literature review and policy analysis

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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

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Political Science Subject Guide: Literature Reviews

• Political Science
• Books & Dissertations
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• Literature Reviews
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More Literature Review Writing Tips

• Thesis Whisperer- Bedraggled Daisy Lay advice on writing theses and dissertations. This article demonstrates in more detail one aspect of our discussion

Books on the Literature Review

What is a literature review?

"A literature review is an account of what has been published on a topic by accredited scholars and researchers. [...] In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries."

(from "The Literature Review: A Few Tips on Writing It," http://www.writing.utoronto.ca/advice/specific-types-of-writing/literature-review )

Strategies for conducting your own literature review

1. Use this guide as a starting point. Begin your search with the resources linked from the political science subject guide. These library catalogs and databases will help you identify what's been published on your topic.

2. What came first? Try bibliographic tracing. As you're finding sources, pay attention to what and whom these authors cite. Their footnotes and bibliographies will point you in the direction of additional scholarship on your topic.

3. What comes next? Look for reviews and citation reports. What did scholars think about that book when it was published in 2003? Has anyone cited that article since 1971? Reviews and citation analysis tools can help you determine if you've found the seminal works on your topic--so that you can be confident that you haven't missed anything important, and that you've kept up with the debates in your field. You'll find book reviews in JSTOR and other databases. Google Scholar has some citation metrics; you can use Web of Science ( Social Sciences Citation Index ) for more robust citation reports.

4. Stay current. Get familiar with the top journals in your field, and set up alerts for new articles. If you don't know where to begin, APSA and other scholarly associations often maintain lists of journals, broken out by subfield . In many databases (and in Google Scholar), you can also set up search alerts, which will notify you when additional items have been added that meet your search criteria.

5. Stay organized. A citation management tool--e.g., RefWorks, Endnote, Zotero, Mendeley--will help you store your citations, generate a bibliography, and cite your sources while you write. Some of these tools are also useful for file storage, if you'd like to keep PDFs of the articles you've found. To get started with citation management tools, check out this guide . 

How to find existing literature reviews

1. Consult Annual Reviews.  The Annual Review of Political Science consists of thorough literature review essays in all areas of political science, written by noted scholars. The library also subscribes to Annual Reviews in economics, law and social science, sociology, and many other disciplines.

2. Turn to handbooks, bibliographies, and other reference sources. Resources like Oxford Bibliographies Online and assorted handbooks ( Oxford Handbook of Comparative Politics , Oxford Handbook of American Elections and Political Behavior , etc.) are great ways to get a substantive introduction to a topic, subject area, debate, or issue. Not exactly literature reviews, but they do provide significant reference to and commentary on the relevant literature--like a heavily footnoted encyclopedia for specialists in a discipline. 

3. Search databases and Google Scholar.   Use the recommended databases in the "Articles & Databases" tab of this guide and try a search that includes the phrase "literature review."

4. Search in journals for literature review articles.  Once you've identified the important journals in your field as suggested in the section above, you can target these journals and search for review articles. 

5. Find book reviews.  These reviews can often contain useful contextual information about the concerns and debates of a field. Worldwide Political Science Abstracts is a good source for book reviews, as is JSTOR . To get to book reviews in JSTOR, select the advanced search option, use the title of the book as your search phrase, and narrow by item type: reviews. You can also narrow your search further by discipline.

6. Cast a wide net--don't forget dissertations.  Dissertations and theses often include literature review sections. While these aren't necessarily authoritative, definitive literature reviews (you'll want to check in Annual Reviews for those), they can provide helpful suggestions for sources to consider.

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Rigorous Policy-Making Amid COVID-19 and Beyond: Literature Review and Critical Insights

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Received 2021 Oct 10; Accepted 2021 Nov 24; Collection date 2021 Dec.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

Policies shape society. Public health policies are of particular importance, as they often dictate matters in life and death. Accumulating evidence indicates that good-intentioned COVID-19 policies, such as shelter-in-place measures, can often result in unintended consequences among vulnerable populations such as nursing home residents and domestic violence victims. Thus, to shed light on the issue, this study aimed to identify policy-making processes that have the potential of developing policies that could induce optimal desirable outcomes with limited to no unintended consequences amid the pandemic and beyond. Methods: A literature review was conducted in PubMed, PsycINFO, and Scopus to answer the research question. To better structure the review and the subsequent analysis, theoretical frameworks such as the social ecological model were adopted to guide the process. Results: The findings suggested that: (1) people-centered; (2) artificial intelligence (AI)-powered; (3) data-driven, and (4) supervision-enhanced policy-making processes could help society develop policies that have the potential to yield desirable outcomes with limited unintended consequences. To leverage these strategies’ interconnectedness, the people-centered, AI-powered, data-driven, and supervision-enhanced (PADS) model of policy making was subsequently developed. Conclusions: The PADS model can develop policies that have the potential to induce optimal outcomes and limit or eliminate unintended consequences amid COVID-19 and beyond. Rather than serving as a definitive answer to problematic COVID-19 policy-making practices, the PADS model could be best understood as one of many promising frameworks that could bring the pandemic policy-making process more in line with the interests of societies at large; in other words, more cost-effectively, and consistently anti-COVID and pro-human.

Keywords: COVID-19, health policy, public health, PADS, people-centered

1. Background

As much as policies shape society, they create it as well [ 1 ]. The change can be either slow or fast—depending on the context, newly found commonalities, communities, cultures, if not new reckonings among the civilizations, can either occur incrementally or with lightning speed [ 2 , 3 , 4 ]. Take COVID-19 prevention policies, for instance. Ranging from loose measures to long-term mandates, COVID-19 policies have created communities (e.g., mask supporters, anti-vaxxers, conspiracy theorists, and citizen vigilantes) [ 5 , 6 , 7 ], cultures (e.g., the xenophobic culture, the civic culture) [ 8 , 9 , 10 ], and perhaps most importantly, new understandings of the shared vulnerabilities and strengthens of the civilization (e.g., the peril of extremely tiny viruses, the power of small vials of vaccines, and the promise of victory-minded humanity) [ 11 , 12 , 13 ].

Public policies can be understood as the “purposive course of action followed by an actor or a set of actors in dealing with a problem or matter of concern” [ 14 ], which are often “formal, legally-binding measures adopted by legislative and administrative units of government” [ 15 ]. Overall, public policies are arbitrary rules and regulations developed to create social goods [ 16 ]. Ranging from shelter-in-place measures to lockdown mandates, and masking rules to vaccine regulations, one common denominator of these policies is their ability to curb the spread of the pandemic, and in turn, COVID-19 infections, hospitalizations, and deaths [ 17 , 18 , 19 ]. In an epidemiological modeling study across eight countries, researchers found that an additional delay of imposing lockdown measures amid COVID-19 outbreaks for one week could result in half a million deaths that could have been avoided [ 20 ]. In a similar vein, an early implementation of stringent public policies on physical distancing and an early lifting of these policies are the main reasons the state of California had successfully controlled the COVID-19 outbreak first [ 21 ], and only later became the first state in the U.S. that surpassed 500,000 confirmed cases and 10,000 deaths [ 22 , 23 ].

However, it is important to note that public policies could also result in unintended consequences. A growing body of research indicates that separating people from their familiar routines and social environments could have devastating effects on their physical and psychological health [ 24 , 25 , 26 ]. Furthermore, evidence indicates that COVID-19 physical distancing measures could cause mental disorders including distress, anxiety, depression, and suicidal behaviors [ 27 , 28 , 29 ]. This might be especially true among vulnerable populations—older adults, domestic violence victims, racial/sexual minorities, and other underserved communities were among those who have been shouldering the most pronounced adverse impacts across the pandemic [ 30 , 31 , 32 , 33 ].

Take nursing home residents, for instance. A key characteristic of nursing home residents is that they have either lost or are losing their abilities to take care of themselves, a situation that is particularly pronounced among those who suffer from cognitive impairments such as dementia [ 31 ]. Amid COVID-19, many nursing home residents were found to have been left for days without access to care, food, or water, let alone basic medicines, and many of them died during the abandonment [ 34 ]. While nursing homes are often plagued with various issues [ 35 , 36 , 37 ], elder abuse and neglect have rarely been this glaring prior to the pandemic [ 38 , 39 ]. One way to address these unintended consequences is via addressing their root cause—rather than scrambling to construct piecemeal policies at the eleventh hour, rigorously and pre-emptively developing policies, such as via evidence-based policy-making processes, may hold the key [ 40 ].

Evidence-based policy making can be understood as the law-making process that is guided by and developed on the basis of evidence [ 41 ]. A rich body of evidence suggests that evidence-based policy making can provide considerable benefits to society at large [ 42 ]. However, it is important to note that evidence-based policy making is not without flaws [ 43 , 44 , 45 , 46 ], many of which have either been highlighted or magnified amid the pandemic [ 47 , 48 ]. Conventional policy making often follows a range of one-directional steps, including agenda setting, policy formulation, policy adoption and application, and policy evaluation [ 49 ]. This means that in order for the resultant policies to be evidence-based, reflective of people’s needs, and have the potential to yield positive outcomes, the policy-making process is often thoroughly planned, detail-rich, time-consuming, and resource-dependent [ 50 ]—parameters that most of the pandemic-era policy-making might not be able to meet.

In other words, the unprecedented nature of the pandemic has effectively deprived policy makers of the time and planning needed to develop most conventional policies pre-emptively, let alone evidence-based ones that might be even more resource-demanding. Second, the fast-evolving characteristics of the pandemic led to the inevitability that, most, if not all, policies developed based on the conventional stage-oriented policy-making procedures would significantly lag reality. As seen amid the pandemic, “facts” and “truisms”, such as “evidence-based” predictions that claim that summer 2021 is when the pandemic would end, might sound naïve, if not juvenile, in light of the Delta-disturbed reality [ 51 ]. This means that policies that are developed on old evidence, even if it is one month old, may offer little to no utility to society at large. Third, due to a lack of clear understanding of and consensus on what could be classified as “evidence” [ 52 ], as seen amid COVID-19, oftentimes even anecdotal stories and personal opinions, if not gut feelings, have been enlisted as the “evidence” upon which policy makers alike based their pandemic policies [ 53 ].

These drawbacks, in turn, could significantly compromise public health policies’ abilities to produce much-needed positive effects on society with limited to no unintended consequences. In other words, the conventional evidence-based policy-making processes may not be able to develop policies amid COVID-19 that could:

yield desirable outcomes;

produce little to no unintended consequences in light of the unique challenges of the pandemic. However, there is a dearth of insights available in the literature that could address the above-mentioned issues. Thus, to bridge the research gap, this study aimed to identify policy-making processes that have the potential to develop policies that could induce optimal desirable outcomes with limited to no unintended consequences amid the pandemic and beyond.

