W.M.O. et al. Trends of Toxoplasma gondii and common transfusable venereal infections among blood donors in Menoufia Province, Egypt. Sci Rep 14 , 20920 (2024). https://doi.org/10.1038/s41598-024-70740-9

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Writing an Effective & Supportive Recommendation Letter

Sarvenaz sarabipour.

1 Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States

Sarah J. Hainer

2 Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States

Emily Furlong

3 Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

Nafisa M. Jadavji

4 Department of Biomedical Sciences, Midwestern University, Glendale, United States

5 Department of Neuroscience, Carleton University, Ottawa, Canada

Charlotte M. de Winde

6 MRC Laboratory for Molecular Cell Biology, University College London, London, United Kingdom

7 Department of Molecular Cell Biology & Immunology, Amsterdam UMC Locatie VUmc, Amsterdam, The Netherlands

Natalia Bielczyk

8 Welcome Solutions, Nijmegen, the Netherlands

9 Stichting Solaris Onderzoek en Ontwikkeling, Nijmegen, the Netherlands

Aparna P. Shah

10 The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland, United States

Author Contributions

Writing recommendation letters on behalf of students and other early-career researchers is an important mentoring task within academia. An effective recommendation letter describes key candidate qualities such as academic achievements, extracurricular activities, outstanding personality traits, participation in and dedication to a particular discipline, and the mentor’s confidence in the candidate’s abilities. In this Words of Advice, we provide guidance to researchers on composing constructive and supportive recommendation letters, including tips for structuring and providing specific and effective examples, while maintaining a balance in language and avoiding potential biases.

Introduction

A letter of recommendation or a reference letter is a statement of support for a student or an early-career researcher (ECR; a non-tenured scientist who may be a research trainee, postdoctoral fellow, laboratory technician, or junior faculty colleague) who is a candidate for future employment, promotion, education, or funding opportunities. Letters of recommendation are commonly requested at different stages of an academic research career and sometimes for transitioning to a non-academic career. Candidates need to request letters early on and prepare relevant information for the individual who is approached for recommendation [ 1 , 2 ]. Writing recommendation letters in support of ECRs for career development opportunities is an important task undertaken frequently by academics. ECRs can also serve as mentors during their training period and may be asked to write letters for their mentees. This offers the ECRs an excellent opportunity to gain experience in drafting these important documents, but may present a particular challenge for individuals with little experience. In general, a letter of recommendation should present a well-documented evaluation and provide sufficient evidence and information about an individual to assist a person or a selection committee in making their decision on an application [ 1 ]. Specifically, the letter should address the purpose for which it is written (which is generally to provide support of the candidate’s application and recommendation for the opportunity) and describe key candidate qualities, the significance of the work performed, the candidate’s other accomplishments and the mentor’s confidence in the candidate’s abilities. It should be written in clear and unbiased language. While a poorly written letter may not result in loss of the opportunity for the candidate, a well-written one can help an application stand out from the others, thus well-enhancing the candidate’s chances for the opportunity.

Letter readers at review, funding, admissions, hiring and promotion committees need to examine the letter objectively with a keenness for information on the quality of the candidate’s work and perspective on their scientific character [ 6 ]. However well-intentioned, letters can fall short of providing a positive, effective, and supportive document [ 1 , 3 – 5 ]. To prevent this, it is important to make every letter personal; thus, writing letters requires time and careful consideration. This article draws from our collective experiences as ECRs and the literature to highlight best practices and key elements for those asked to provide recommendation letters for their colleagues, students, or researchers who have studied or trained in their classroom or research laboratory. We hope that these guidelines will be helpful for letter writers to provide an overall picture of the candidate’s capabilities, potential and professional promise.

Decide on whether to write the letter

Before you start, it is important to evaluate your relationship with the candidate and ability to assess their skills and abilities honestly. Consider how well and in what context you know the person, as well as whether you can be supportive of their application [ 7 ]. Examine the description of the opportunity for which the letter is being requested ( Figure 1 ). Often you will receive a request by a student or a researcher whom you know very well and have interacted with in different settings – in and out of the classroom, your laboratory or that of a colleague, or within your department – and whose performance you find to be consistently satisfactory or excellent. Sometimes a mentee may request a recommendation letter when still employed or working with you, their research advisor. This can come as an unpleasant surprise if you are unaware that the trainee was seeking other opportunities (for instance, if they haven’t been employed with you for long, or have just embarked on a new project). While the mentee should be transparent about their goals and searching for opportunities, you should as a mentor offer to provide the letter for your mentee (see Table 1 ).

