recommendation for future research example

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 formulate research recommendations

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• Peer review
• Polly Brown ( pbrown{at}bmjgroup.com ) , 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 ,
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• Julie Glanville , associate director/information service manager 5 ,
• Nicholas J Hicks , consultant in public health medicine 6 ,
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• 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 the population of interest?)

I Intervention (What are the interventions of interest?)

C Comparison (What are the comparisons of interest?)

O Outcome (What are the outcomes of interest?)

T Time stamp (Date of recommendation)

Optional elements

d Disease burden or relevance

t Time aspect of core elements of EPICOT

s Appropriate study type according to local need

In January 2006, the National Coordinating Centre for Health Technology Assessment presented the findings of an initial comparative analysis of how different organisations currently structure their research recommendations. The National Institute for Health and Clinical Excellence and the National Coordinating Centre for Health Technology Assessment request authors to present recommendations in a four component format for formulating well built clinical questions around treatments: population, intervention, comparison, and outcomes (PICO). 1 In addition, the research recommendation is dated and authors are asked to provide the current state of the evidence to support the proposal.

Clinical Evidence , although not directly standardising its sections for research recommendations, presents gaps in the evidence using a slightly extended version of the PICO format: evidence, population, intervention, comparison, outcomes, and time (EPICOT). Clinical Evidence has used this inherent structure to feed research recommendations on interventions categorised as “unknown effectiveness” back to the National Coordinating Centre for Health Technology Assessment and for inclusion in the Database of Uncertainties about the Effects of Treatments ( http://www.duets.nhs.uk/ ).

We decided to propose the EPICOT format as the basis for its statement on formulating research recommendations and tested this proposal through discussion and example. We agreed that this set of components provided enough context for formulating research recommendations without limiting researchers. In order for the proposed framework to be flexible and more widely applicable, the group discussed using several optional components when they seemed relevant or were proposed by one or more of the group members. The final outcome of discussions resulted in the proposed EPICOT+ format (box).

A recent BMJ article highlighted how lack of research hinders the applicability of existing guidelines to patients in primary care who have had a stroke or transient ischaemic attack. 2 Most research in the area had been conducted in younger patients with a recent episode and in a hospital setting. The authors concluded that “further evidence should be collected on the efficacy and adverse effects of intensive blood pressure lowering in representative populations before we implement this guidance [from national and international guidelines] in primary care.” Table 1 outlines how their recommendations could be formulated using the EPICOT+ format. The decision on whether additional research is indeed clinically and ethically warranted will still lie with the organisation considering commissioning the research.

Research recommendation based on gap in the evidence identified by a cross sectional study of clinical guidelines for management of patients who have had a stroke

• View inline

Table 2 shows the use of EPICOT+ for an unanswered question on the effectiveness of compliance therapy in people with schizophrenia, identified by the Database of Uncertainties about the Effects of Treatments.

Research recommendation based on a gap in the evidence on treatment of schizophrenia identified by the Database of Uncertainties about the Effects of Treatments

Discussions around optional elements

Although the group agreed that the PICO elements should be core requirements for a research recommendation, intense discussion centred on the inclusion of factors defining a more detailed context, such as current state of evidence (E), appropriate study type (s), disease burden and relevance (d), and timeliness (t).

Initially, group members interpreted E differently. Some viewed it as the supporting evidence for a research recommendation and others as the suggested study type for a research recommendation. After discussion, we agreed that E should be used to refer to the amount and quality of research supporting the recommendation. However, the issue remained contentious as some of us thought that if a systematic review was available, its reference would sufficiently identify the strength of the existing evidence. Others thought that adding evidence to the set of core elements was important as it provided a summary of the supporting evidence, particularly as the recommendation was likely to be abstracted and used separately from the review or research that led to its formulation. In contrast, the suggested study type (s) was left as an optional element.

A research recommendation will rarely have an absolute value in itself. Its relative priority will be influenced by the burden of ill health (d), which is itself dependent on factors such as local prevalence, disease severity, relevant risk factors, and the priorities of the organisation considering commissioning the research.

Similarly, the issue of time (t) could be seen to be relevant to each of the core elements in varying ways—for example, duration of treatment, length of follow-up. The group therefore agreed that time had a subsidiary role within each core item; however, T as the date of the recommendation served to define its shelf life and therefore retained individual importance.

Applicability and usability

The proposed statement on research recommendations applies to uncertainties of the effects of any form of health intervention or treatment and is intended for research in humans rather than basic scientific research. Further investigation is required to assess the applicability of the format for questions around diagnosis, signs and symptoms, prognosis, investigations, and patient preference.

When the proposed format is applied to a specific research recommendation, the emphasis placed on the relevant part(s) of the EPICOT+ format may vary by author, audience, and intended purpose. For example, a recommendation for research into treatments for transient ischaemic attack may or may not define valid outcome measures to assess quality of life or gather data on adverse effects. Among many other factors, its implementation will also depend on the strength of current findings—that is, strong evidence may support a tightly focused recommendation whereas a lack of evidence would result in a more general recommendation.

The controversy within the group, especially around the optional components, reflects the different perspectives of the participating organisations—whether they were involved in commissioning, undertaking, or summarising research. Further issues will arise during the implementation of the proposed format, and we welcome feedback and discussion.

Summary points

No common guidelines exist for the formulation of recommendations for research on the effects of treatments

Major organisations involved in commissioning or summarising research compared their approaches and agreed on core questions

The essential items can be summarised as EPICOT+ (evidence, population, intervention, comparison, outcome, and time)

Further details, such as disease burden and appropriate study type, should be considered as required

We thank Patricia Atkinson and Jeremy Wyatt.

Contributors and sources All authors contributed to manuscript preparation and approved the final draft. NJH is the guarantor.

Competing interests None declared.

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Report Statistics
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• GETTING STARTED
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FUTURE RESEARCH

Types of future research suggestion.

The Future Research section of your dissertation is often combined with the Research Limitations section of your final, Conclusions chapter. This is because your future research suggestions generally arise out of the research limitations you have identified in your own dissertation. In this article, we discuss six types of future research suggestion. These include: (1) building on a particular finding in your research; (2) addressing a flaw in your research; examining (or testing) a theory (framework or model) either (3) for the first time or (4) in a new context, location and/or culture; (5) re-evaluating and (6) expanding a theory (framework or model). The goal of the article is to help you think about the potential types of future research suggestion that you may want to include in your dissertation.

Before we discuss each of these types of future research suggestion, we should explain why we use the word examining and then put or testing in brackets. This is simply because the word examining may be considered more appropriate when students use a qualitative research design; whereas the word testing fits better with dissertations drawing on a quantitative research design. We also put the words framework or model in brackets after the word theory . We do this because a theory , framework and model are not the same things. In the sections that follow, we discuss six types of future research suggestion.

Addressing research limitations in your dissertation

Building on a particular finding or aspect of your research, examining a conceptual framework (or testing a theoretical model) for the first time, examining a conceptual framework (or testing a theoretical model) in a new context, location and/or culture.

• Expanding a conceptual framework (or testing a theoretical model)

Re-evaluating a conceptual framework (or theoretical model)

In the Research Limitations section of your Conclusions chapter, you will have inevitably detailed the potential flaws (i.e., research limitations) of your dissertation. These may include:

An inability to answer your research questions

Theoretical and conceptual problems

Limitations of your research strategy

Problems of research quality

Identifying what these research limitations were and proposing future research suggestions that address them is arguably the easiest and quickest ways to complete the Future Research section of your Conclusions chapter.