A literature review was conducted in PubMed, PsycINFO, and Scopus to identify rigorous policy-making processes that could develop competent policies with the potential of producing desirable outcomes and curbing unintended consequences amid the unique challenges of the COVID-19 pandemic. Overall, the research question raised in the study had three interconnected components: rigorous policy-making processes that could (1) produce desirable pandemic prevention outcomes, with (2) limited to no unintended consequences, in light of the (3) unique challenges of COVID-19. In this study, desirable pandemic prevention outcomes can be understood as reduced COVID-19 infections, hospitalizations, and deaths. Whereas “adverse unintended consequences” and “unintended consequences” are used interchangeably, referring to negative policy outcomes that were different from expected results.

The search was developed based on two overarching concepts: COVID-19 and policy making. An example PubMed search term can be found in Table 1 . All records reviewed were published in English. To effectively address this three-pronged research aim, the review strategy was developed based on three themes:

Example PubMed search strings.

unique characteristics of COVID-19;

rigorous policy-making processes;

intended and unintended policy outcomes.

A set of eligibility criteria was adopted to screen the papers. Overall, articles were excluded if they:

did not focus on COVID-19;

did not center on the pandemic policy-making process;

did not provide insights into approaches that could either improve intended outcomes or avoid unintended consequences.

To ensure up-to-date insights were included in the analysis, validated news reports were also reviewed. Furthermore, Google Scholar alerts were set up so that relevant and most updated insights could be reviewed and analyzed to further shed light on the research question. The initial search was first conducted on 8 August 2021, with the subsequent one conducted on 15 October 2021, to include updated insights in the review.

3. Theoretical Underpinning

To better guide the review process and the subsequent analysis, theoretical insights from behavioral sciences were adopted as the guiding framework. Specifically, the theoretical underpinning of the study was grounded in the extensively documented understanding that behaviors could be both rational and irrational, as seen in the well-debated strengths and weaknesses of value-expectancy theories such as the Theory of Planned Behavior [ 54 , 55 , 56 ], for instance. In other words, the study investigated the research question via an empirically based understanding that, regardless of the scale and scope of the impacts of the actions, the policy-making process can be both rational and irrational. Furthermore, drawing insights from the Social Ecological Model [ 57 ], which posits that social behaviors are often shaped by a multitude of factors with divergent strengthens of influences that often manifest on varied levels of society, the study adopted a solution-focused mindset to address the research question—with difficulties galore, what can be done to improve the efficacy of pandemic policy making with substantially limited or eliminated unintended consequences?

In terms of peer-reviewed research, a total of 28 papers were included in the final review (see Table 2 ). The findings of the review were organized in accordance with the research aim—identify rigorous policy-making processes that could produce positive outcomes with limited to no unintended consequences in light of the unique challenges and opportunities of the COVID-19 pandemic. It is important to underscore that only a limited number of studies have investigated COVID-19 policies from a procedural perspective (e.g., [ 58 , 59 , 60 ]). In other words, instead of examining COVID-19 policies from a connected and comprehensive perspective, most of the research has focused on nuanced aspects of COVID-19 policy making, ranging from concrete facilitators (e.g., more effective prediction or monitoring of virus spread) and tangible barriers (e.g., lack of quality data), to the promises of advanced technology-enabled decision aids (e.g., AI-based decision models) (e.g., [ 61 , 62 , 63 , 64 ]) that could either hinder the smoothness or success of the policy-making process. However, while these insights could not answer the research question directly, they nonetheless were important and could be useful to tackle the research aim.

List of articles included in the final review.

Therefore, in light of the novelty of the research question and the dearth of research insights available in the literature, all relevant insights were thoroughly reviewed and analyzed. Overall, based on the literature review and the subsequent analysis, the result suggests that policy-making processes incorporating the following strategies could develop policies that have the potential of yielding desirable outcomes with limited unintended consequences:

people-centered: put people’s needs and wants at the center of the policy-making process, effectively prioritizing people over profits, politics, and the like [ 58 , 76 , 88 , 89 , 90 , 91 , 92 , 93 ];

artificial intelligence (AI)-powered: incorporating intelligent and automatic decision-making mechanisms to ensure the policies are developed based on the most updated evidence [ 83 , 94 , 95 , 96 , 97 , 98 ];

data-driven: the need to anchor key policy-making decisions with the support of empirical insights from quality data of optimal quantity and diversity [ 61 , 62 , 63 , 64 , 99 , 100 , 101 , 102 ];

supervision-enhanced: oversight mechanisms that scrutinize the behaviors of both the policy makers and the AI systems to further enhance policies’ abilities to produce positive outcomes without incurring unintended consequences [ 69 , 103 , 104 , 105 , 106 , 107 , 108 ].

To leverage these strategies’ interconnectedness, the people-centered, AI-powered, data-driven, and supervision-enhanced (PADS) model of policy making was subsequently developed. In the following section, the PADS model will be discussed in detail.

5. Discussion

This study aims to identify policy-making processes that have the potential to develop policies that could induce optimal desirable outcomes with limited to no unintended consequences amid COVID-19 and beyond. This is one of the first studies that investigated solutions that could shed light on the bevy of policy-making issues the COVID-19 pandemic has introduced or intensified, ranging from opaque and questionable policy-making processes and unquestioned and unchecked power of policymakers, to the unprecedented pace seen in the erosion of health equity and implosion of public dissent partially caused by unintended consequences of COVID-19 policies [ 109 , 110 , 111 ]. Aiming to address key issues in current policy-making practices—poor adoption of rigorous data analytics, lack of accountability, and oversized dependence on individual decision makers or policy makers, the study identified strategies that could establish and sustain the rigor in COVID-19 policy-making processes—the people-centered, AI-powered, data-driven, and supervision-enhanced (PADS) model of policy-making.

5.1. People-Centered

People-centered means to put people’s needs and wants at the center of the policy-making process, effectively prioritizing people over profits, politics, and the like [ 58 , 76 , 88 , 89 , 90 , 91 , 92 , 93 ]. It is important to note that “people” refers to all key stakeholders that are involved in the policy-making process, ranging from decision makers such as policy makers, decision supervisors such as independent experts, and decision benefactors such as the general public. Overall, it is important to underscore that the degree to which people agree with policies is a critical factor in shaping COVID-19 containment outcomes [ 112 , 113 ]. As the literature shows, how individuals adopt and comply with public policies, whether due to belief in science [ 114 ], economic concerns [ 115 ], political ideology [ 116 ], or perceived people-friendliness of the public policies (e.g., duration of the lockdown) [ 117 ], may influence the effectiveness of these policies in controlling the spread of COVID-19. In other words, public health policies, such as lockdowns, self-isolation, and spatial distancing measures are only effective if the public acts willingly in accordance with these measures [ 112 , 113 , 114 , 115 , 116 , 117 ].

By prioritizing the people’s collective interests over individual profits, partisan politics, or the dominant powers at the moment, the people-centeredness of the policy-making process or the PADS model could not only safeguard personal and public health, but also prompt better adherence to the resultant COVID-19 policies. Take China’s zero-COVID policy, for instance. The zero-COVID policy is a unique disease elimination/eradiation policy that has two pillars:

a “zero-tolerance” mindset that treats even single-digit positive COVID-19 cases or small disease outbreaks with the utmost urgency;

a “zero-delay” action plan that employs and deploys robust and rigorous collective and corroborative actions and measures to subdue positive cases and squash potential outbreaks.

Understandably, the zero-COVID policy and its use of mass quarantines and lockdowns are often considered draconian [ 118 ], particularly in light of the ever-loosening pandemic measures adopted by other societies [ 119 , 120 ]. However, as the policy is people-centered—developed factoring in the needs of all members of the society, including vulnerable communities such as older adults, frontline workers, and volunteers [ 67 , 121 , 122 ], and possibly future short-term residents such as participants of the Beijing 2022 Winter Olympic Games [ 123 ]—the zero-COVID policy remains well supported and rigorously followed by the public [ 93 ].

5.2. AI-Powered

AI can be understood as machine programs or algorithms that are “able to mimic human intelligence” [ 124 ]. The AI-powered component of the PADS model emphasizes the importance of incorporating intelligent and automatic decision-making mechanisms to ensure the policies are developed based on the most updated and comprehensive evidence robustly analyzed [ 83 , 94 , 95 , 96 , 97 , 98 ]. Advanced AI systems can help policymakers to make more informed policies that are both reactive (retrospectively analyzing data to develop intelligent solutions) and proactive (predictive decision-making insights based on advanced modelling) in nature [ 125 , 126 , 127 ]. Furthermore, AI systems can often serve as the essential platform that enables other advanced technologies, ranging from augmented reality and virtual reality to mixed reality, if not the metaverse. In addition to AI’s role as the enabler, it can also perform the function of enhancer—improving performance of everyday services or commonplace information and communication technologies [ 125 , 126 , 127 ].

For example, AI-based systems could help government and health officers develop algorithms that incorporate in-depth and comprehensive insights gained on big data analysis on diverse data in the policy-making process, ranging from search queries, medical records, public health records, social media posts, online purchases, and wastewater to surveillance footage [ 83 , 124 ]. The potential of AI systems can be further amplified when coupled with 5G or 6G technologies; 6G, the sixth-generation networking technologies, can be understood as the next-generation transmission technique following the 5G communication strategies [ 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 ] with enhanced key performance indicators (KPIs) and a wider range of real-world applications. Both 5G and 6G technologies can offer substantially greater computing powers to further improve an AI system’s abilities to generate empirical-based intelligence [ 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 ]. Research shows that, for instance, analyzing social media posts can offer a grounded and timely insight into citizens’ needs and wants, as well as concerns and considerations in times of crisis such as the COVID pandemic [ 136 , 137 , 138 ]. Emerging insights also suggest that even small local governments in the U.S. have integrated social media platforms, such as Facebook and Twitter, into their government functions [ 139 ], aiming to proactively incorporate public participation in the policy-making process.

5.3. Data-Driven

Data-driven entails the need to anchor key policy-making decisions upon the support of empirical evidence abstracted from quality data of great quantity and diversity [ 99 , 100 , 101 , 102 ]. Data-driven can refer to either big data analytics or data analyses based on smaller-scale databases. The importance of the data-driven element in the PADS model centers on the use and application of empirically gained insights, as opposed to subjective ideas, in the policy-making process. It is important to underscore that, thanks to advanced technologies such as 5G/6G and AI, a bevy of multifaceted information about public needs and preferences can be cost-effectively monitored, ranging from search queries, social media posts, and sewage data to medical records [ 83 ]. Having a diverse pool of heterogenous data paired with advanced computing powers provided by 5G/6G technologies and competent analytical skills enabled by AI means that government and health officials can gain a more complete and comprehensive understanding of the public’s perspective and sentiments towards key policy issues.