First, it is important to establish whether you are equipped to write a strong letter of support. If not, it is best to have a candid conversation with the applicant and discuss alternative options or opportunities. If you are in a position to write a strong letter of support, first acquire information regarding the application and the candidate, draft a letter in advance (see Box 1 ) and submit the letter on time. When drafting the letter, incorporate specific examples, avoid biases, and discuss the letter with the candidate (see Tables 1 – 2 for specific examples). After submission, store a digital copy for potential future use for the same candidate.

Key do’s and don’ts when being asked to write a letter of recommendation

Other requests may be made by a candidate who has made no impression on you, or only a negative one. In this case, consider the candidate’s potential and future goals, and be fair in your evaluation. Sending a negative letter or a generic positive letter for individuals you barely know is not helpful to the selection committee and can backfire for the candidate. It can also, in some instances, backfire for you if a colleague accepts a candidate based on your generic positive letter when you did not necessarily fully support that individual. For instance, letter writers sometimes stretch the truth to make a candidate sound better than they really are, thinking it is helpful. If you do not know the applicant well enough or feel that you cannot be supportive, you are not in a strong position to write the recommendation letter and should decline the request, being open about why you are declining to write the letter. Also, be selective about writing on behalf of colleagues who may be in one’s field but whose work is not well known to you. If you have to read the candidate’s curriculum vitae to find out who they are and what they have done, then you may not be qualified to write the letter [ 8 ].

When declining a request to provide a letter of support, it is important to explain your reasoning to the candidate and suggest how they might improve their prospects for the future [ 8 ]. If the candidate is having a similar problem with other mentors, try to help them identify a more appropriate referee or to explore whether they are making an appropriate application in the first place. Suggest constructive steps to improve relationships with mentors to identify individuals to provide letters in the future. Most importantly, do not let the candidate assume that all opportunities for obtaining supportive letters of recommendation have been permanently lost. Emphasize the candidate’s strengths by asking them to share a favourite paper, assignment, project, or other positive experience that may have taken place outside of your class or lab, to help you identify their strengths. Finally, discuss with the candidate their career goals to help them realize what they need to focus on to become more competitive or steer them in a different career direction. This conversation can mark an important step and become a great interaction and mentoring opportunity for ECRs.

Examine the application requirements

Once you decide to write a recommendation letter, it is important to know what type and level of opportunity the candidate is applying for, as this will determine what should be discussed in the letter ( Figure 1 ). You should carefully read the opportunity posting description and/or ask the candidate to summarize the main requirements and let you know the specific points that they find important to highlight. Pay close attention to the language of the position announcement to fully address the requested information and tailor the letter to the specific needs of the institution, employer, or funding organisation. In some instances, a waiver form or an option indicating whether or not the candidate waives their right to see the recommendation document is provided. If the candidate queries a waiver decision, note that often referees are not allowed to send a letter that is not confidential and that there may be important benefits to maintaining the confidentiality of letters (see Table 1 ). Specifically, selection committees may view confidential letters as having greater credibility and, value and some letter writers may feel less reserved in their praise of candidates in confidential letters.

Acquire candidate information and discuss letter content

To acquire appropriate information about the candidate, one or more of the following documents may be valuable: a resume or curriculum vitae (CV), a publication or a manuscript, an assignment or exam written for your course, a copy of the application essay or personal statement, a transcript of academic records, a summary of current work, and specific recommendation forms or questionnaires (if provided) [ 9 ]. Alternatively, you may ask the candidate to complete a questionnaire asking for necessary information and supporting documents [ 10 ]. Examine the candidate’s CV and provide important context to the achievements listed therein. Tailor the letter for the opportunity using these documents as a guide, but do not repeat their contents as the candidate likely submits them separately. Even the most articulate of candidates may find it difficult to describe their qualities in writing [ 11 ]. Furthermore, a request may be made by a person who has made a good impression, but for whom you lack significant information to be able to write a strong letter. Thus, even if you know a candidate well, schedule a brief in-person, phone, or virtual meeting with them to 1) fill in gaps in your knowledge about them, 2) understand why they are applying for this particular opportunity, 3) help bring their past accomplishments into sharper focus, and 4) discuss their short- and long-term goals and how their current studies or research activities relate to the opportunity they are applying for and to these goals. Other key information to gather from the applicant includes the date on which the recommendation letter is due, as well as details on how to submit it.