Often, the findings from your dissertation research will highlight a number of new avenues that could be explored in future studies. These can be grouped into two categories:

Your dissertation will inevitably lead to findings that you did not anticipate from the start. These are useful when making future research suggestions because they can lead to entirely new avenues to explore in future studies. If this was the case, it is worth (a) briefly describing what these unanticipated findings were and (b) suggesting a research strategy that could be used to explore such findings in future.

Sometimes, dissertations manage to address all aspects of the research questions that were set. However, this is seldom the case. Typically, there will be aspects of your research questions that could not be answered. This is not necessarily a flaw in your research strategy, but may simply reflect that fact that the findings did not provide all the answers you hoped for. If this was the case, it is worth (a) briefly describing what aspects of your research questions were not answered and (b) suggesting a research strategy that could be used to explore such aspects in future.

You may want to recommend that future research examines the conceptual framework (or tests the theoretical model) that you developed. This is based on the assumption that the primary goal of your dissertation was to set out a conceptual framework (or build a theoretical model). It is also based on the assumption that whilst such a conceptual framework (or theoretical model) was presented, your dissertation did not attempt to examine (or test) it in the field . The focus of your dissertations was most likely a review of the literature rather than something that involved you conducting primary research.

Whilst it is quite rare for dissertations at the undergraduate and master's level to be primarily theoretical in nature like this, it is not unknown. If this was the case, you should think about how the conceptual framework (or theoretical model) that you have presented could be best examined (or tested) in the field . In understanding the how , you should think about two factors in particular:

What is the context, location and/or culture that would best lend itself to my conceptual framework (or theoretical model) if it were to be examined (or tested) in the field?

What research strategy is most appropriate to examine my conceptual framework (or test my theoretical model)?

If the future research suggestion that you want to make is based on examining your conceptual framework (or testing your theoretical model) in the field , you need to suggest the best scenario for doing so.

More often than not, you will not only have set out a conceptual framework (or theoretical model), as described in the previous section, but you will also have examined (or tested) it in the field . When you do this, focus is typically placed on a specific context, location and/or culture.

If this is the case, the obvious future research suggestion that you could propose would be to examine your conceptual framework (or test the theoretical model) in a new context, location and/or culture. For example, perhaps you focused on consumers (rather than businesses), or Canada (rather than the United Kingdom), or a more individualistic culture like the United States (rather than a more collectivist culture like China).

When you propose a new context, location and/or culture as your future research suggestion, make sure you justify the choice that you make. For example, there may be little value in future studies looking at different cultures if culture is not an important component underlying your conceptual framework (or theoretical model). If you are not sure whether a new context, location or culture is more appropriate, or what new context, location or culture you should select, a review the literature will often help clarify where you focus should be.

Expanding a conceptual framework (or theoretical model)

Assuming that you have set out a conceptual framework (or theoretical model) and examined (or tested) it in the field , another series of future research suggestions comes out of expanding that conceptual framework (or theoretical model).

We talk about a series of future research suggestions because there are so many ways that you can expand on your conceptual framework (or theoretical model). For example, you can do this by:

Examining constructs (or variables) that were included in your conceptual framework (or theoretical model) but were not focused.

Looking at a particular relationship aspect of your conceptual framework (or theoretical model) further.

Adding new constructs (or variables) to the conceptual framework (or theoretical model) you set out (if justified by the literature).

It would be possible to include one or a number of these as future research suggestions. Again, make sure that any suggestions you make have are justified , either by your findings or the literature.

With the dissertation process at the undergraduate and master's level lasting between 3 and 9 months, a lot a can happen in between. For example, a specific event (e.g., 9/11, the economic crisis) or some new theory or evidence that undermines (or questions) the literature (theory) and assumptions underpinning your conceptual framework (or theoretical model). Clearly, there is little you can do about this. However, if this happens, reflecting on it and re-evaluating your conceptual framework (or theoretical model), as well as your findings, is an obvious source of future research suggestions.

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Writing a Research Paper Conclusion | Step-by-Step Guide

Published on October 30, 2022 by Jack Caulfield . Revised on April 13, 2023.

• Restate the problem statement addressed in the paper
• Summarize your overall arguments or findings
• Suggest the key takeaways from your paper

The content of the conclusion varies depending on whether your paper presents the results of original empirical research or constructs an argument through engagement with sources .

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Table of contents

Step 1: restate the problem, step 2: sum up the paper, step 3: discuss the implications, research paper conclusion examples, frequently asked questions about research paper conclusions.

The first task of your conclusion is to remind the reader of your research problem . You will have discussed this problem in depth throughout the body, but now the point is to zoom back out from the details to the bigger picture.

While you are restating a problem you’ve already introduced, you should avoid phrasing it identically to how it appeared in the introduction . Ideally, you’ll find a novel way to circle back to the problem from the more detailed ideas discussed in the body.

For example, an argumentative paper advocating new measures to reduce the environmental impact of agriculture might restate its problem as follows:

Meanwhile, an empirical paper studying the relationship of Instagram use with body image issues might present its problem like this:

“In conclusion …”

Avoid starting your conclusion with phrases like “In conclusion” or “To conclude,” as this can come across as too obvious and make your writing seem unsophisticated. The content and placement of your conclusion should make its function clear without the need for additional signposting.

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Having zoomed back in on the problem, it’s time to summarize how the body of the paper went about addressing it, and what conclusions this approach led to.

Depending on the nature of your research paper, this might mean restating your thesis and arguments, or summarizing your overall findings.

Argumentative paper: Restate your thesis and arguments

In an argumentative paper, you will have presented a thesis statement in your introduction, expressing the overall claim your paper argues for. In the conclusion, you should restate the thesis and show how it has been developed through the body of the paper.

Briefly summarize the key arguments made in the body, showing how each of them contributes to proving your thesis. You may also mention any counterarguments you addressed, emphasizing why your thesis holds up against them, particularly if your argument is a controversial one.

Don’t go into the details of your evidence or present new ideas; focus on outlining in broad strokes the argument you have made.

Empirical paper: Summarize your findings

In an empirical paper, this is the time to summarize your key findings. Don’t go into great detail here (you will have presented your in-depth results and discussion already), but do clearly express the answers to the research questions you investigated.

Describe your main findings, even if they weren’t necessarily the ones you expected or hoped for, and explain the overall conclusion they led you to.

Having summed up your key arguments or findings, the conclusion ends by considering the broader implications of your research. This means expressing the key takeaways, practical or theoretical, from your paper—often in the form of a call for action or suggestions for future research.

Argumentative paper: Strong closing statement

An argumentative paper generally ends with a strong closing statement. In the case of a practical argument, make a call for action: What actions do you think should be taken by the people or organizations concerned in response to your argument?

If your topic is more theoretical and unsuitable for a call for action, your closing statement should express the significance of your argument—for example, in proposing a new understanding of a topic or laying the groundwork for future research.

Empirical paper: Future research directions

In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research.

Whatever the scope of your own research, there will always be room for further investigation of related topics, and you’ll often discover new questions and problems during the research process .

Finish your paper on a forward-looking note by suggesting how you or other researchers might build on this topic in the future and address any limitations of the current paper.

Full examples of research paper conclusions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

While the role of cattle in climate change is by now common knowledge, countries like the Netherlands continually fail to confront this issue with the urgency it deserves. The evidence is clear: To create a truly futureproof agricultural sector, Dutch farmers must be incentivized to transition from livestock farming to sustainable vegetable farming. As well as dramatically lowering emissions, plant-based agriculture, if approached in the right way, can produce more food with less land, providing opportunities for nature regeneration areas that will themselves contribute to climate targets. Although this approach would have economic ramifications, from a long-term perspective, it would represent a significant step towards a more sustainable and resilient national economy. Transitioning to sustainable vegetable farming will make the Netherlands greener and healthier, setting an example for other European governments. Farmers, policymakers, and consumers must focus on the future, not just on their own short-term interests, and work to implement this transition now.