Data are, essentially, information about people. Depending on how the data were collected, they could either shed light on information on the people from a third-person perspective (e.g., surveillance footage), relevant information provided by the people via the lens of first-person perspective (e.g., digital diary), or information that is less reflective of differences in perspectives or transitory changes (e.g., biomedical data) [ 140 ]. In other words, the data-driven strategy could ensure that both the policy-making process and the resultant policies are founded on and reflective of the collective willpower from diverse perspectives. Overall, incorporating empirical evidence in the design, development, and delivery of policies to ensure the specific rules and regulations are in line with the general public’s needs and wants can be understood as a novel approach to public participation in policy making. Public participation can be understood as the involvement of the public in the government’s agenda-setting and decision-making processes [ 141 ]. Essentially, by incorporating big data about people, and oftentimes from people, the data-driven policy-making process constitutes a novel way of ensuring that the individual circumstances are sufficiently heard, considered, and reflected in the public policies, without demanding people’s physical presence in the policy-making process.

5.4. Supervision-Enhanced

To err is but human, and artificial intelligence is but a human creation. Noticeably, AI is intrinsically flawed in terms of its lack of ability to initiate ethical considerations and moral judgments [ 142 , 143 ]. In other words, regardless of how remarkable the AI-powered data analytical system might become, in light of the inherent flaws of AI systems—intelligent but without consciousness (e.g., ethical and moral considerations) [ 144 , 145 , 146 ]—it is essential to safeguard AI systems with instrumental human involvement, in the forms of both policy making by government and health officers and rigorous supervision by independent experts [ 106 ]. In other words, to effectively prevent AI from “augmenting disparities” [ 103 ] and fostering its abilities to address inequalities or accelerate integrity, sufficient supervision is needed.

Supervision can be understood as oversight mechanisms that scrutinize the behaviors of both the policy makers and the AI systems to further enhance the policies’ abilities to produce positive outcomes without incurring unintended consequences [ 69 , 103 , 104 , 105 , 106 , 107 , 108 ]. By rigorously leveraging the supervision-enhanced strategy, the PADS model could help society at large better limit or eliminate potential unintended consequences that could emerge in the policy development, deployment, or delivery processes. One way to form the supervision system is via incorporating an independent review board with rigorously vetted experts participating in the review board on a rotating basis. Other approaches, such as global collaboration [ 60 , 147 ], potentially paired with expertise from international health organizations such as the World Health Organization, may also work. Overall, in light of the multifaceted nature of the concept of “unintended consequences”, it is important to note that, while the presence and robustness of the supervision system are of utmost significance, having an “expert-review-needed” or supervision-needed mindset among policy makers is of equal importance.

One way to view unintended consequences is that they could either be a result of unplanned or unforeseen policy planning—”unplanned” refers to situations in which the negative outcomes are unintended but nonetheless not unanticipated [ 148 ], whereas “unforeseen” refers to scenarios in which policy makers were completely unaware of the potential unintended consequences. In other words, not all unintended consequences denote innocence and ignorance on the part of policy makers’—some policies might be made as a result of balancing pros and cons, which means that the welfare of some members of the society could be arbitrarily ignored or neglected during the policy-making process. These flaws could be reflected in AI systems as well [ 103 ], which could further compound the potential unintended consequences caused by the policies. A “supervision-needed” mindset could be the solution:

it could facilitate the establishment of policy-making practices that value the importance of supervision;

it could help policy makers avoid causing “unforeseen” consequences in the policy-making process;

it could help policy makers incorporate moral and ethical considerations, ranging from fairness, equality, and privacy to security concerns, in the policy-making process.

5.5. The Advantages of the PADS Model

In line with the principle of parsimony [ 149 ], the policy-making process could be simplified into two collaborative and non-collaborative processes [ 60 , 150 ]. A non-collaborative policy-making process often only involves policy makers. In other words, stakeholders’ input or feedback is often not involved in the process. On the other hand, the collaborative policy-making process not only involves the policy makers, but also stakeholders as well. As the process of policy making evolves, the degree of stakeholder involvement differs across contexts. However, regardless of how the collaboration takes place, this collaborative policy-making process nonetheless suffers from a key flaw—oftentimes both the policymakers and the stakeholders’ input are subjective. A schematic representation of these two policy-making approaches can be found in Figure 1 .

A schematic representation of noncollaborative and collaborative policy-making processes.

Essentially, the leap from noncollaborative policy-making processes to collaborative policy-making processes only addresses one issue in the practice—the lack of public involvement in the decision-making process. In other words, though policies produced via the collaborative policy-making process might have greater abilities to address people’s needs and wants, they nonetheless could be flawed due to the highly subjective nature of the data upon which they are developed. One way to further improve the collaborative policy-making process is via replacing highly subjective and cross-sectional physical public participation with accumulated data that capture both the subjective and the objective needs and preferences of the stakeholders. In other words, data from the stakeholders (e.g., surveys), combined with data about the stakeholders (e.g., third-person perspective data such as surveillance footage, internet activities, etc.) and data about the overall situation from a multitude of perspectives, could serve as a considerably improved virtual proxy of public participation.

As evidence suggests, the general public may be well justified regarding whether or to what degree they wish to comply with COVID-19 public policies [ 112 , 113 , 114 , 115 , 116 , 117 ]. It is also worth noting that many, if not all, of the COVID-19 public policies were developed based on a top-down approach [ 151 , 152 ], and often without following the proper procedures that allow public participation in the policy-making process [ 153 , 154 ]. Though oftentimes public policy is held as a belief by some governments that “the governments decide to do or not to do” [ 155 ], as seen from COVID-19, for the greater good (e.g., achieve a post-pandemic reality), it should be considered and treated as a people-centered ecosystem that aims to serve the general public needs and preferences.

In other words, the data-driven component of PADS can effectively address issues that have been long plaguing policy-making: cross-sectional surveys about people’s needs and preferences are often flawed in offering stable and definitive insights about people, and longitudinal studies are often resource-dependent to conduct or limited in their abilities to provide timely insights into the subject matter. These insights combined suggest that the data-driven characteristics of the PADS model also share advantages that are commonly seen in general public participation in policy-making. It could:

better capture and comprehend the public’s needs and preferences;

design and develop public policies that are grounded in reality and people-centric; and in turn;

yield more desirable policy outcomes and limit potential unintended consequences [ 141 , 156 , 157 , 158 , 159 ].

An example of applying the PADS model for developing policies on the use and application of 5G and AI technologies in the context of aging-in-place can be found in Figure 2 . Overall, Figure 2 illustrates how the people-centeredness of the PADS model respects and reflects key stakeholders’ needs and preferences in the policy-making process, with the aid of advanced technologies such as AI-powered systems and comprehensive supervision mechanisms.

An example utilization of the PADS model in the context of aging-in-place policies.

5.6. Limitations

While this study bridged important research gaps, it was not without limitations. For starters, the review only focused on relevant articles published in the context of the COVID-19 pandemic. This means that potentially valuable insights that were not COVID-19-specific were not included in the review. Due to the focus of the study, challenges such as developmental hurdles associated with the use and application of AI were not discussed in detail in the study. Furthermore, due to the conceptual nature of the PADS model, no empirical evidence about its real-world efficacy is available at the moment. While the efficacies of public health policies could be difficult to evaluate [ 46 ], future studies could nonetheless explore innovative approaches to gauge the effectiveness of the PADS model in generating promising policies.

6. Conclusions

Policies can be the defining factor in shaping personal and public health, especially amid global catastrophes such as COVID-19. Amid the ever-increasingly chaotic jungle of COVID-19 policy making and the rapidly intensifying public expectations of greater accountabilities among policymakers, it is then vital to investigate rigorous policy-making strategies that could help societies at large develop more cost-effective COVID-19 policies. Based on insights gained from reviewing and analyzing the state-of-the-art evidence in the literature, this study developed the PADS model, which proposes a people-centered, AI-powered, data-driven, and supervision-enhanced approach towards policy making amid COVID-19. The PADS model can develop policies that have the potential to induce optimal outcomes and limit or eliminate unintended consequences amid COVID-19 and beyond. Rather than serving as a definitive answer to problematic COVID-19 policy-making practices, the PADS model can be best understood as one of many promising frameworks that could bring the pandemic policy-making process more in line with the interests of societies at large; in other words, more cost-effectively, and consistently anti-COVID and pro-human.

Acknowledgments

The author wishes to express her gratitude to the editor and reviewers for their constructive input and insightful feedback.

Abbreviations

This research received no external funding.

Conflicts of Interest

The author declares no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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• Published: 01 May 2023

What are the core concerns of policy analysis? A multidisciplinary investigation based on in-depth bibliometric analysis

• Yuxue Yang   ORCID: orcid.org/0000-0002-8772-1024 1 , 2 ,
• Xuejiao Tan 1 ,
• Yafei Shi 1 &
• Jun Deng 1 , 2  

Humanities and Social Sciences Communications volume  10 , Article number:  190 ( 2023 ) Cite this article

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• Environmental studies
• Medical humanities
• Social policy

Policy analysis provides multiple methods and tools for generating and transforming policy-relevant information and supporting policy evolution to address emerging social problems. In this study, a bibliometric analysis of a large number of studies on historical policy analysis was performed to provide a comprehensive understanding of the distribution and evolution of policy problems in different fields among countries. The analysis indicates that policy analysis has been a great concern for scholars in recent two decades, and is involved in multiple disciplines, among which the dominant ones are medicine, environment, energy and economy. The major concerns of policy analysts and scholars are human health needs, environmental pressures, energy consumption caused by economic growth and urbanization, and the resulting demand for sustainable development. The multidisciplinary dialog implies the complicated real-world social problems that calls for more endeavors to develop a harmonious society. A global profiling for policy analysis demonstrates that the central policy problems and the corresponding options align with national development, for example, developing countries represented by China are faced with greater environmental pressures after experiencing extensive economic growth, while developed countries such as the USA and the UK pay more attention to the social issues of health and economic transformation. Exploring the differences in policy priorities among countries can provide a new inspiration for further dialog and cooperation on the development of the international community in the future.

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Introduction.

Social problems are evolving with the rapid development of economy, and the problems mankind is facing and options they choose reflect the developmental demand. Policy is a political action with specific subjects, targets, and strategies in a certain period of time, which primarily aims to create a healthy environment for the development of society (Porter, 1998 ; Lasswell and Kaplan, 1950 ; Yang et al., 2020 ). As for policy analysis, the definition varies a lot. According to William Dunn ( 2015 ), policy analysis is ‘an applied social science discipline, which uses multiple methods of inquiry and argument to produce and transform policy-relevant information that may be utilized in political settings to resolve policy problems.’ Jabal et al. ( 2019 ) defined that policy analysis provides methods and tools for assessing whether a policy is ‘correct and fit for their use’ and supporting policy evolution. Manski ( 2019 ) regarded policy analysis as a shorthand term that describes the process of scientific evaluation for the impact of past public policies and prediction of the potential outcomes of future policies. More generically, policy analysis is aimed to understand who develops and implements certain policies, for whom, by what, with what effects, and what techniques and tools can be used, and so on (Blackmore and Lauder, 2005 ; Collins, 2005 ).