For most applications (for both academic and non-academic opportunities), a letter of recommendation will need to cover both scholarly capabilities and achievements as well as a broader range of personal qualities and experiences beyond the classroom or the laboratory. This includes extracurricular experiences and traits such as creativity, tenacity, and collegiality. If necessary, discuss with the candidate what they would like to see additionally highlighted. As another example of matching a letter with its purpose, a letter for a fellowship application for a specific project should discuss the validity and feasibility of the project, as well as the candidate’s qualifications for fulfilling the project.

Draft the letter early and maintain a copy

Another factor that greatly facilitates letter writing is drafting one as soon as possible after you have taught or trained the candidate, while your impressions are still clear. You might consider encouraging the candidate to make their requests early [ 11 ]. These letters can be placed in the candidate’s portfolio and maintained in your own files for future reference. If you are writing a letter in response to a request, start drafting it well in advance and anticipate multiple rounds of revision before submission. Once you have been asked by a candidate to write a letter, that candidate may return frequently, over a number of years, for additional letters. Therefore, maintain a digital copy of the letter for your records and for potential future applications for the same candidate.

Structure your letter

In the opening, you should introduce yourself and the candidate, state your qualifications and explain how you became acquainted with the candidate, as well as the purpose of the letter, and a summary of your recommendation ( Table 2 ). To explain your relationship with the candidate you should fully describe the capacity in which you know them: the type of experience, the period during which you worked with the candidate, and any special assignments or responsibilities that the candidate performed under your guidance. For instance, the letter may start with: “This candidate completed their postdoctoral training under my supervision. I am pleased to be able to provide my strongest support in recommending them for this opportunity.” You may also consider ranking the candidate among similar level candidates within the opening section to give an immediate impression of your thoughts. Depending on the position, ranking the candidate may also be desired by selection committees, and may be requested within the letter. For instance, the recommendation form or instructions may ask you to rank the candidate in the top 1%, 5%, 10%, etc., of applicants. You could write "the student is in the top 5% of undergraduate students I have trained" Or “There are currently x graduate students in our department and I rank this candidate at the top 1%. Their experimental/computational skills are the best I have ever had in my own laboratory.”. Do not forget to include with whom or what group you are comparing the individual. If you have not yet trained many individuals in your own laboratory, include those that you trained previously as a researcher as reference. Having concentrated on the candidate’s individual or unique strengths, you might find it difficult to provide a ranking. This is less of an issue if a candidate is unambiguously among the top 10% that you have mentored but not all who come to you for a letter will fall within that small group. If you wish to offer a comparative perspective, you might more readily be able to do so in more specific areas such as whether the candidate is one of the most articulate, original, clear-thinking, motivated, or intellectually curious.

Key do’s and don’ts when writing a letter of recommendation

The body of the recommendation letter should provide specific information about the candidate and address any questions or requirements posed in the selection criteria (see sections above). Some applications may ask for comments on a candidate’s scholarly performance. Refer the reader to the candidate’s CV and/or transcript if necessary but don’t report grades, unless to make an exceptional point (such as they were the only student to earn a top grade in your class). The body of the recommendation letter will contain the majority of the information including specific examples, relevant candidate qualities, and your experiences with the candidate, and therefore the majority of this manuscript focuses on what to include in this section.

The closing paragraph of the letter should briefly 1) summarize your opinions about the candidate, 2) clearly state your recommendation and strong support of the candidate for the opportunity that they are seeking, and 3) offer the recipient of the letter the option to contact you if they need any further information. Make sure to provide your email address and phone number in case the recipient has additional questions. The overall tone of the letter can represent your confidence in the applicant. If opportunity criteria are detailed and the candidate meets these criteria completely, include this information. Do not focus on what you may perceive as a candidate’s negative qualities as such tone may do more harm than intended ( Table 2 ). Finally, be aware of the Forer’s effect, a cognitive error, in which a very general description, that fits almost everyone, is used to describe a person [ 20 ]. Such generalizations can be harmful, as they provide the candidate the impression that they received a valuable, positive letter, but for the committee, who receive hundreds of similar letters, this is non-informative and unhelpful to the application.