As social media becomes increasingly central to young people’s everyday lives, it is important to understand how different platforms affect their developing self-conception. By testing the effect of daily Instagram use among teenage girls, this study established that highly visual social media does indeed have a significant effect on body image concerns, with a strong correlation between the amount of time spent on the platform and participants’ self-reported dissatisfaction with their appearance. However, the strength of this effect was moderated by pre-test self-esteem ratings: Participants with higher self-esteem were less likely to experience an increase in body image concerns after using Instagram. This suggests that, while Instagram does impact body image, it is also important to consider the wider social and psychological context in which this usage occurs: Teenagers who are already predisposed to self-esteem issues may be at greater risk of experiencing negative effects. Future research into Instagram and other highly visual social media should focus on establishing a clearer picture of how self-esteem and related constructs influence young people’s experiences of these platforms. Furthermore, while this experiment measured Instagram usage in terms of time spent on the platform, observational studies are required to gain more insight into different patterns of usage—to investigate, for instance, whether active posting is associated with different effects than passive consumption of social media content.

If you’re unsure about the conclusion, it can be helpful to ask a friend or fellow student to read your conclusion and summarize the main takeaways.

• Do they understand from your conclusion what your research was about?
• Are they able to summarize the implications of your findings?
• Can they answer your research question based on your conclusion?

You can also get an expert to proofread and feedback your paper with a paper editing service .

The conclusion of a research paper has several key elements you should make sure to include:

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

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers 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 results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

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Suggestions for Future Research

Your dissertation needs to include suggestions for future research. Depending on requirements of your university, suggestions for future research can be either integrated into Research Limitations section or it can be a separate section.

You will need to propose 4-5 suggestions for future studies and these can include the following:

1. Building upon findings of your research . These may relate to findings of your study that you did not anticipate. Moreover, you may suggest future research to address unanswered aspects of your research problem.

2. Addressing limitations of your research . Your research will not be free from limitations and these may relate to formulation of research aim and objectives, application of data collection method, sample size, scope of discussions and analysis etc. You can propose future research suggestions that address the limitations of your study.

3. Constructing the same research in a new context, location and/or culture . It is most likely that you have addressed your research problem within the settings of specific context, location and/or culture. Accordingly, you can propose future studies that can address the same research problem in a different settings, context, location and/or culture.

4. Re-assessing and expanding theory, framework or model you have addressed in your research . Future studies can address the effects of specific event, emergence of a new theory or evidence and/or other recent phenomenon on your research problem.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

Recommendations for Research in the Future and Final Comments

• First Online: 04 September 2021

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The limitations of the studies examined in the previous chapter are presented in this chapter. A study’s limitations can highlight areas where more research can be conducted in the future. The focus of this chapter then shifts to providing several general recommendations for the responsible conduct of research to avoid the reproducibility crisis that is currently challenging the practice of research in psychology and medicine.

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Bennett, M., Goodall, E. (2021). Recommendations for Research in the Future and Final Comments. In: Employment of Persons with Autism. SpringerBriefs in Psychology. Springer, Cham. https://doi.org/10.1007/978-3-030-82174-6_4

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Leaders weigh in on where we are and what’s next

The explosion of generative AI technology over the past year and a half is raising big questions about how these tools will impact higher education. Across Harvard, members of the community have been exploring how GenAI will change the ways we teach, learn, research, and work.

As part of this effort, the Office of the Provost has convened three working groups . They will discuss questions, share innovations, and evolve guidance and community resources. They are:

• The Teaching and Learning Group , chaired by Bharat Anand , vice provost for advances in learning and the Henry R. Byers Professor of Business Administration at Harvard Business School. This group seeks to share resources, identify emerging best practices, guide policies, and support the development of tools to address common challenges among faculty and students.
• The Research and Scholarship Group , chaired by John Shaw , vice provost for research, Harry C. Dudley Professor of Structural and Economic Geology in the Earth and Planetary Sciences Department, and professor of environmental science and engineering in the Paulson School of Engineering and Applied Science. It focuses on how to enable, and support the integrity of, scholarly activities with generative AI tools.
• T he Administration and Operations Group , chaired by Klara Jelinkova , vice president and University chief information officer. It is charged with addressing information security, data privacy, procurement, and administration and organizational efficiencies.

Klara Jelinkova, Bharat Anand, and John Shaw.

Photos by Kris Snibbe/Harvard Staff Photographer; Evgenia Eliseeva; and courtesy of John Shaw

The Gazette spoke with Anand, Shaw, and Jelinkova to understand more about the work of these groups and what’s next in generative AI at Harvard.

When generative AI tools first emerged, we saw universities respond in a variety of ways — from encouraging experimentation to prohibiting their use. What was Harvard’s overall approach?

Shaw: From the outset, Harvard has embraced the prospective benefits that GenAI offers to teaching, research, and administration across the University, while being mindful of the potential pitfalls. As a University, our mission is to help enable discovery and innovation, so we had a mandate to actively engage. We set some initial, broad policies that helped guide us, and have worked directly with groups across the institution to provide tools and resources to inspire exploration.

Jelinkova: The rapid emergence of these tools meant the University needed to react quickly, to provide both tools for innovation and experimentation and guidelines to ensure their responsible use. We rapidly built an AI Sandbox to enable faculty, students, and staff to experiment with multiple large language models in a secure environment. We also worked with external vendors to acquire enterprise licenses for a variety of tools to meet many different use cases. Through working groups, we were able to learn, aggregate and collate use cases for AI in teaching, learning, administration, and research. This coordinated, collective, and strategic approach has put Harvard ahead of many peers in higher education.

Anand: Teaching and learning are fundamentally decentralized activities. So our approach was to ask: First, how can we ensure that local experimentation by faculty and staff is enabled as much as possible; and second, how can we ensure that it’s consistent with University policies on IP, copyright, and security? We also wanted to ensure that novel emerging practices were shared across Schools, rather than remaining siloed.

What do these tools mean for faculty, in terms of the challenges they pose or the opportunities they offer? Is there anything you’re particularly excited about?

Anand: Let’s start with some salient challenges. How do we first sift through the hype that’s accompanied GenAI? How can we make it easy for faculty to use GenAI tools in their classrooms without overburdening them with yet another technology? How can one address real concerns about GenAI’s impact?

While we’re still early in this journey, many compelling opportunities — and more importantly, some systematic ways of thinking about them — are emerging. Various Harvard faculty have leaned into experimenting with LLMs in their classrooms. Our team has now interviewed over 30 colleagues across Harvard and curated short videos that capture their learnings. I encourage everyone to view these materials on the new GenAI site; they are remarkable in their depth and breadth of insight.

Here’s a sample: While LLMs are commonly used for Q&A, our faculty have creatively used them for a broader variety of tasks, such as simulating tutors that guide learning by asking questions, simulating instructional designers to provide active learning tips, and simulating student voices to predict how a class discussion might flow, thus aiding in lesson preparation. Others demonstrate how more sophisticated prompts or “prompt engineering” are often necessary to yield more sophisticated LLM responses, and how LLMs can extend well beyond text-based responses to visuals, simulations, coding, and games. And several faculty show how LLMs can help overcome subtle yet important learning frictions like skill gaps in coding, language literacy, or math.