Accordingly, regarding the typology of policy analysis, three categories can be established based on ontology and epistemology (Fig. 1 ) (Bacchi, 1999 ; Colebatch, 2006 ; Jennifer et al., 2018 ): (1) Positivism paradigm. Focusing on policy facts, this orientation of policy analysis aims to identify policy problems and weighting the optimal solution guided by the theory of economic frameworks, basic scientific models, and behavioral psychology through objective analysis. Economic analysis, cost-benefit analysis, quantitative modeling and nudge politics are the most commonly used methods in this orientation (Althaus et al., 2013 ; Jennifer et al., 2018 ); (2) Constructivism paradigm. In this orientation, policy is conceptualized as ‘the interaction of values, interests and resources guided through institutions and mediated through politics’ (Davis et al., 1993 ) rather than a comprehensively rational and linear process in which analysis involves policy agenda setting, policy processes, policy networks and governance, mainly focusing on values, actors and political rationality of policy. Theoretical frameworks, such as multiple stream theory, behavioral psychology and advocacy coalition framework, etc. are typically used in such orientation (Kingdon, 1984 ; Browne et al., 2019 ; Sabatier and Weible, 2014 ); (3) Interpretivism paradigm. This orientation is focused on interpreting how policy problems can be defined or constructed and how the problem framing shapes the possible policy responses (Bardach, 2000 ). A substantial body of research has discussed the theory underlying the problem, framing and governmentality using narrative analysis, discourse analysis, ethnographic methods, etc. (Hajer, 1995 ; Hajer, 2006 ; Martson and Mcdonald, 2006 ). Therefore, a systematic review of policy analysis can present the past and present policy problems of concern and the relevant possible options from an evolutionary perspective.

The framework was organized according to Jennifer et al. ( 2018 ).

The profoundly complex and diversified realistic demands such as equity and sustainability (Akadiri et al., 2020 ), the changes of energy planning (Banerjee et al., 2000 ; Pandey et al., 2000 ; Pandey, 2002 ) and transition of modern markets (Blackman and Wu, 1999 ) have important implication on policy decisions (Munda, 2004 ). A multidisciplinary investigation on policy analysis can provide more reflections on how to develop a harmonious society. Studies have shown that the priority of policy agenda is determined by three key factors: the nature of the issue (Shiffman and Smith, 2007 ), the policy environment (Adams and Judd, 2016 ; Sweileh, 2021 ) and the capabilities of proponents (Shawar and Shiffman, 2017 ). Due to differences in geography, economics, politics and many other aspects, social concerns and policy priorities vary enormously in different countries. In the global context, how countries set policy priorities in different stages of development, and how policy priorities align with the national development remain unknown. So, developing a global profiling for policy analysis can present the differences in core concerns of polices among countries, thus promoting further dialog and cooperation on the development of the international community in the future.

Bibliometric analysis has long been used as a statistical tool to systematically review scientific literature (Hood and Concepcion, 2001 ). A rigorous bibliometric analysis can provide systematic insights into previous publications, which can not only delve into the academic research community of active and influential researchers, but also identify the current research topics, and further explore potential directions for future research (Fahimnia et al., 2015 ). Bibliometrics has been widely applied in a wide range of sectors and specific domains, for example, mapping and visualizing the knowledge progress avenues and research collaboration patterns of cultural heritage (Vlase and Lähdesmäki, 2023 ), analyzing the sub-areas and core aspects of disease (Baskaran et al., 2021 ), visualizing and graphing the evolution of research related to sustainable development goals (Belmonte-Ureña et al., 2021 ), and studying policies, such as agricultural policy (Fusco, 2021 ), medical information policy (Yuxi et al., 2018 ), and science, technology and innovation policy (Zhang et al., 2016 ). However, the research trajectory and focus of policy analysis around the world remain a black box. In the present paper, a bibliometric analysis was performed from three dimensions: time, intensity, and scope, which referred to hot point changes over time, the quantity of research and the core concerns of policy, respectively.

In the present paper, a bibliometric analysis of a large number of studies on historical policy analysis was performed to answer the questions: (1) What core concerns are reflected in the policy analysis and how does these core concerns reflect real-world social problems? (2) How do these core concerns change over time? (3) What are the differences in core concerns among countries and what drives those differences? From an evolutionary perspective, this paper aims to uncover the past and present policy problems of concern and the relevant possible options, thus providing a clue for future policy analysis. The analysis of the evolution and differences in policy problems among countries may provide a view of the development context of different countries and put forward new inspiration and hope for further dialog and cooperation on the development of the international community in the future. Furthermore, another possible key sustainability implication with respect to the core concerns of policy analysis is to provide a reference for exploring the gaps between academic research and policy agenda.

Literature research

In the present study, Web of Science (WOS) Core Collection database was used for data retrieval (Vlase and Lähdesmäki, 2023 ). This research was conducted in four steps. Firstly, articles related to policy analysis were searched to select the most cited ones, which reflect the most influential research and the cutting-edge knowledge over time. MerigÓ et al. ( 2016 ) and Markard et al. ( 2012 ) weighted the most citation in an absolute term that means the total citations of all time. According to Fusco ( 2021 ) and Essential Science Indicators, the most citation was weighted in a relative term, which means the citation number in the publication year. The top 1% papers, compared to other articles in the academic field published in the same publication year, were included in this study following the refining principle of Essential Science Indicators, ensuring that the impact of these articles does not fade with time. Secondly, the selected papers were further screened, and narrowed down to different collected datasets for in-depth analysis according to the results of screening. Thirdly, statistical analysis and network visualization of authorship, organization and geographical distribution, topics and their chronological trends in each dataset were performed using VOSviewer software, which is freely available to construct and visualize bibliometric network (see www.vosviewer.com ) (Van-Eck and Waltman, 2010 ). Lastly, the association between policy analysis and academic articles was explored in different fields.

Dataset construction

Originally, a total of 118,535 articles related to policy analysis were retrieved using the strategy “TS = (policy analysis)”. For further discipline analysis, the most cited articles were selected with the quick filtering toolbar of WOS. Consequently, 1287 most cited papers of policy analysis were included in dataset 1. Then co-citation analysis of journals was performed to provide clues for discipline research (Supplementary Table 2 ). Accordingly, policy analysis-related articles from journals in the medicine field were selected for dataset 2, and 7963 articles were finally included. Similarly, 15,705 articles from journals in the field of environment were included in dataset 3; 6253 articles from journals in the field of energy in dataset 4; 1268 articles from journals in the field of economy in dataset 5; and 2243 articles from multidisciplinary journals in dataset 6. According to Journal Citation Reports of WOS, multidisciplinary journals refer to those journals in which articles involve at least two disciplines, such as Ecological Economics that involves ecology and economics. The search strategy of each database is shown in Table 1 .

Network visualization

Publication information of policy analysis was presented, including publication number, countries and organizations of key players, which reflects the value of and actual needs for policy analysis. Then, VOSviewer was used for network visualization of co-authorship, co-occurrence and citation. Co-authorship analysis for organizations and countries, which met the thresholds identified more than 5 articles for further investigation of the key players’ geographical distributions and their collaboration patterns. Co-occurrence analysis for all keywords based on the frequency of keywords used in the same article was carried out for topic mining (Kern et al., 2019 ). Citation analysis was performed to investigate the citation attributes received by other items. Meaningless or common terms were removed (Zhang and Porter, 2021 ). The research framework is shown in Fig. 2 .

The research framework for multidisciplinary investigation in policy analysis.

Publication information of policy analysis

Firstly, the publication number of policy analysis was determined. A total of 118,535 policy analysis articles were published between 2003 and 2021 (Fig. 3 ), showing a surge in the development of policy analysis with an exponential growth rate of 53.98 and 84.03% in the last 5 years (2017–2021) and 10 years (2012–2021), respectively.

Source : Data was collected from Web of Science (WOS) Core Collection database on the topic (TS) “policy analysis”.

For network construction, 1287 most cited papers were screened. The collaboration network of countries was visualized and illustrated, showing that 112 countries have published the most cited policy analysis articles. As for the co-authorship of countries and organizations, 2286 universities were identified, and 193 of them from 59 countries met the criteria of network analysis, among which the universities from the USA (University of Washington, Harvard University), the UK (University of Oxford, University of Cambridge) and China (University of Chinese Academy of Sciences) had the largest number of links and the strongest willingness to cooperate with other organizations (Fig. 4A, B and Supplementary Table 1 ). The willingness of cooperation not only meets the needs of academic research, but also conforms to the general expectations of the international community. Citation analysis for sources identified 51 journals from five different fields (Fig. 4C and Supplementary Table 2 ), in which environment-related journals accounted for the largest number (e.g., Journal of Cleaner Production, Science of The Total Environment , Global Environmental Change-Human and Policy Dimensions , Transportation Research Part D: Transport and Environment and Environmental Modeling & Software) , followed by medicine-related journals ( The Lancet , JAMA , The Lancet Infectious Diseases , PLOS One and The Lancet Global Health) , the journals of energy science ( Sustainable Cities and Society , Energy Policy , Applied Energy , Renewable Energy and Energy ), the journals of economy ( International Journal of Production Economics and Transportation Research Part A: Policy and Practice ), and then several multidisciplinary journals ( Ecological Economics , Nature , PNAS, Nature Communications and European Journal of Operational Research ).

A Co-authorship analysis for countries; B Co-authorship analysis for organizations; C Citation network; D Co-occurrence network.

In the co-word network of policy analysis, four main clusters were displayed: the blue cluster concerned with environmental policy problems; the green cluster related to medicine (e.g., public health, prevalence and mortality of disease); the red cluster centering policy, such as policy framework, policy systems, and policy implementation; and the yellow cluster mainly concerned with energy (e.g., energy consumption, energy efficiency and electricity generation) (Fig. 4D and Table 2 ). Simultaneously, more details related to real-world social issues were also found, such as the common and core concerns about carbon emission, economic growth, prevalence and mortality of disease. Additionally, management is in the spotlight (e.g., system, framework, efficiency and challenge).