Describe relevant candidate qualities with specific examples and without overhyping

In discussing a candidate’s qualities and character, proceed in ways similar to those used for intellectual evaluation ( Box 1 ). Information to specifically highlight may include personal characteristics, such as integrity, resilience, poise, confidence, dependability, patience, creativity, enthusiasm, teaching capabilities, problem-solving abilities, ability to manage trainees and to work with colleagues, curriculum development skills, collaboration skills, experience in grant writing, ability to organize events and demonstrate abilities in project management, and ability to troubleshoot (see section “ Use ethical principles, positive and inclusive language within the letter ” below for tips on using inclusive terminology). The candidate may also have a specific area of knowledge, strengths and experiences worth highlighting such as strong communication skills, expertise in a particular scientific subfield, an undergraduate degree with a double major, relevant work or research experience, coaching, and/or other extracurricular activities. Consider whether the candidate has taught others in the lab, or shown particular motivation and commitment in their work. When writing letters for mentees who are applying for (non-)academic jobs or admission to academic institutions, do not merely emphasize their strengths, achievements and potential, but also try to 1) convey a sense of what makes them a potential fit for that position or funding opportunity, and 2) fill in the gaps. Gaps may include an insufficient description of the candidate’s strengths or research given restrictions on document length. Importantly, to identify these gaps, one must have carefully reviewed both the opportunity posting as well as the application materials (see Box 1 , Table 2 ).

Recommendations for Letter Writers

• Consider characteristics that excite & motivate this candidate.
• Include qualities that you remember most about the candidate.
• Detail their unusual competence, talent, mentorship, teaching or leadership abilities.
• Explain the candidate’s disappointments or failures & the way they reacted & overcame.
• Discuss if they demonstrated a willingness to take intellectual risks beyond the normal research & classroom experience.
• Ensure that you have knowledge of the institution that the candidate is applying for.
• Consider what makes you believe this particular opportunity is a good match for this candidate.
• Consider how they might fit into the institution’s community & grow from their experience.
• Describe their personality & social skills.
• Discuss how the candidate interacts with teachers & peers.
• Use ethical principles, positive & inclusive language within the letter.
• Do not list facts & details, every paper, or discovery of the candidate’s career.
• Only mention unusual family or community circumstances after consulting the candidate.
• A thoughtful letter from a respective colleague with a sense of perspective can be quite valuable.
• Each letter takes time & effort, take it seriously.

When writing letters to nominate colleagues for promotion or awards, place stronger emphasis on their achievements and contributions to a field, or on their track record of teaching, mentorship and service, to aid the judging panel. In addition to describing the candidate as they are right now, you can discuss the development the person has undergone (for specific examples see Table 2 ).

A letter of recommendation can also explain weaknesses or ambiguities in the candidate’s record. If appropriate – and only after consulting the candidate - you may wish to mention a family illness, financial hardship, or other factors that may have resulted in a setback or specific portion of the candidate’s application perceived weakness (such as in the candidate’s transcript). For example, sometimes there are acceptable circumstances for a gap in a candidate’s publication record—perhaps a medical condition or a family situation kept them out of the lab for a period of time. Importantly, being upfront about why there is a perceived gap or blemish in the application package can strengthen the application. Put a positive spin on the perceived negatives using terms such as “has taken steps to address gaps in knowledge”, “has worked hard to,” and “made great progress in” (see Table 2 ).

Describe a candidate’s intellectual capabilities in terms that reflect their distinctive or individual strengths and be prepared to support your judgment with field-specific content [ 12 ] and concrete examples. These can significantly strengthen a letter and will demonstrate a strong relationship between you and the candidate. Describe what the candidate’s strengths are, moments they have overcome adversity, what is important to them. For example: “candidate x is exceptionally intelligent. They proved to be a very quick study, learning the elements of research design and technique y in record time. Furthermore, their questions are always thoughtful and penetrating.”. Mention the candidate’s diligence, work ethic, and curiosity and do not merely state that “the applicant is strong” without specific examples. Describing improvements to candidate skills over time can help highlight their work ethic, resolve, and achievements over time. However, do not belabor a potential lower starting point.