Do these tools offer students an opportunity to support or expand upon their learning?

Anand: Yes. GenAI represents a unique area of innovation where students and faculty are working together. Many colleagues are incorporating student feedback into the GenAI portions of their curriculum or making their own GenAI tools available to students. Since GenAI is new, the pedagogical path is not yet well defined; students have an opportunity to make their voices heard, as co-creators, on what they think the future of their learning should look like.

Beyond this, we’re starting to see other learning benefits. Importantly, GenAI can reach beyond a lecture hall. Thoughtful prompt engineering can turn even publicly available GenAI tools into tutorbots that generate interactive practice problems, act as expert conversational aids for material review, or increase TA teams’ capacity. That means both that the classroom is expanding and that more of it is in students’ hands. There’s also evidence that these bots field more questions than teaching teams can normally address and can be more comfortable and accessible for some students.

Of course, we need to identify and counter harmful patterns. There is a risk, in this early and enthusiastic period, of sparking over-reliance on GenAI. Students must critically evaluate how and where they use it, given its possibility of inaccurate or inappropriate responses, and should heed the areas where their style of cognition outperforms AI. One other thing to watch out for is user divide: Some students will graduate with vastly better prompt engineering skills than others, an inequality that will only magnify in the workforce.

What are the main questions your group has been tackling?

Anand: Our group divided its work into three subgroups focused on policy, tools, and resources. We’ve helped guide initial policies to ensure safe and responsible use; begun curating resources for faculty in a One Harvard repository ; and are exploring which tools the University should invest in or develop to ensure that educators and researchers can continue to advance their work.

In the fall, we focused on supporting and guiding HUIT’s development of the AI Sandbox. The Harvard Initiative for Learning and Teaching’s annual conference , which focused exclusively on GenAI, had its highest participation in 10 years. Recently, we’ve been working with the research group to inform the development of tools that promise broad, generalizable use for faculty (e.g., tutorbots).

What has your group focused on in discussions so far about generative AI tools’ use in research?

Shaw: Our group has some incredible strength in researchers who are at the cutting edge of GenAI development and applications, but also includes voices that help us understand the real barriers to faculty and students starting to use these tools in their own research and scholarship. Working with the other teams, we have focused on supporting development and use of the GenAI sandbox, examining IP and security issues, and learning from different groups across campus how they are using these tools to innovate.

Are there key areas of focus for your group in the coming months?

Shaw: We are focused on establishing programs — such as the new GenAI Milton Fund track — to help support innovation in the application of these tools across the wide range of scholarship on our campus. We are also working with the College to develop new programs to help support students who wish to engage with faculty on GenAI-enabled projects. We aim to find ways to convene students and scholars to share their experiences and build a stronger community of practitioners across campus.

What types of administration and operations questions are your group is exploring, and what type of opportunities do you see in this space?

Jelinkova: By using the group to share learnings from across Schools and units, we can better provide technologies to meet the community’s needs while ensuring the most responsible and sustainable use of the University’s financial resources. The connections within this group also inform the guidelines that we provide; by learning how generative AI is being used in different contexts, we can develop best practices and stay alert to emerging risks. There are new tools becoming available almost every day, and many exciting experiments and pilots happening across Harvard, so it’s important to regularly review and update the guidance we provide to our community.

Can you talk a bit about what has come out of these discussions, or other exciting things to come?

Jelinkova: Because this technology is rapidly evolving, we are continually tracking the release of new tools and working with our vendors as well as open-source efforts to ensure we are best supporting the University’s needs. We’re developing more guidance and hosting information sessions on helping people to understand the AI landscape and how to choose the right tool for their task. Beyond tools, we’re also working to build connections across Harvard to support collaboration, including a recently launched AI community of practice . We are capturing valuable findings from emerging technology pilot programs in HUIT , the EVP area , and across Schools. And we are now thinking about how those findings can inform guiding principles and best practices to better support staff.

While the GenAI groups are investigating these questions, Harvard faculty and scholars are also on the forefront of research in this space. Can you talk a bit about some of the interesting research happening across the University in AI more broadly ?

Shaw: Harvard has made deep investments in the development and application of AI across our campus, in our Schools, initiatives, and institutes — such as the Kempner Institute and Harvard Data Science Initiative. In addition, there is a critical role for us to play in examining and guiding the ethics of AI applications — and our strengths in the Safra and Berkman Klein centers, as examples, can be leading voices in this area.

What would be your advice for members of our community who are interested in learning more about generative AI tools?

Anand: I’d encourage our community to view the resources available on the new Generative AI @ Harvard website , to better understand how GenAI tools might benefit you.

There’s also no substitute for experimentation with these tools to learn what works, what does not, and how to tailor them for maximal benefit for your particular needs. And of course, please know and respect University policies around copyright and security.

We’re in the early stages of this journey at Harvard, but it’s exciting.

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• Open access
• Published: 09 April 2024

National norms for the obstetric nurses’ and midwives’ health education competence, and its influencing factors: a nationwide cross-sectional study

• Jingjing Zou 1   na1 ,
• Jingling Wu 2   na1 &
• Xiumin Jiang 3  

BMC Medical Education volume  24 , Article number:  389 ( 2024 ) Cite this article

Metrics details

Strengthening obstetric nurses’ and midwives’ health education competence is the investment and guarantee for the population’s future health. The purpose of study is to establish national norms for their health education competence, and explore possible influencing factors for providing an uniform criterion identifying levels and weaknesses.

An online questionnaire with a standard process was used to collect data. Three normative models were constructed, and multiple linear regression analysis analyzed possible influencing factors.

The sample respondents ( n  = 3027) represented obstetric nurses and midwives nationally. Three health education competency normative norms (mean, percentile and demarcation norm) were constructed separately. Locations, hospital grade, department, marital status, training times and satisfaction with health education training influenced obstetrical nurses’ and midwives’ health education competence ( P <0.05).

This study constructed the first national standard for assessing obstetric nurses’ and midwives’ health education competence, providing a scientific reference to evaluate the degree of health education competence directly. These known factors could help clinical and policy managers designate practice improvement measures. In future research, Grade I hospitals should be studied with larger sample sizes, and indicators need to improve to reflect health education’s effect better.

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The World Health Organization has long recommended [ 1 ] that pregnant women need more health education, life guidance, and follow-up visits. “Outline of the Healthy China 2030 Plan” [ 2 , 3 ] also proposed that health services would be strengthened to improve the health of women and children, and it is essential to provide health education covering the prenatal, perinatal and postnatal periods. Comprehensive and practical health education can significantly enhance maternal and infant safety, promote spontaneous delivery, and increase exclusive breastfeeding rates [ 4 ]. Authorities such as the International Confederation of Midwives [ 5 ] and the American College of Nurse-Midwives [ 6 ] emphasise the critical role of midwives and obstetric nurses in providing comprehensive care, assisting in labour and delivey, and managing complications [ 7 ]. Obstetric nurses and midwives should possess extensive health knowledge and excellent education competence to ensure women and their families can make informed decisions, and safely manage maternal health and well-being [ 8 ].

Strengthening obstetric nurses’ and midwives’ health education competence is the investment and guarantee for the population’s future health [ 9 ]. Given its importance, researchers have conducted in-depth discussions on health education quality, goals, strategy and evaluation. However, no study has built a uniform criterion for assessing the performance of obstetric nurses’ and midwives’ health education competency. A norm, a reference standard for the scores obtained using a scale, is usually the average score and standard deviation of many testers. A norm could compare the differences between different groups and assess individual performance [ 10 ]. Meanwhile, based on the normal analysis, a more scientific and reliable scale promotion strategy can be formed to popularize and promote relevant scientific theories and methods [ 11 ].