Publication information of policy analysis in different fields

Policy analysis-related articles mainly involved the fields of medicine, environment, energy, economy and multidiscipline. The publication information in different fields was investigated. First, the volume growth trend over time was traced. Generally, a growing number of articles were published annually. The most obvious growth was found in policy analysis in environment, followed by medicine and energy, and the growth in economy and multidiscipline was relatively stable (Fig. 5 ). Specifically, the first increase in the publication number of policy analysis in medicine was seen in 2009, and then a steady growth was maintained, followed by a second acceleration after 2019, which may relate to the pandemic of H1N1 influenza and COVID-19, respectively (WHO, 2012 ; Wouters et al., 2021 ). A great growth in environmental policy analysis was observed after 2015, and a linear growth after 2017. In energy policy analysis, the first increase occurred in 2009, reaching a peak in 2013, followed by a second increase in 2016, reaching another peak in 2020. Then the publication information about organizations and countries was explored. The top five countries and institutions with the largest number of policy analysis articles in different fields are presented in Supplementary Table 3 . The results showed that the USA, the UK and China attached great importance to policy analysis in all of these fields.

Publication dynamics of policy analysis-related articles in the fields of medicine, environment, energy, economy and multidiscipline between 2003 and 2021.

Policy analysis in the field of medicine

A total of 8381 organizations from 177 countries contributed to medical policy analysis. Further investigation showed that universities from the UK (e.g., University of London, London School of Hygiene & Tropical Medicine and University College London), the USA (e.g., Harvard University and University of California San Francisco), Canada (e.g., University of Toronto) and Australia (e.g., University of Melbourne, University of Sydney) contributed the most to medical policy analysis with the greatest willingness to collaborate both domestically and internationally. By contrast, Chinese universities, such as Peking University, University of Chinese Academy of Sciences and Zhejiang University, were more prone to domestic collaboration (Fig. 6A, B ).

A Co-authorship analysis for countries; B Co-authorship analysis for organizations; C Co-occurrence network; D Overlay network.

Co-occurrence analysis of keywords showed that of the 16,719 keywords identified from 7963 retrieved items, 1778 keywords met the threshold. In addition to the three core topics “medicine”, “policy” and “health” (e.g. health policy, public health), the mortality, prevalence, risk factors as well as prevention of diseases have been the key focus of medical policies. Additionally, the issues of children and adolescents, such as physical activity, overweight and childhood obesity, have also attracted medical scientists and policy analysts. Figure 6D shows the average annual overlay network of keywords. The most recent concerns are the prevalence of COVID-19 and relevant topics associated with SARS-CoV-2 and coronavirus. Moreover, sex-specific mortality, life satisfaction and affordable care act are also the hot topics in recent years (Fig. 6C, D ).

Policy analysis in the field of environment

Co-authorship analysis showed that 9060 organizations from 160 countries contributed to environmental policy analysis, among which universities from China played a key role, especially University of Chinese Academy of Sciences, Tsinghua University, Beijing Normal University, North China Electric Power University and Beijing Institute of Technology (Fig. 7A, B and Supplementary Table 3 ). Of the 44,213 keywords in retrieved 1 5705 articles related to environmental policy analysis, 3638 met the threshold of keyword co-occurrence analysis. The co-word network showed that apart from the words with vague meanings such as “policy”, “impact” and “management”, “carbon emission”, “climate change” and “sustainability” were the most visible in the network. Note that the terms like “energy”, “economic growth” and “urbanization” were also easy to notice (Fig. 7C ). The analysis for the average annual overlay showed that “kyoto protocol”, “acid deposition” and “policy development”, etc. were earlier terms, while “plastic pollution”, “Cross-Sectionally Augmented Autoregressive Distributed Lag” and “population structure”, though lightly weighted, were the most recent ones. The color of overlay network visualization of environmental policy analysis appeared to be yellow, indicating that environmental problems have attracted researchers all over the world in past decades (Fig. 7D ). The abovementioned results demonstrated the positive attitude of policy analysts and indicated a shift of their attention over time, possibly due to the evolution of environmental problems.

Policy analysis in the field of energy

The collaboration network showed that 3668 organizations from 117 countries performed policy analysis in energy. The top five organizations were Tsinghua University, University of Chinese Academy of Sciences, Xiamen University, North China Electric Power University and Beijing Institute of Technology, all of which showed strong willingness to collaborate both domestically and internationally. The network showed that there was complex knowledge interaction and flow in the citation of energy policy analysis (Fig. 8A, B ). Of the 15,027 keywords in retrieved 6253 articles, 1225 met the threshold. Co-occurrence network (Fig. 8C ) revealed that policy analysis in energy was primarily focused on the demand for renewable energy (such as “wind power”, “solar power”, “bioenergy”) due to emission (e.g. “carbon emission”, “greenhouse gas emission”) and energy consumption. The terms “restructuring”, “discount rates” and “kyoto protocol” were early noticed by researchers, and the analysis of kyoto protocol was performed earlier in energy than that in ecology. Then, “green power”, “green certificates” and “energy policy analysis” gradually came into the eyes of analysts. Similarly, the prevalence of COVID-19 was the greatest concern of energy policy analysts, followed by “energy communities” and “renewable energy consumption” (Fig. 8D ).

Policy analysis in the field of economy

1144 organizations from 67 countries were found to contribute almost the same to policy analysis in economy. Hong Kong Polytechnic University, Delft University of Technology, University of Leeds, Rensselaer Polytechnic Institute and University of Sydney had the largest number of publications. Hong Kong Polytechnic University, Delft University of Technology, University of British Columbia, University of Sydney and Rensselaer Polytechnic Institute had the highest collaboration (Fig. 9A, B ). Of the 5970 keywords in retrieved 1268 papers, 395 met the threshold. The co-word network showed that in addition to the general words frequently used in articles (e.g. “policy”, “impact”, “system”), the specific words reflecting the most common topics for policy problem of economy were “transport” (associated with vehicles, public transport, travel behavior, etc.), “supply chain” (related to supply chain management, supply chain coordination, green supply chain, etc.), and “inventory” (related to the model, control and system of inventory, etc.) (Fig. 9C ). The overlay network analysis showed that economic policy analysts had an early interest in inventory-related topics and the issue of supply chain management, but has been concerned with the sustainability of supply chain management only in recent years. Additionally, topics like “circular economy”, “life-cycle assessment”, “industry 4.0” and “automated vehicles” also attracted scholars’ attention. (Fig. 9D ).

Policy analysis in multidiscipline

In the co-authorship network, universities such as Stanford University, University of Chinese Academy of Sciences, University of Maryland, University of California, Berkeley and University of Cambridge had the most publications and a high collaboration. University of California Irvine had fewer publications but relatively higher link, showing that this university was strongly willing to cooperate with other organizations (Fig. 10A, B ). Of the 9467 keywords in retrieved 2243 articles, 648 met the threshold. This multidisciplinary research revealed the relationship between economy, environment and energy. However, there were obstacles to extend the relationship between them. Co-word network demonstrated that the policy analysis articles published on the multidisciplinary journals were mainly focused on the topics of “climate change”, “sustainability” and “inventory”. The term “climate change” is mainly related to issues of environmental resources (e.g., land use, deforestation, biodiversity), greenhouse gas emission (especially carbon emission) and energy consumption. The term “sustainability” is mainly connected with the relationship between environmental resources and economic growth. In addition to COVID-19, the terms “big data” and “circular economics” were on the cut edge (Fig. 10C, D ).

Policy analysis aims to understand what is the governments’ focal point, investigate why and how governments issue policies, evaluate the effects of certain policies (Browne et al., 2019 ), and reflect political agenda driven by social concerns or international trends (Kennedy et al., 2019 ). In this study, a bibliometric analysis of a large number of publications on historical policy analysis was carried out to explore the policy problems of concern and the relevant possible options from an evolutionary perspective, and provide a guide for future research. From 2003 to 2021, the number of publications on policy analysis grew exponentially. Before 2011, little attention was paid to policy analysis, but in recent decades, more importance has been attached to policy analysis around the world due to increasingly prominent social problems, especially the human health needs, degradation of environment, energy consumption and the relationship between economy, energy and environment.

From the perspective of global visibility, the policy analysis in medicine has received increasing attention from scholars from 8381 organizations of 177 countries, indicating that health problems, though not numerically dominant, have the widest coverage. Among these countries, the USA, the UK, Australia, Canada and China are the major contributors. The developed countries, such as the USA, the UK, Canada and Australia, have strongly supported addressing complex public health issues by developing effective policy responses (Moore et al., 2011 ; Atkinson et al., 2015 ). Typically, they spend the most on health, with 12318, 5387, 5905 and 5627 dollars per capital, respectively, while the developing countries spend relatively less, such as 894 dollars per capital in China and 231 dollars per capital in India (OECD, 2022 ). Great attempts have been made to analyze the burden of prevalence and mortality of diseases such as cancer, cardiovascular diseases and diabetes both globally and regionally (Yusuf et al., 2020 ; Rudd et al., 2020 ; Kearney et al., 2005 ). Other health issues of women, children and adolescents have been monitored and measured for years in many countries that respond to the Countdown to 2030 (Countdown to 2030 Collaboration, 2018 ). In addition, the worldwide outbreak of epidemics such as H1N1 influenza and COVID-19 pandemic has caused excess mortality and enormous social and economic costs all over the world, which greatly affect social policy and reveal the fragility of health systems to shocks (Wouters et al., 2021 ; Chu et al., 2020 ). By analyzing the global burden of disease, scholars have recommended policy-makers to give priority to the prevention and management of relevant diseases (Kearney et al., 2005 ).

Environmental policy analysis involving 15,705 articles has attracted largest attention from policy analysts and scientists. Greenhouse gas emission (mainly carbon emission) resulting in climate change and environmental degradation remains to be the most threatening and urgent issue, and has attracted attention of governments and the society (Tang et al., 2021 ; Ahmad et al., 2019 ). Different countries issued different climate policies aiming to reduce greenhouse gas emissions. The Kyoto protocol, ratified by 180 countries, committed to reduce the GHG emissions by 5% by 2012, compared with the 1990 emission levels (Kuosmanen et al., 2009 ). In the EU climate policy framework in 2014, the carbon emissions were projected to reduce by 40% by 2030, and by 80% by 2050 (European Council, 2014 ). The relationship between urbanization and environmental pressure was observed in the present research. During urbanization, the consumption of resources such as land, water and fuel has increased significantly, causing serious ecological pressure such as climate change, loss of biodiversity, land erosion and pollution. With the acceleration of economic growth and social commercialization, urbanization further increases the demands for housing, food, transportation, electricity and so on, which in turn aggravates the ecological pressure because of natural resource consumption, climate change, over-extraction and pollution (Ahmed et al., 2019 ; Wang et al., 2019 ). Hence, urbanization policies with restrictions on unplanned urban sprawl are under the way (Ahmed et al., 2020 ).