Provide specific examples for when leadership was demonstrated, but do not include leadership qualities if they have not been demonstrated. For example, describe the candidate’s qualities such as independence, critical thinking, creativity, resilience, ability to design and interpret experiments; ability to identify the next steps and generate interesting questions or ideas, and what you were especially impressed by. Do not generically list the applicant as independent with no support or if this statement would be untrue.

Do not qualify candidate qualities based on a stereotype for specific identities. Quantify the candidate’s abilities, especially with respect to other scientists who have achieved success in the field and who the letter reader might know. Many letter writers rank applicants according to their own measure of what makes a good researcher, graduate trainee, or technician according to a combination of research strengths, leadership skills, writing ability, oral communication, teaching ability, and collegiality. Describe what the role of the candidate was in their project and eventual publication and do not assume letter readers will identify this information on their own (see Table 2 ). Including a description about roles and responsibilities can help to quantify a candidate’s contribution to the listed work. For example, “The candidate is the first author of the paper, designed, and led the project.”. Even the best mentor can overlook important points, especially since mentors typically have multiple mentees under their supervision. Thus, it can help to ask the candidate what they consider their strengths or traits, and accomplishments of which they are proud.

If you lack sufficient information to answer certain questions about the candidate, it is best to maintain the integrity and credibility of your letter - as the recommending person, you are potentially writing to a colleague and/or someone who will be impacted by your letter; therefore, honesty is key above all. Avoid the misconception that the more superlatives you use, the stronger the letter. Heavy use of generic phrases or clichés is unhelpful. Your letter can only be effective if it contains substantive information about the specific candidate and their qualifications for the opportunity. A recommendation that paints an unrealistic picture of a candidate may be discounted. All information in a letter of recommendation should be, to the best of your knowledge, accurate. Therefore, present the person truthfully but positively. Write strongly and specifically about someone who is truly excellent (explicitly describe how and why they are special). Write a balanced letter without overhyping the candidate as it will not help them.

Be careful about what you leave out of the letter

Beware of what you leave out of the recommendation letter. For most opportunities, there are expectations of what should be included in a letter, and therefore what is not said can be just as important as what is said. Importantly, do not assume all the same information is necessary for every opportunity. In general, you should include the information stated above, covering how you know the candidate, their strengths, specific examples to support your statements, and how the candidate fits well for the opportunity. For example, if you don’t mention a candidate’s leadership skills or their ability to work well with others, the letter reader may wonder why, if the opportunity requires these skills. Always remember that opportunities are sought by many individuals, so evaluators may look for any reason to disregard an application, such as a letter not following instructions or discussing the appropriate material. Also promote the candidate by discussing all of their scholarly and non-scholarly efforts, including non-peer reviewed research outputs such as preprints, academic and non-academic service, and advocacy work which are among their broader impact and all indicative of valuable leadership qualities for both academic and non-academic environments ( Table 2 ).

Provide an even-handed judgment of scholarly impact, be fair and describe accomplishments fairly by writing a balanced letter about the candidate’s attributes that is thoughtful and personal (see Table 2 ). Submitting a generic, hastily written recommendation letter is not helpful and can backfire for both the candidate and the letter writer as you will often leave out important information for the specific opportunity; thus, allow for sufficient time and effort on each candidate/application.

Making the letter memorable by adding content that the reader will remember, such as an unusual anecdote, or use of a unique term to describe the candidate. This will help the application stand out from all the others. Tailor the letter to the candidate, including as much unique, relevant information as possible and avoid including personal information unless the candidate gives consent. Provide meaningful examples of achievements and provide stories or anecdotes that illustrate the candidate’s strengths. Say what the candidate specifically did to give you that impression ( Box 1 ). Don’t merely praise the candidate using generalities such as “candidate x is a quick learner”.