The rating scale of health education competence for nurses (RSHECN) was developed and verified its reliability and validity (Tong and Li, 2010). The scale determined that good performance in health education requires nurses to have adequate expertise, sound assessment, planning and implementation and the ability to evaluate the effectiveness of health education, which calcified the connotation of health education competence for nurses and provided a way for evaluation. Therefore, a nationwide cross-sectional survey of multi-stage stratified cluster sampling was conducted to establish norms for RSHECN and explore their influencing factors of health education competence, providing a reference for clinical and policy managers to identify weaknesses and formulate practice improvement plans.

Study design

A cross-sectional study of multi-stage stratified cluster sampling was carried out [ 12 , 13 ]. The nationwide obstetric nurses and midwives were selected as participants from April to May 2021 to establish the mean norm, percentile norm and demarcation norm of RSHEC and explore possible influencing factors of obstetric nurses’ and midwives’ health education competence.

Participants

The participants were recruited using a stratified multistage cluster sampling method with three steps: (1) Selected representative regions. Three regions (Eastern China, Central China and Western China) were selected, divided by the National Bureau of Statistics of China according to geographic location and economic level. (2) Selected provincial administrative unit (from now it was referred to as the “unit”). The convenient sampling method was used to decide the final units. Seven out of eleven in the eastern region were selected: Tianjin, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian and Guangdong. Four out of eight units in the central region were selected: Shanxi, Heilongjiang, Jiangxi and Hunan. Seven out of twelve units in the western region were selected: Sichuan, Chongqing, Gansu, Qinghai, Xinjiang, Guangxi and Inner Mongolia. (3) Selected included hospitals. The selection of hospitals adopted a convenient sampling method and ensured the diversity of garde I, II and III hospitals as much as possible. After that, with the consent of the hospital nursing department, a whole-group sampling method was used to include all obstetric nurses who met the inclusion criteria in the included hospitals. All active registered obstetrical nurses or midwives who voluntarily participated were included in this study. Moreover, interns, visiting nurses, and nurses who were absent during the survey or could not attend for personal reasons were excluded. The ethical committee of the principal researcher’s hospital approved the study (No 2018 − 206). Before the survey, written consent was obtained from all nursing departments. The questionnaire does not collect the personal information of the participants, and the database can only be accessed by the members of the research group. Participants were informed consent, and the returning online questionnaire was considered consent of participation.

Data collection

An introduction letter stating the study aim and process was issued to the department of the selected hospital to obtain survey permission. Then the online training was held to conduct a unified training for the hospital responsible person for the project. The standard data collection process was introduced to the responsible person with a standard language, and the contact information of the research group was provided to communicate the problem during the survey. The standard data collection process is the following: (1) Screen potential participants according to inclusion and exclusion criteria; (2) Seek the consent of potential participants. (3) Emphasize anonymity and confidentiality and sign the informed consent; (4) Invite participants to complete the questionnaire. Considering workforce and material resources, this survey adopts electronic questionnaires by the software “Questionnaire Star”, which helps to distribute questionnaires more scientifically in such an extensive national survey. The procedure was set so that each electronic device could only fill in the questionnaire once and submit the questionnaire after completing all items within 30 min. At the end of the survey, 5% of the questionnaires were randomly selected for quality check.

Measurements

The health education competence assessment questionnaire involves two parts: (1) general information questionnaire: The questionnaire was designed by reviewing relevant literature research and discussing with obstetric nursing experts, which covered the potential factors that might affect the health education competence of obstetrical nurses and midwives, including the type of hospital, age, educational level, current work department, additional training in health education, working years and other basic social demographic information. (2) Rating Scale of Health Education Competence for Obstetric Nurses and Midwives. The scale was used to evaluate the competence of health education of nurses and midwives, which had been authorised by the developer of Tong [ 14 ]. The self-evaluation scale includes four dimensions: assessment, plan, implementation and evaluation. Thirty-eight items on a five-point Likert-type scale (1 to 5, “completely disagree” to “completely agree”) and all items are positive. The score ranges from 37 to 185, and higher scores indicate better health education competence. The psychological verification was completed among various departments, including the obstetric nurse and midwife. The scale’s Cronbach α and half-fraction reliability were 0.949 and 0.953, the content validity index was 0.90, and it was verified with good construction validity and distinguish validity [ 14 ]. In this sample, 500 questionnaires were randomly selected in proportion to the number of obstetric nurses and midwives for reliability testing, and its Cronbach α was 0.987. Moreover, to facilitate understanding and comparison, the results of this study were analysed using conversion score, and the formula is as follows: conversion score = (original score theoretical minimum score of this aspect) / (theoretical maximum score theoretical minimum score of this aspect) ×100.

Data analysis

All calculations were performed using IBM SPSS Statistics software (version 26.0). Continuous variables were reported as mean ( \(\stackrel{-}{\text{x}}\) ) ± standard (S), and categorical variables were presented as frequencies and proportions. Three types of norms were developed in this study to establish normative values for health education competence among obstetric nurses and midwives. The mean norm was determined using the results of one-way ANOVA to calculate the mean and standard deviation of conversion scores and each dimension score. Percentile norm was established using the percentile method, with 5% percentile intervals, resulting in normative values at the 5th, 25th, 50th, 75th, and 95th percentiles. The demarcation norm was established through the distribution method with different demarcation schemes calculated at a spacing of 0.5 S within the total scale score ( \(\stackrel{-}{x}\)  ± 2.5 S). After that, we performed correlation analysis and selected the scheme with the highest correlation as the demarcation constant for the study [ 15 ]. Differences in assessment, plan, implementation, evaluation and conversion scores were analysed using an independent two-sample t-test and one-way analysis of variance, with demographic characteristics as independent variables. Statistically significant variables from the ANOVA analysis were included as independent variables in a stepwise multiple linear regression analysis to evaluate their contributions to conversion scores. In this study, covariance diagnosis of independent variables is based on tolerance (TOL) and variance inflation factor (VIF), and if TOL < 0.1 or VIF ≥ 10, it means that there is serious covariance between independent variables.

Three thousand three hundred questionnaires were received, 97 were excluded due to logical self-contradiction of data and abnormal distribution of values, and 3207 questionnaires were available with an effective recovery of 97.18%. All participants were female between eighteen and sixty-four years (33.20 ± 7.51 years). They had one to forty-five working years with an average of (11.00 ± 8.15) years covering the general population for job title, education, department and health education training conditions. Detailed demographic characteristics of the sample are shown in Table  1 .

Mean norms could be established for groups with different characteristics in the tested population. Considering the different economic and medical levels, five categorical mean norms were determined, including grade III hospitals, grade II hospitals, eastern China, central China and western China (Table  2 ). There is no specification Grade I for hospitals because of the insufficient sample size of primary hospitals (only 41 nurses from Grade I hospitals). The percentile norm was calculated based on scale scores and each dimension score at an interval of 5%, as shown in Table  3 . The distribution method was used to establish the demarcation norm, and plan 4, with the highest correlation coefficient ( r  = 0.970), was selected as the final scheme, as shown in Table  4 . The final demarcation grade was extremely poor [0, 70.32), poor [70.32, 76.5), medium [76.5, 88.86), good [88.86, 95.04), and excellent [95.04, 100].