Energy is another big agenda for policy analysis. The close connection between energy and emission has been presented noticeably in this study. Governments have come to a consensus that there should be greater balance between ecological purity, energy supply and economic well-being if a country strives for healthy and sustainable economic development (Alola and Joshua, 2021 ). New environmental policies should be designed to control environmental pollution through reducing pollutant emissions and sustaining economic growth, and should be incorporated into governments’ macro policies (Halicioglu, 2009 ). Transformation of energy sector was on agenda to meet the ambitious goals (Cong, 2013 ). The UK, the USA and China are the global leaders in reducing actual emissions and increasing energy supply. In the USA, the shale revolution brought global attention to energy supply and remains to be a driving force for energy policies. Low-cost shale gas combined with the policy support for renewables have notably reduced CO 2 emissions over the past decades. Environmental deregulation is another central focus, which may affect the trajectory of greenhouse gas emission (International Energy Agency, IEA, 2019a , 2019b ). In the UK, the policy objectives of actual emission reduction, carbon budgets setting and investment in energy technology and innovation reflect the ambition for decarbonization (IEA, 2019a , 2019b ). As is known, China’s GDP grows rapidly, which has multiplied more than 170 times since the founding of the People’s Republic of China 73 years ago. However, the extensive economic growth mode depending on the primary and secondary industries has put high pressure on environment, such as large amounts of consumption and pollution (He et al., 2016 ; Yue et al., 2021 ; Yu and Liu, 2020 ). Data showed that the greenhouse gas emission (OECD, 2020 ) and air pollution exposure (OECD, 2022 ) in China have been far higher than those in other countries for a long time, posing great challenges to both the government and scholars. A specific policy package, such as the “Atmosphere Ten Articles”, “Soil Ten Plan” and “Water Ten Plan” from 2013 to 2016, and the “Regulation on the Implementation of the Environmental Protection Tax Law of the People’s Republic of China” in 2017, has been issued by Chinese government, aiming to improve the ecological environment. Furthermore, goals for renewable energy production were also set by scholars. Jacobson suggested that wind, water and sunlight energy should be produced by 2030, and then replace the existing energy by 2050 (Jacobson and Delucchi, 2011 ), while Lund proposed that renewable energy (the combination of biomass with wind, wave and solar) should account for 50% by 2030, and 100% by 2050 (Lund and Mathiesen, 2009 ). However, it remains unclear how many countries can achieve their stated goals. Numerous studies have shown the efforts of governments and scholars to transform the resource and energy usage-driven economic expansion to sustainable development.

From the economics perspective, the environmental Kuznets curve (EKC) hypothesis demonstrates the relationship between environmental quality and economic output, which has been proved by empirical studies (Fodha and Zaghdoud, 2010 ; Saboori et al., 2012 ). Additionally, the relationship between economic growth and energy consumption has also been confirmed (Shahbaz et al., 2015 ). In recent years, countries have been facing the challenge of economic structural transformation. The mode of economic growth that relies on the consumption of natural resource and waste disposal seems increasingly outdated (McDowall et al., 2017 ). Circular economy, a new mode for reconciling environmental and economic imperatives, has come into the public eye and appears to meet the common vision of sustainable development. With the increase of requirements of sustainable development and circular economy, greening of supply chain management also faces challenges, including inventory management, mode of transportation, life-cycle assessment and coordination with other areas (Ghosh and Shah, 2012 ; Ghosh and Shah, 2015 ). Thus, providing support for green supply chain supplier deserves the attention from policy-makers and practitioners.

Key findings

(1) Policy analysis has been a great concern of scholars for many years and has attracted increasing attention year by year, which reflects the value of and actual needs for policy analysis. (2) The world is facing common problems, which requires attention and efforts of the whole world, and a more harmonious social development such as the management of epidemics and complex disease, environmental-friendly development, green energy production and transformation from resource and energy usage-driven economic expansion to sustainable development is on the way. (3) Global profiling for policy analysis demonstrates that the central policy problems align with national development, which inspires further dialog and cooperation on the development of the international community in the future.

Limitations

This study has limitations. First, keywords cannot fully reflect the essential intent of an article although they are the key points of a study. Therefore, using keywords as an element for bibliometric analysis is far from enough. Second, this paper deals with academic research of policy analysis, but whether it is fully consistent with the policy agenda is unexplored. Moreover, we have shown the correlations between different phenomena, but the underlying mechanism remains indefinable.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository ( https://doi.org/10.7910/DVN/XZMVMN ).

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This work was financially supported by Special Project on Innovation and Generation of Medical Support Capacity (NO. 20WQ008) and Chongqing Special Project on Technological Foresight and Institution Innovation (NO. cstc2019jsyj-zzysbAX0037). We are also deeply grateful to prof. Ying Li and prof. Xia Zhang for their constructive suggestions to improve the manuscript.

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Yang, Y., Tan, X., Shi, Y. et al. What are the core concerns of policy analysis? A multidisciplinary investigation based on in-depth bibliometric analysis. Humanit Soc Sci Commun 10 , 190 (2023). https://doi.org/10.1057/s41599-023-01703-0

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Health policy triangle framework: Narrative review of the recent literature

Gary l o'brien, sarah-jo sinnott, valerie walshe, mark mulcahy, stephen byrne.

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Corresponding author at: Pharmaceutical Care Research Group, Room 2.01, Cavanagh Pharmacy Building, University College Cork, College Road, Cork, Ireland. [email protected]

Received 2020 Jun 15; Revised 2020 Sep 1; Accepted 2020 Sep 14; Collection date 2020 Dec.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Developed in the late 20th century, the health policy triangle (HPT) is a policy analysis framework used and applied ubiquitously in the literature to analyse a large number of health-related issues.

To explore and summarise the application of the HPT framework to health-related (public) policy decisions in the recent literature.

This narrative review consisted of a systematic search and summary of included articles from January 2015 January 2020. Six electronic databases were searched. Included studies were required to use the HPT framework as part of their policy analysis. Data were analysed using principles of thematic analysis.

Of the 2217 studies which were screened for inclusion, the final review comprised of 54 studies, mostly qualitative in nature. Five descriptive categorised themes emerged (i) health human resources, services and systems, (ii) communicable and non-communicable diseases, (iii) physical and mental health, (iv) antenatal and postnatal care and (v) miscellaneous. Most studies were conducted in lower to upper-middle income countries.

This review identified that the types of health policies analysed were almost all positioned at national or international level and primarily concerned public health issues. Given its generalisable nature, future research that applies the HPT framework to smaller scale health policy decisions investigated at local and regional levels, could be beneficial.

Keywords: Health policy, Policy analysis, Health policy framework, Policy triangle model, Literature review

In 1994, the health policy triangle was first described in the literature.

Its generalisable nature allows for analysis of many diverse health-related topics.

In recent years, its utilisation in low and middle-income countries has increased.

1. Introduction

The World Health Organisation (WHO) defines health policy as ‘ the decisions, plans, and actions (and inactions) undertaken to achieve specific health care goals within a society or undertaken by a set of institutions and organisations, at national, state and local level, to advance the public's health ’ [ 1 ]. Health policy informs decisions like which health technologies to develop and utilise, how to structure and fund health services, and which pharmaceuticals will be freely available [ 2 ]. Appreciating the intrinsic relationship between health policy and health, and the impact that other policies have on health, is crucial as it can help to address some of the major health problems that exist. However, health policy decisions are not always the result of a rational process of discussion and evaluation of how a particular objective should be met. The context in which the decisions are made can often be highly political and concern the degree of public provision of healthcare and who pays for it [ 3 ]. Health policy decisions can also be conditional on the value judgements implicit in society. As a result, health policies do not always achieve their aims and implementation targets [ 4 , 5 ]. Consequently, health policy analysis is regularly undertaken to understand past policy failures and successes and to plan for future policy implementation [ 6 ].

Just as there are various definitions of what policy is, there too are many ideas about the analysis of health policy, and its focus [ 2 , 6 ]. However, what a lot of health policy analysis studies have in common, whether that be analysis of policy or analysis for policy [ 7 ], is the use of a policy framework. A myriad of policy frameworks and theories exists [ 6 ]. The burgeoning literature of health policy analysis sees novel policy frameworks being developed quite frequently with the ‘ policy cube ’ approach being the latest addition [ 8 ]. A recent literature review investigated the application of some of the more commonly applied frameworks [ 9 ]: the advocacy coalition framework (ACF) [ 10 ], the stages heuristic model [ 11 ], the Kingdon's multiple stream theory [ 12 ], the punctuated equilibrium framework [ 13 ] and the institutional analysis and development framework [ 13 ]. See online supplementary data appendix 1 for brief descriptions of policy frameworks. While the review did mention the health policy triangle (HPT) framework as a means to help organise and think about the descriptive analysis of key variable types, and to facilitate use of said information in one of the aforementioned political science theories/models, it did not investigate its application to public health policies.

The HPT framework was designed in 1994 by Walt and Gilson for the analysis of health sector policies, although its relevance extends beyond this sector [ 14 ]. They noted that health policy research focused largely on the content of policy, neglecting actors, context and processes ( Fig. 1 ). Content includes policy objectives, operational policies, legislation, regulations, guidelines, etc. Actors refer to influential individuals, groups and organisations. Context refers to systemic factors: social, economic, political, cultural, and other environmental conditions. Process refers to the way in which policies are initiated, developed or formulated, negotiated, communicated, implemented and evaluated [ 2 ]. The framework, which can be used retrospectively and prospectively, has influenced health policy research in many countries with diverse systems and has been used to analyse a large number of health issues [ 15 ].

Walt and Gilson policy triangle framework [ 14 ].

In 2015, a historic new sustainable development agenda was unanimously adopted by 193 United Nations (UN) members [ 16 ]. World leaders agreed to 17 sustainable development goals (SDGs). These goals have the power to create a better world by 2030; they strive to end poverty, fight inequality and address the urgency of climate change. The SDGs call on all sectors of society to mobilise for action at a global, local and people level. Given that an estimated 40·5 million of the 56·9 million worldwide deaths were from non-communicable diseases in 2016 [ 17 ]; approximately 810 women died every day from preventable causes related to pregnancy and childbirth in 2017 [ 16 ]; an estimated 6.2 million children and adolescents under 15 years of age died mostly from preventable causes in 2018 [ 16 ]; and approximately 38 million people globally were living with HIV in 2019 [ 16 ], SDG no. 3 aims to address these issues by ensuring healthy lives and promoting wellbeing for all [ 16 ]. This goal has many sub-targets: to reduce maternal mortality; fight communicable diseases; end all preventable deaths under five years of age; promote mental health; achieve universal health coverage (UHC); increase universal access to sexual and reproductive care, family planning and education; and many more. Fortunately, these health topics are regularly examined in the health policy literature and frequently analysed with policy frameworks like the policy triangle model [ [18] , [19] , [20] , [21] ].