Use ethical principles, positive and inclusive language within the letter

Gender affects scientific careers. Avoid providing information that is irrelevant to the opportunity, such as ethnicity, age, hobbies, or marital status. Write about professional attributes that pertain to the application. However, there are qualities that might be important to the job or funding opportunity. For instance, personal information may illustrate the ability to persevere and overcome adversity - qualities that are helpful in academia and other career paths. It is critical to pay attention to biases and choices of words while writing the letter [ 13 , 14 ]. Advocacy bias (a letter writer is more likely to write a strong letter for someone similar to themselves) has been identified as an issue in academic environments [ 3 ]. Studies have also shown that there are often differences in the choice of words used in letters for male and female scientists [ 3 , 5 ]. For instance, letters for women have been found not to contain much specific and descriptive language. Descriptions often pay greater attention to the personal lives or personal characteristics of women than men, focusing on items that have little relevance in a letter of recommendation. When writing recommendation letters, employers have a tendency to focus on scholarly capabilities in male candidates and personality features in female candidates; for instance, female candidates tend to be depicted in letters as teachers and trainees, whereas male candidates are described as researchers and professionals [ 15 ]. Also, letters towards males often contain more standout words such as “superb”, “outstanding”, and “excellent”. Furthermore, letters for women had been found to contain more doubt-raising statements, including negative or unexplained comments [ 3 , 15 , 16 ]. This is discriminative towards women and gives a less clear picture of women as professionals. Keep the letter gender neutral. Do not write statements such as “candidate x is a kind woman” or “candidate y is a fantastic female scientist” as these have no bearing on whether someone will do well in graduate school or in a job. One way to reduce gender bias is by checking your reference letter with a gender bias calculator [ 17 , 18 ]. Test for gender biases by writing a letter of recommendation for any candidate, male or female, and then switch all the pronouns to the opposite gender. Read the letter over and ask yourself if it sounds odd. If it does, you should probably change the terms used [ 17 ]. Other biases also exist, and so while gender bias has been the most heavily investigated, bias based on other identities (race, nationality, ethnicity, among others) should also be examined and assessed in advance and during letter writing to ensure accurate and appropriate recommendations for all.

Revise and submit on time

The recommendation letter should be written using language that is straightforward and concise [ 19 ]. Avoid using jargon or language that is too general or effusive ( Table 1 ). Formats and styles of single and co-signed letters are also important considerations. In some applications, the format is determined by the application portal itself in which the recommender is asked to answer a series of questions. If these questions do not cover everything you would like to address you could inquire if there is the option to provide a letter as well. Conversely, if the recommendation questionnaire asks for information that you cannot provide, it is best to explicitly mention this in writing. The care with which you write the letter will also influence the effectiveness of the letter - writing eloquently is another way of registering your support for the candidate. Letters longer than two pages can be counterproductive, and off-putting as reviewers normally have a large quantity of letters to read. In special cases, longer letters may be more favourable depending on the opportunity. On the other hand, anything shorter than a page may imply a lack of interest or knowledge, or a negative impression on the candidate. In letter format, write at least 3-4 paragraphs. It is important to note that letters from different sectors, such as academia versus industry tend to be of different lengths. Ensure that your letter is received by the requested method (mail or e-mail) and deadline, as a late submission could be detrimental for the candidate. Write and sign the letter on your department letterhead which is a further form of identification.

Conclusions

Recommendation letters can serve as important tools for assessing ECRs as potential candidates for a job, course, or funding opportunity. Candidates need to request letters in advance and provide relevant information for the recommender. Readers at selection committees need to examine the letter objectively with an eye for information on the quality of the candidate’s scholarly and non-scholarly endeavours and scientific traits. As a referee, it is important that you are positive, candid, yet helpful, as you work with the candidate in drafting a letter in their support. In writing a recommendation letter, summarize your thoughts on the candidate and emphasize your strong support for their candidacy. A successful letter communicates the writer’s enthusiasm for an individual, but does so realistically, sympathetically, and with concrete examples to support the writer’s associations. Writing recommendation letters can help mentors examine their interactions with their mentee and know them in different light. Express your willingness to help further by concluding the letter with an offer to be contacted should the reader need more information. Remember that a letter writer’s judgment and credibility are at stake thus do spend the time and effort to present yourself as a recommender in the best light and help ECRs in their career path.

Acknowledgements

S.J.H. was supported by the National Institutes of Health grant R35GM133732. A.P.S. was partially supported by the NARSAD Young Investigator Grant 27705.

Abbreviations:

Conflicts of Interest

The authors declare no conflicts of interest.