The results of one-way ANOVA showed statistically significant differences ( P  < 0.05) in the health education competency conversion scores comparing hospital type, hospital grade, department, locations, marital status, satisfaction with health education training, and training times of health education. The multiple linear regression analysis showed that hospital grade ( P  = 0.002), locations ( P  = 0.032), department ( P  = 0.001), marital status( P  = 0.003), satisfaction with health education training ( P  < 0.001), and training times of health education ( P  = 0.006) were independent influencing factors of obstetric nurses’ and midwives’ health education competency scores. In this study, the TOL values were 0.956–0.993 and VIF values were 1.007–1.046, which cannot be considered as the existence of multiple covariance between independent variables, and all independent variables can be analysed by multiple regression.

This study established the first national norms for obstetric nurses’ and midwives’ health education competency and explored possible influencing factors. The mean norm can be used to determine whether obstetric nurses’ and midwives’ health education competency is within the reference range [ 15 ]. The result showed that the health education competency was highest in Central areas, followed by Eastern areas, and the lowest in Western areas. The central and east areas have superior medical resources, attracting more medical and nursing talents, while the western region has more mountainous areas with less developed medical resources. Central region scores higher than East region, probably because Central region contains fewer cities. The sample size of this survey is smaller, which makes its average score higher. The mean norm describes the overall level, and the percentile norm was formed to compare the individual score within the corresponding percentile norm to identify individual positions in the group [ 16 , 17 ]. The higher the score, the higher the percentile norm position, which means the health education competency level is better. The result of showed that the best division scheme was extremely poor [0, 70.32), poor [70.32, 76.5), medium [76.5, 88.86), good [88.86, 95.04) and excellent [95.04, 100], which make the scores for different indicators can be compared easily, reducing the difficulty of interpreting and comparing data, while also allowing for a more intuitive and accurate assessment of individual performance.

In this study, the mean scale score was (82.68 ± 12.36), which is intermediate compared to the norm [ 18 , 19 ]. The conversion scores from highest to lowest were implementation, evaluation, assessment and planning, consistent with clinical practice. In the clinical environment, each pregnant woman has different educational needs. However, nurses, as mainly part of implementer of health education, only teach fixed content but do not individualise health education on a case-by-case basis. Although there are often many research materials, such as guidelines, to guide obstetric nurses and midwives on what to do, they often copy and use indoctrination again, lacking individualised assessment of pregnant women [ 20 ]. Thus, the result prompts us to form a practical health education model in line with national conditions, strengthing the status of evaluation, assessment and planning to provide individualised health education and play the role of health education better.

The study identified that locations, hospital grade, department, marital status, satisfaction with health education training and training times were influencing factors for obstetric nurses’ and midwives’ health education competence. Among different locations, the disparity in medical conditions may lead to managers with different perceptions on the role of nurses’ and midwives’ in health education. Within health care teams, obstetric nurses and midwives are vital health education providers throughout the pregnancy and delivery. The government could introduce more policies and supportive steps to improve the attention of hospitals in underdeveloped areas to the health education capacity of nurses.

The score of tertiary hospitals was higher than secondary, and the possible reason is that tertiary hospitals absorbs higher qualified nursing talents [ 21 , 22 ], and they have more robust medical resources, research and teaching capabilities to provide more professional training and education and are more excellent regarding professional qualifications and skills [ 23 ]. Meanwhile, the regression analysis showed that the times and satisfaction of health education training were influencing factors. Long-term participation in health education training could enhance the professional confidence, stability and self-confidence of obstetrical nurses and midwives [ 23 , 24 ]. Satisfactory training can encourage applying knowledge and skills in practical work, promoting health education competency and work continuity [ 25 ]. Each training is a process of knowledge accumulation, and the increasing knowledge reserve in reproductive health, prenatal, intrapartum and postpartum care can better guide maternal health management and improve the life quality of birthing mothers and their infants [ 26 , 27 ]. Therefore, for hospitals managers, the organization of comprehensive, professional and satisfactory health education knowledge training should be regarded as an important part of management, especially for grassroots hospitals.

Another interesting result is that the health education competence of married and fertile nurses was better, who can better feel the actual needs of pregnant women and combine their own experience to provide more detailed and thoughtful health education in dealing with various real situations [ 28 , 29 ]. Future research can explore more health education methods from the perspective of maternity, so as to help unmarried and infertile nurses and midwives. Our result also showed that midwives scored lower than obstetric nurses, which may be due to the different work nature. Generally, obstetric nurses provide health education in the ward, while midwives in the delivery room. The unique physiological conditions for childbirth can make it challenging to provide health education. And the demand for health education after delivery is more significant, as the mother and her family require more information about puerperal rehabilitation and neonatal care. When providing health education, midwives and obstetric nurses could promote strengths and avoid weaknesses. Obstetric nurses can provide comprehensive health education for mothers and their families after delivery, and midwives can try to move forward their own health education opportunities and provide health education in midwives’ outpatient clinics.

A normative standardised reference will serve as a reference to help obstetric nurses and midwives identify strengths and weaknesses in health education competence and help management establish a more reasonable nursing echelon for enhancing maternal health [ 30 , 31 ]. The nationwide cross-sectional survey could help clinical and policy managers understand the current health education situation and formulate corresponding management plans for practice improvement [ 32 , 33 ]. Although the results reported here are of interest, it is necessary to acknowledge certain limitations of the study. Firstly, due to time and human constraints, the small sample size of the Grade I hospitals in this study affected the completeness of the norm. Also, the convenience sampling method used for hospital selection might introduce bias, as it does not ensure a randomized and comprehensive representation of all hospital grades, particularly Grade I hospitals. Future studies should aim for a more extensive and diverse sample, including a better representation of all hospital grades. Secondly, the study is limited to a specific time frame, which may not adequately represent changes over time. A longitudinal approach could offer insights into how health education competence evolves over time and its long-term impact on patient care and outcomes. Thirdly, the scale is a self-assessment scale, which is subjective in evaluating health education competence and lacks objective evaluation indicators. Obstetric nurses and midwives with higher scores indicate a certain level of health education competence. However, the effect of health education is not reflected by objective indicators, which need to be improved in future studies. Finally, implementing and evaluating training interventions could provide practical insights into effective strategies for improving health education competence among obstetric nurses and midwives.

A nationwide cross-sectional study of multi-stage stratified cluster sampling was conducted to establish the first national norms for obstetric nurses’ and midwives’ health education competency. Locations, hospital grade, department, marital status, satisfaction with health education training and training times were independent influencing factors for obstetric nurses’ and midwives’ health education competence. The study provides a valid way to assess obstetric nurses’ and midwives’ health education competency comprehensively and comparatively. It helps practitioners make more informed choices when developing relevant programs or decisions. In future research, Grade I hospitals should be studied with larger sample sizes, and indicators need to improve to reflect health education’s effect better.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Thanks to all participants for their valuable contribution to this study.

Author information

Jingjing Zou and Jingling Wu contributed equally to this work.

Authors and Affiliations

School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China

Jingjing Zou

Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China

Jingling Wu

Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, No.18 Daoshan, Fuzhou, Fujian Province, China

Xiumin Jiang

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Contributions

JJ.Z was responsible for data analysis, data interpretation and drafted the work.JL.W was responsible for conception, design and substantively revised work.XM.J was responsible for data acquisition and project administration.

Corresponding author

Correspondence to Xiumin Jiang .

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Ethics approval and consent to participate.

All active registered obstetrical nurses or midwives who voluntarily participated were included in this study. The ethical committee of the principal researcher’s hospital approved the study (No 2018 − 206). Before the survey, written consent was obtained from all nursing departments. Participants were informed consent, and the returning online questionnaire was considered consent of participation.