Having established prominence in its field, the objective of this review is to explore and summarise the application of the HPT framework to health-related (public) policy decisions in the recent literature i.e. from January 2015 (corresponding with the year that the SDGs were launched) to January 2020. By investigating the application of the HPT framework to health policies during this time period, such analysis can inform action to strengthen future global policy growth and implementation in line with SDG no.3, and provide a basis for the development of policy analysis work. A review of past literature has previously reported on the wide-ranging use of the HPT framework to understand many policy experiences in multiple lower-middle-income country (LMIC) settings only [ 15 ]. This is the first literature review to include a compilation of health policy analysis studies using the HPT framework in both LMIC and high-income country (HIC) settings.

2.1. Literature search

The Medline, CINAHL Plus with Full Text, Web of Science (Core Collection), APA PsycInfo, PubMed and Embase databases were searched for primary, original literature in English published between 1st January 2015 and 31st January 2020. No Geofilter was applied to the searches. Given the subtle differences which exist between Medline and PubMed databases, it was deemed prudent to search both.

A search strategy was developed based on the use of index and free-text terms related to (i) Health Policy Triangle OR (ii) Policy Triangle Framework OR (iii) Policy Triangle Model. The lack of index terms to describe the HPT framework complicated the development of the search strategy. After much debate and perusal of the literature [ 9 , 22 ], a qualified medical librarian reviewed and approved a search strategy prior to undertaking the literature searches. The search strategy was pre-tested prior to use to maximise sensitivity and specificity and to optimise the difference between both. See online supplementary data appendix 2 for the complete search strategy which attempted to include medical subject headings (MeSH) and Emtree terms and the use of Boolean operators.

Search results from multiple databases were transferred to a reference manager, End Note X9 [ 23 ]. Due to the broad remit of the search strategy, a ‘ title revie w’ stage was conducted to remove non-pertinent studies ( Fig. 2 ). Studies were removed in a cautious manner. An abstract review was then performed whereupon studies which clearly did not meet the inclusion criteria were excluded. The remaining studies underwent full-text review. To ensure consistency, one reviewer performed all stages of the review. Experts in academia were contacted to provide several suggestions for potentially pertinent studies. A ‘ snowballing ’ approach was used to identify additional literature through manual screening of the reference lists of the retrieved literature as well as the reference lists of such articles eligible for inclusion.

Flow chart of study selection process.

2.2. Study selection

The retrieved literature was screened for eligibility according to pre-specified inclusion and exclusion criteria ( Table 1 ).

Inclusion and exclusion criteria.

2.3. Study appraisal and data synthesis

The findings of each study included could not be pooled or combined as in systematic reviews or meta-analyses, and it was not deemed necessary to formally assess the study quality [ 24 ]. Indeed, due to the nature of this review, not all of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were relevant, however, insofar as was practical; the PRISMA guidelines were followed [ 25 ]. Instead, data from each study included in the review were extracted following guidance from similar studies [ 9 , 24 , 26 , 27 ], the National Institute for Health and Care Excellence (NICE) [ 27 ] and from the Centre for Reviews and Dissemination's guidance for undertaking reviews in healthcare [ 28 ]. Data were extracted and categorised according to country, country classification by income in 2020 [ 29 ], study design, data collection method, type and number of participants, type of analysis and health policy field i.e. non-communicable diseases, mental health, tobacco control, etc. The health policy field of the included studies was grouped according to similarity by applying the principles of thematic analysis [ 30 , 31 ]. Occasionally, ambiguity arose as to whether some of the included articles' content concerned health-related/public health policy issues, particularly in relation to the studies which investigated road traffic injury prevention [ 32 ] and domestic violence prevention and control [ 33 ]. In such instances, a decision of eligibility for inclusion was made after consultation with a co-author.

3.1. Search results

From the literature searches conducted in the six databases, a total of 2217 citations were retrieved after the removal of duplicates. Based upon the title and abstract screening of the citations, 2142 articles were excluded. Another 35 articles were excluded after reading the full texts. Considering the additional records identified through consultation with experts in the field and by handsearching bibliographies, a total of 54 studies were eligible for inclusion in the review. The process of study selection and reasons for exclusions are outlined in Fig. 2 . Corresponding authors of all conference abstracts ( n  = 9) excluded were emailed to inquire whether a full-length manuscript of their work was published. The response rate was 100%. As of May 2020, no conference abstract had been published as a full-length manuscript.

3.2. Study characteristics

The characteristics of the 54 studies included in the review are summarised in Table 2 . Forty-two of these studies describe themselves as having primarily used a qualitative study design. Data collection via various interview formats seemed to be the most common means of information retrieval. Eight of these studies would consider themselves to have a document analysis study design where one of the eight studies also included field work in its methodology. The remaining four studies can be described as respectively having a scoping review, mixed methods approach, literature review and theoretical analysis study design. According to country classification by income in 2020 [ 29 ], four of the included studies investigated low-income countries (LICs), 20 LMICs, 16 upper-middle income countries (UMICs), and six HICs. Eight studies were classed as ‘ varied ’ due to multiple countries of different classifications of income being simultaneously examined. All the included studies can be described as some variant of policy analysis. Certain articles highlighted whether the policy analysis was retrospective, prospective or comparative in nature; approximately 20% of the studies incorporated additional conceptual frameworks. Such additional details are outlined in the ‘ Type of analysis ’ column in Table 2 . Six studies conducted a supplementary stakeholder analysis/mapping [ 34 ].

Characteristics of included studies (listed alphabetically according to first author).

Abbreviations: ACF - Advocacy Coalition Framework; AIDS - Acquired Immune Deficiency Syndrome; EMCONET - Employment and Working Conditions Knowledge Network; GP - General Practitioner/Physician; HIC - High-Income Country; HIV - Human Immunodeficiency Virus; HPT – Health Policy Triangle (Framework); HPV – Human Papillomavirus; HRH - Human Resources for Health; LIC - Low-Income Country; LMIC - Lower-Middle-Income Country; UHC – Universal Health Coverage; UMIC - Upper-Middle-Income Country; UN – United Nations; WHO – World Health Organisation; ? – Not specifically mentioned in related text.

Hansen et al. [ 47 ], 2017 - Content and process factors omitted in HPT analysis but justified elsewhere in manuscript.

Juma et al. [ 51 , 52 ], 2018 - Juma et al. have published two study papers on a related topic from the same project using the same retrieved data sources. Thus, given the similarity, one data entry was deemed sufficient to encompass these two related study papers.

McNamara et al. [ 59 ], 2017 - A framework by the EMCONET of the WHO's Commission on the Social Determinants of Health that comprehensively outlines pathways to health via labour markets [ 87 ].

Mureithi et al. [ 67 ], 2018 - A conceptual framework by Liu et al. [ 88 ] on the impact of ‘ contracting-out ’ on health system performance.

Nogueira-Jr et al. [ 89 ], 2018 – Actor factor omitted in HPT analysis but justified elsewhere in manuscript.

Odoch et al. [ 71 ], 2015 – Bespoke frameworks used that were conceived from Walt and Gilson's concepts for analysing the inter-relationships between actors, process, and contexts [ 14 ]. Odoch et al. also cited Kingdon's multiple stream theory model [ 12 ], Foucault's concept of power [ 90 ] and the Glassman et al. [ 91 ] concept of position mapping of actors, in their bespoke frameworks.

Oladepo et al. [ 73 ], 2018 - Interview guides were informed by the Walt and Gilson policy analysis framework [ 14 ] and the McQueen analytical framework for inter-sectoral action [ 92 ].

Tokar et al. [ 79 ], 2019 - A framework analysis initially developed by Goffman et al. [ 93 ] and adapted by Caldwell et al. [ 94 ] was used in order to examine how the HIV/AIDS programme was conceptualised.

Wisdom et al. [ 83 ], 2018 – Wisdom et al. use the same key informant interviews data source that was utilised by Juma et al. [ 51 , 52 ].

Zhu et al. [ 85 ], 2018 – Authors purport to use a policy triangle framework proposed by Hawkes et al. [ 95 ]. Upon further inspection and email contact with Hawkes, the framework used was in fact the HPT model originally proposed by Walt and Gilson [ 14 ] thus this study was included in the review. It is assumed that the authors accidentally miscited the policy triangle framework in their study.

Zupanets et al. [ 86 ], 2018 – It is unclear which genre of study design best describes this article. For the purposes of this review, its study design was dubbed as a ‘ theoretical analysis ’.

3.3. Study findings

From the content analysis approach to the health policy fields of the included studies, five broad descriptive categorised themes were identified demonstrating how the HPT framework was applied to health-related (public) policy decisions in the recent literature: (i) health human resources, services and systems, (ii) communicable and non-communicable diseases, (iii) physical and mental health, (iv) antenatal and postnatal care and (v) miscellaneous. Unsurprisingly, many of the health policy fields explored in the included studies aimed to address sub-targets of SDG no. 3 [ 16 ].

3.3.1. Health human resources, services and systems

The implementation of the human resources for health (HRH) commitments announced at the third global forum on HRH [ 96 ], with particular attention given to health workforce commitments, were analysed by two separate studies for different countries [ 42 , 81 ]. Another study by Witter et al. focused on the patterns and drivers of HRH policy-making in post-conflict and post-crisis health systems: namely those of Cambodia, Sierra Leone Uganda and Zimbabwe, all lower to lower middle-income countries. Similarly, Van de Pas et al. conducted a policy analysis study which sought to inform capacity development that aimed to strengthen public health systems, and health workforce development and retention, in a post-Ebola LIC setting [ 80 ]. Indeed, health workforce retention policy analysis was also carried out by Joarder et al. where retaining doctors in rural areas of Bangladesh was a challenge [ 49 ].

Two studies looked at potential issues and policies surrounding UHC facilitation in the primary healthcare setting [ 22 , 40 ]. The somewhat related concept of contracting health services arose in three studies where it was explored in relation to contracting for public healthcare delivery in rural Cambodia [ 54 ], contracting-out urban primary healthcare in Bangladesh [ 48 ], and the emergence of three general practitioner/physician (GP) contracting-in models in South Africa [ 67 ].

At primary and community healthcare level, a variety of policy analysis studies scrutinised topics like the formation of primary healthcare in rural Iran in the 1980s [ 64 ], contextual factors and actors that influenced policies on team-based primary healthcare in Canada [ 60 ], the potential implementation of out-of-pocket payments to GPs in Denmark [ 47 ], and policy resistance surrounding integrated community case management for childhood illness in Kenya [ 50 ].

There were three policy analysis studies which focused on medicines and pharmaceutical safety within the health system. Abolhassani et al. reviewed medication safety policy that saw the establishment of the drug naming committee to restrict look-alike medication names [ 36 ]. Alostad et al. investigated herbal medicine registration systems policy [ 38 ] while Zupanets et al. sought to formulate theoretical approaches to the improvement of pharmaceutical care and health system integration [ 86 ].