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Zou, J., Wu, J. & Jiang, X. National norms for the obstetric nurses’ and midwives’ health education competence, and its influencing factors: a nationwide cross-sectional study. BMC Med Educ 24 , 389 (2024). https://doi.org/10.1186/s12909-024-05249-w

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Adams J, Bateman B, Becker F, et al. Effectiveness and acceptability of parental financial incentives and quasi-mandatory schemes for increasing uptake of vaccinations in preschool children: systematic review, qualitative study and discrete choice experiment. Southampton (UK): NIHR Journals Library; 2015 Nov. (Health Technology Assessment, No. 19.94.)

Effectiveness and acceptability of parental financial incentives and quasi-mandatory schemes for increasing uptake of vaccinations in preschool children: systematic review, qualitative study and discrete choice experiment.

Chapter 7 recommendations for future research.

Recommendations for future research have been considered in the discussion sections of Chapters 3 – 5 and are summarised here for ease of reference. We have attempted to place these in priority order.

• Further evidence is required on the effectiveness and cost-effectiveness of parental financial incentive and quasi-mandatory interventions for encouraging the uptake of preschool vaccinations. As such, interventions are likely to be implemented on a large scale; evaluation strategies such as natural experiments and step-wedge designs may be most useful in generating such evidence. 82
• Further evidence is required on the most effective and cost-effective configuration of any parental financial incentive and quasi-mandatory interventions for encouraging the uptake of preschool vaccinations. Intervention development work, taking account of existing behaviour-change theory, may be useful to maximise the potential effectiveness of incentive interventions. This should involve further consideration of the effective component, or components, of financial incentive interventions.
• Further consideration of reasons for non-vaccination should be incorporated into new interventions for promoting the uptake of preschool vaccinations. Parental financial incentive and quasi-mandatory interventions for encouraging uptake of preschool vaccinations may not adequately address the reasons for non-vaccination in high-income countries that tend to achieve overall high coverage of preschool vaccinations.
• Further consideration of how a quasi-mandatory intervention for encouraging the uptake of preschool vaccinations could be designed and implemented is required. Particular issues requiring further consideration include data sharing of vaccination status between health-care providers and schools, responsibilities of different sectors and staff, and how provision would be made for legitimate opt-out.
• If high-quality evidence of effectiveness of parental financial incentive and quasi-mandatory interventions for encouraging uptake of preschool vaccinations is generated, further evidence is required on how to effectively communicate this information to all stakeholders. As acceptability is linked to perceived effectiveness, further evidence on the impact of well-communicated effectiveness evidence on perceived acceptability is also required.
• The factors that may increase acceptance of mandatory schemes warrant further research, and additional DCEs could be conducted to explore parental preferences on how a mandate for vaccination might be imposed.
• Further consideration may be required of how existing systems and resources for encouraging the uptake of preschool vaccinations can be optimised. In particular, further evidence may be required on how to provide accessible information and education, and how to deliver accessible vaccination services. However, although these issues were raised in the present work, we did not conduct a systematic review on these topics and, as such, cannot make definitive recommendations for future research.
• Research engaging parents in an iterative codesign process to design optimally acceptable and usable information that conveys robust and balanced data on the consequences of disease and the benefits and risks of vaccinations is required.

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• Cite this Page Adams J, Bateman B, Becker F, et al. Effectiveness and acceptability of parental financial incentives and quasi-mandatory schemes for increasing uptake of vaccinations in preschool children: systematic review, qualitative study and discrete choice experiment. Southampton (UK): NIHR Journals Library; 2015 Nov. (Health Technology Assessment, No. 19.94.) Chapter 7, Recommendations for future research.
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• Q&A: Tips for viewing the 2024 solar eclipse

Q&A: Tips for viewing the 2024 solar eclipse

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On Monday, April 8, the United States will experience a total solar eclipse — a rare astronomical event where the moon passes directly between the sun and the Earth, blocking out the sun’s light almost completely. The last total solar eclipse in the contiguous U.S. was in 2017, and the next one won’t be until 2044.

If the weather cooperates, people across the United States — from northeastern Maine to southwestern Texas — will be able to observe the eclipse using protective eyewear. Those in the path of totality , where the moon entirely covers the sun, will have the best view, but 99% of people in the continental U.S. will be able to see a partial eclipse. Weather permitting, those on the MIT campus and the surrounding area will see 93 percent of the sun covered, with the partial eclipse starting at 2:15 p.m. and reaching its peak around 3:29 p.m. Gatherings are planned at the Kresge Oval and the MIT Museum , and a live NASA stream will be shown in the Building 55 atrium .

Brian Mernoff , manager of the CommLab in the Department of Aeronautics and Astronautics, is an accomplished astrophotographer and science educator. Mernoff is headed to Vermont with his family to experience the totality from the best possible angle — but has offered a few thoughts on how to enjoy the eclipse safely, wherever you are.

Q: What should viewers expect to see and experience with this solar eclipse?

A: When you’re watching TV (the sun) and your toddler, dog, or other large mammal (the moon) blocks your view, you no doubt move over a bit to try to get a partial or full view of the TV. This is exactly how the path of totality works for an eclipse. If you are exactly in line with the moon and sun, it will be completely blocked, but if you start moving away from this path, your view of the sun will start to increase until the moon is not in the way at all.

The closer you are to the path of totality, the more of the sun will be blocked. At MIT, about 93 percent of the sun will be blocked. Those in the area will notice that things around you will get slightly darker, just like when it starts to become overcast. Even so, the sun will remain very bright in the sky and solar glasses will be required to view the entirety of the eclipse. It really goes to show how incredibly bright the sun is!

Within the narrow path of totality, the moon will continue to move across the sun, reaching 100 percent coverage. For this short period of time, you can remove your glasses and see a black disk where the sun should be. Around the disk will be wispy white lines. This is the corona, the outermost part of the sun, which is normally outshone by the sun’s photosphere (surface). Around the edges of the black disk of the moon, right as totality begins and ends, you can also see bright spots around the edges, known as Bailey’s Beads, caused by sunlight shining between mountains and craters on the moon.

But that’s not all! Although you will be tempted to stare up at the sun throughout totality, do not forget to observe the world around you. During totality, it feels like twilight. There is a 360-degree sunset, the temperature changes rapidly, winds change, animals start making different sounds, and shadows start getting weird (look into “shadow bands” if you have a chance).

As soon as totality ends, and you start to see Baily’s Beads again, put your solar glasses back on as it will get very bright again very fast as the moon moves out of the way.

Q: What are the best options for viewing the eclipse safely and to greatest effect?

A: No matter where you are during the eclipse, make sure you have solar glasses. These glasses should be ISO-approved for solar viewing. Do not use glasses with scratches, holes, or other damage.

If you are unable to obtain solar glasses in time, you can safely view the eclipse using a home-made projection method , such as a pinhole camera or even projecting the image of the sun through a colander.

The best view of the eclipse will be from within the path of totality, but even if you are not within it, you should still go outside to experience the partial eclipse. Use the NASA Eclipse Explorer to find the start, maximum, and end times, and then find a nice spot outside — preferably with some shade — put on your glasses, and enjoy the show.

For a closer view of the sun, find a friend that has a telescope with the correct ISO-certified solar filter. This will let you see the photosphere (or chromosphere if it is an H-alpha scope) in a lot more detail. If you do not have access to a telescope, NASA plans to livestream a telescope view throughout the eclipse. [The livestream will be displayed publicly on a large screen in Building 55 at MIT, rain or shine.]

The only time you can look at or image the sun without a filter is during 100 percent totality. As soon as this period is done, glasses and filters must be put back on.

After the eclipse, keep your glasses and filters. You can use them to look at the sun on any day (it took me an embarrassing amount of time to realize that I could use the glasses at any time instead of lugging out a telescope). On a really clear day, you can sometimes see sunspots!

Q: How does eclipse photography work?

A: This year I plan to photograph the eclipse in two ways. The first is using a hydrogen-alpha telescope. This telescope filters out all light except for one wavelength that is given off by hydrogen. Because it blocks out most of the light from the sun’s surface, it allows you to see the turbulent upper atmosphere of the sun, including solar prominences that follow magnetic field lines.

Because this telescope does not allow for imaging during totality as too much light is blocked, I also plan to set up a regular camera with a wide-angle lens to capture the total eclipse with the surrounding environment as context. During the 2017 eclipse, I only captured close-ups of the sun using a regular solar filter and missed the opportunity to capture what was going on around me.

Will it work? That depends on if we get clear skies, and how many pictures of my 1.5-year-old need to be taken (as well as how much chasing needs to be done).

If you would like to take pictures of the eclipse, make sure you protect your camera sensor. The sun can easily damage lenses, sensors, and other components. Here are some examples of solar damaged cameras . The solution is simple, though. If using a camera phone, you can take pictures through an extra pair of solar glasses, or even tape them to the phone. For cameras with larger lenses, you can buy cardboard filters that slide over the front of your camera or even buy ISO-approved solar film and make your own.

Q: Any fun, unique, cool, or interesting science facts about this eclipse to share?

A: If you want to get even more involved with the eclipse, there are many citizen science projects that plan to collect as much data as possible throughout the eclipse.

NASA is planning to run several experiments during the eclipse , and researchers with MIT Haystack Observatory will also be using four different technologies to monitor changes in the upper atmosphere , both locally and across the continent.

If you are interested in learning more about the eclipse, here are two of my favorite videos, one on “ unexpected science from a 0.000001 megapixel home-made telescope ” and one on solar eclipse preparation .

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A marketing master class by taylor swift.

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Swift left an indelible mark on modern marketing practices.

David Lopez-Lopez is Senior Lecturer in Marketing at Esade

The Taylor Swift phenomenon goes far beyond music. The iconic singer-songwriter and 14-time Grammy winner has not only redefined her industry but also left an indelible mark on modern marketing practices, showcasing the transformative power of strategic branding and community engagement and even influencing geopolitical dynamics. Here are some of the strategies that explain the global fascination that the 34-year-old artist has won.

Personal narrative and connection with fans

A consistent brand narrative: Swift has created a consistent brand over time by weaving captivating storytelling throughout her career, turning each album into a chapter of her personal story. She managed to transition from country to pop (starting with the album “1989,” released in 2014), before adopting a darker and more challenging image (in “ Reputation ,” 2017), and then continuing to evolve towards a more alternative and indie sound (in “ Folklore ” and “ Evermore ,” released, surprisingly, in July and December 2020). What’s more, she has done all this while successfully navigating numerous key moments in her career in which she has proven deft at managing communication, incidents, and reputation, such as the controversy sparked by Kanye West’s song “Famous” (which refers to her in unpleasant terms ), her romantic adventures and misadventures, or even her clear political positioning.

Understanding your audience : Swift has demonstrated exceptional mastery in understanding and catering to her audience : the “Swifties.” She studies their preferences, habits, and desires in depth, cultivating a unique emotional connection that goes beyond the typical artist-fan relationship. An example is her active presence on social media platforms such as TikTok, where she interacts directly with her followers under the hashtag #SwiftTok, generating a continuous, proactive, two-way feedback loop and leveraging user-generated content.

Omnichannel strategy: This connection with her audience extends to her active presence on other social media and digital platforms, and even to her song lyrics, where she implements an omnichannel strategy to engage with her fans across various touchpoints. Through her lyrics, which explore themes of love, personal challenges, and fictional stories, she establishes a deep connection with her mostly young audience, nurturing a feeling of identification and belonging. The song “ 22 ,” for example, about how she felt about being 22 years old, captures the experience of being young and having fun, addressing topics such as friendship, freedom, and the celebration of life. The song has become an anthem for many young people transitioning to early adulthood and is widely used in the industry to evoke emotions in this segment, appearing in advertisements, TV shows, and movies.

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Exclusivity and closeness: Swift goes beyond mere interaction, offering exclusive experiences such as secret sessions for select fans and hidden messages embedded in her lyrics, enhancing the feeling of exclusivity and closeness within her community. Additionally, her fans actively participate in community-building activities, such as creating and exchanging “ friendship bracelets ” during concerts, further strengthening the bond between Swift and her followers.

Queen of anticipation and surprise

Marketing tactics: Applying buzz marketing and experiential marketing tactics, Swift has managed to generate anticipation and excitement around her releases. From the mysterious removal of content on social media to the use of hidden clues and surprises at her concerts, the artist has kept her followers on edge, fueling frenzy before each new release by offering “teasers.” These teasers provide a brief and enticing preview of what’s to come without revealing too much, in order to intrigue and capture the audience’s attention, thereby generating even more anticipation for the full release. She complements these strategies by offering pre-sale tickets to her concerts for fans, through her website or specific ticketing platforms. These exclusive pre-sales make her fans feel privileged and excited to secure their tickets before the general public, helping to create an atmosphere of anticipation and excitement surrounding the concert, which some fans describe as “almost like a parallel world.”

The power of collaboration: Additionally, she has established key partnerships with artists such as Ed Sheeran or Shawn Mendes, as well as with brands, such as her long-standing collaborations with Target, Diet Coke, Apple, and Keds or her exclusive releases through AMC Theaters, helping her expand her fan base and reinforce her brand image.

Perpetual self-reinvention: Moreover, she has demonstrated a willingness to reinvent herself without sacrificing brand coherence, from reinterpreting her past hits to experimenting with new sounds. In 2019, she faced legal controversy over the acquisition of the rights to her music, leading her to re-record previous albums such as “Fearless (Taylor’s Version)” and “Red (Taylor’s Version),” thereby regaining creative and financial control over her music. This strategy garnered publicity and media attention, leveraging the love and nostalgia of her fans, resulting in a significant increase in sales and streaming of these new versions, and, as some authors said, allowing her to “ replace herself at No. 1 .”

More than just music

Economy and geopolitical power: Taylor Swift’s global impact extends beyond music to have a considerable economic reach. Her concerts not only boost ticket sales but also generate additional income for the local industry, including hotels, restaurants, and transportation. The case of Singapore is particularly noteworthy, as the city has bought the exclusivity of her 6 concerts in Southeast Asia. They expect to generate around $370 million from tourism, and have already experienced a 186% increase in flights during her first stay. However, neighboring countries such as Thailand and the Philippines feel disadvantaged by what they consider “unfair competition.” This conflict underscores the economic and geopolitical disparities in the region. Even as Singapore emerges as a world-class event hub, places such as Hong Kong are marginalized by missing out on the opportunity to attract international artists and the associated tourist spending.

Swift has meticulously crafted a cohesive brand narrative focusing on her audience and the creation of a community. Through these efforts, she has successfully built a unique identity, garnered significant media interest, and maintained her relevance in the highly competitive music industry. Her ability to mobilize the masses and generate global fervor offers a valuable marketing master class and highlights the power of artists to influence the economy and society worldwide.

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