3.3.2. Communicable and non-communicable diseases

The policy response to non-communicable diseases by the Ministry of Health in Zambia was explored by Mukanu et al. [ 65 ], where similarly, Juma et al. investigated non-communicable disease prevention policy development and processes, and how multi-sectoral action is involved [ 51 , 52 ]. Kaldor et al. analysed policy which used regulation to limit salt intake and prevent non-communicable diseases [ 53 ]. O'Connell et al. compared frameworks from different countries that aimed to improve self-management support for chronic (non-communicable) diseases [ 70 ]. Two studies focused on diabetes, one of the leading non-communicable diseases worldwide, where prevention and control policies for the disease state were reviewed [ 44 , 77 ].

Communicable disease policy analysis studies concentrated on two main viruses; human immunodeficiency virus (HIV) and human papillomavirus (HPV). Analyses in relation to HPV looked at the feasibility of implementation and non-implementation of a HPV vaccination programme in upper-middle to high income countries [ 41 , 72 ]. HIV-related studies varied from policies like task shifting of HIV/AIDS case management to community health service centres [ 55 ], and male circumcision for HIV prevention [ 71 ], to HIV testing policies among female sex workers [ 79 ]. Nogueira-Jr et al. investigated the implementation of national programmes for the prevention and control of healthcare associated infections in three upper-middle to high income countries [ 69 ].

3.3.3. Physical and mental health

Alcohol consumption, illegal drugs ingestion, nutritional habits and tobacco inhalation are all potential determinants of the quality of physical health status. Four studies investigated varying factors surrounding tobacco control policies [ 57 , 61 , 73 , 83 ]. Two studies examined alcohol-related policies [ 35 , 68 ] where one study scrutinised illegal drug policies [ 37 ]. Three studies explored nutrition: two focusing on malnutrition management and prevention in UMICs [ 56 , 62 ] and one reviewing school food policy development and implementation in the Philippines [ 74 ]. Interestingly, all three mental health policy analysis studies included in this review focused on the topic of child, and mostly, adolescent mental health policy [ 45 , 63 , 75 ].

3.3.4. Antenatal and postnatal care

Policy analysis studies regarding pregnancy and mother and child wellbeing featured strongly. Zhu et al. outlined the progress of midwifery-related policies in contemporary and modern China [ 85 ] while Munabi-Babigumira et al. analysed the strategies implemented and bottlenecks experienced as Uganda's skilled birth attendance policy was launched [ 66 ]. Other studies looked at the various factors which promoted or impeded agenda setting and the formulation of policy regarding perinatal healthcare reform [ 82 ], person-centered care in maternal and newborn health, family planning and abortion policies [ 78 ], and the integrated maternal newborn and child health strategy [ 58 ].

3.3.5. Miscellaneous

There were some other policy analysis studies that can be treated as standalone articles within the context of this review: palliative care system design [ 39 ]; national law on domestic violence prevention and control within the health system [ 33 ]; oral health policy development [ 43 ]; road traffic injury prevention [ 32 ]; national school health policy implementation [ 76 ]; and medical tourism policy [ 46 ]. Interestingly, given that the impact of the Trans-Pacific partnership agreement on employment and working conditions is a major point of contention in broader public debates worldwide [ 97 ], one prospective policy analysis study examined the potential health impacts of the Trans-Pacific partnership agreement [ 98 ] by investigating labour market pathways [ 59 ].

4. Discussion

From the findings of this review, the most common method of data collection was by means of some form of interview with participants involved in the relevant policy area. The same finding was found in a similar review [ 15 ]. Talking to actors can provide rich information for policy analysis. These collection methods may be the only way to gather valid information on the political interests and resources of relevant actors and to gather historical and contextual information. Indeed, interviews are generally more useful in eliciting information of a more sensitive nature where the goal of the interview is to obtain useful and valid data on stakeholders' perceptions of a given policy issue [ 2 ]. However, interview data can be ambiguous in the sense that what interviewees say and the manner in which they say it, may contrast what one actually thinks or does. Many of the studies included in this review overcome this potential limitation by triangulating the responses with additional responses from other informants, or with data collected via alternative channels, particularly documentary sources.

Many different types of policy fields were unearthed throughout the data extraction process. Quite a lot of the studies reviewed large-scale health policies at national level whether that policy be UHC implementation, infectious disease vaccination programmes, or malnutrition management. Some studies conducted policy analysis at international level investigating areas such as the health impact of the Trans-Pacific partnership agreement, and the implementation of the HRH commitments announced at the third global forum on HRH that involved over fifty countries. Cross-country comparative policy analysis was also common and examined topics like medical tourism, factors of HRH policy-making in post-crisis health systems, and frameworks to improve self-management support for chronic diseases. Indeed, health policy fields explored within the descriptive categorised theme ‘ miscellaneous ’ demonstrated how wide-ranging the applicability of the HPT framework is to a variety of health-related (public) policy decisions. None of the included published literature explored policy analysis of local or regional health-related policy decisions using the HPT framework. Given its generalisable nature, further and perhaps more novel uses of the descriptive policy triangle model could be trialed in a diverse range of health policy decisions made at local and regional level.

Of the policy analysis study countries reviewed, approximately 40% were classified as LMIC settings. In recent years, such work has been incorporated into analysis of LMIC public sector reform experiences [ 15 ] thus possibly explaining this relatively high percentage. In addition, a reader recently published by WHO to encourage and deepen health policy analysis work in LMIC settings, which considers how to use health policy analysis prospectively to support health policy change, could explain this high percentage [ 99 ]. Interestingly, notwithstanding that work conducted within the field of policy analysis is fairly well-established in the United States and Europe [ 100 , 101 ], only approximately 12% of the policy analysis studies yielded from this review were conducted in HIC settings. This finding is open to many interpretations with one crude deduction being that perhaps policy analysis is currently more common in LMIC settings than in HIC settings. Another possibility is that commissioned policy analysis studies in HIC settings are seldom published in peer-reviewed academic journals. Also, it may be the case that LMIC settings rely on external academics to carry out and publish their health policy analysis studies as a recently published evidence assessment reports that LMICs often have an incomplete and fragmented policy framework for research [ 102 ]. Further research is required.

All the included studies in this review can be described as some variant of policy analysis where certain articles specifically stated whether the policy analysis was retrospective, prospective or comparative in nature. In fact, the vast majority of studies can be categorised as analyses of policy rather than for policy [ 7 ]. Most of the studies still seek to assist future policy-making, but are largely descriptive in nature, limiting understanding of policy change processes. Similar findings are found in the literature [ 15 ].

The comparative policy analysis studies included often involved more than one country with exception of the analysis by Misfeldt et al. who explored the context and factors shaping team-based primary healthcare policies in three Canadian provinces [ 60 ]. Although such comparative studies may introduce further challenges (such as working across multiple languages and cultures, and procuring additional funding), the comparisons between similar (and different) country contexts can help disentangle generalisable effects from country context-specific effects in policy adaptation, evolution and implementation [ 6 ].

Six studies conducted a supplementary stakeholder analysis/mapping. Stakeholder analysis can be used to help understand about relevant actors, their intentions, inter-relations, agendas, interests, and the influence or resources they have brought or could bring on decision-making processes during policy development [ 103 ]. The use of stakeholder analysis in this review was complemented by other policy analysis approaches as is corroborated by the literature [ 34 ].

Interestingly, approximately 20% of the studies in this review applied an additional analytical/theoretical framework. McNamara et al. used a framework by the Employment and Working Conditions Knowledge Network (EMCONET) of the WHO's Commission on the Social Determinants of Health [ 59 ] which comprehensively outlines pathways to health via labour markets [ 87 ]. Mureithi et al. applied a conceptual framework by Liu et al. on the impact of contracting-out on health system performance [ 67 , 88 ]. Odoch et al. decided to implement many bespoke frameworks [ 71 ] that were conceived from Walt and Gilson's concepts for analysing the interrelationships between actors, process, and context [ 14 ] as well as citing the Kingdon's multiple stream theory model [ 12 ], Foucault's concept of power [ 90 ] and the Glassman et al. concept of position mapping of actors [ 91 ]. Oladepo et al. utilised the McQueen analytical framework for inter-sectoral action [ 73 , 92 ] while Tokar et al. incorporated a framework analysis that was initially developed by Goffman et al. and subsequently adapted by Caldwell et al. in order to examine how the HIV/AIDS programme in question was conceptualised [ 79 , 93 , 94 ]. Given that there is a paucity of theoretical and conceptual approaches to analysis of the processes of health policy in LMIC settings [ 6 , 104 ], the need to use multiple bespoke frameworks in the aforementioned recent policy analyses may be a plausible finding. In addition, other research has shown that the Walt and Gilson triangle model ‘ needs to be operationalised and transformed ’ in practice which may suggest that it is not fit for purpose in its primitive state [ 105 ]. This could explain why auxiliary frameworks are applied alongside the HPT model in these studies.

Other studies applied the Kingdon model in addition to the HPT framework [ 47 , 62 , 64 ] where Reeve et al. used components of the ACF, Kingdon model and HPT framework [ 74 ]. The policy triangle model is often regarded as being descriptive in nature [ 9 , 13 ] thus supplementation with additional frameworks such as the ACF and Kingdon model can enrich the analysis by making it more explanatory [ 9 ]. Doshmangir et al. used a tailored version of the HPT framework incorporating the stages heuristic model to guide data analysis [ 22 ]. Like the policy triangle model, the stages heuristic are often characterised as being descriptive in nature [ 9 ], thus the aforementioned study provided a highly descriptive policy analysis of UHC facilitation in the primary healthcare setting in Iran. Unfortunately, no single policy framework offers a fully comprehensive description or understanding of the policy process as each model answers somewhat different questions [ 104 , 106 ]. Existing policy frameworks have complementary strengths since policy dynamics are driven by a multiplicity of causal paths [ 107 ]. Thus, multiple frameworks can be applied as ‘ tools ’ in order to assess and plan action. However, it is important to discern which frameworks may be better suited for particular scenarios and policy issues [ 106 ].

Some of the 23 articles (see Fig. 2 ) that were excluded from this review for not utilising the policy triangle model used other bespoke and well-known health policy frameworks, with the Kingdon's multiple streams theory being the most common [ 12 ]. As previously mentioned, a ‘ snowballing ’ approach was used to identify additional literature through manual screening of the reference lists of the retrieved literature as well as the reference lists of such articles eligible for inclusion. Eleven additional studies were identified from this strategy ( Fig. 2 ) meaning many more were excluded for not meeting the inclusion criteria ( Table 1 ). Such studies were too many to document. However, two articles identified from this process appeared to be quite misleading and thus noteworthy. Onwujekwe et al. described a conceptual model that they used in their policy analysis which was almost identical to the HPT framework [ 108 ]. However, as the authors didn't characterise or reference their framework to the policy triangle model or to the work of Walt and Gilson, it was omitted from the review. Similarly, Doshmangir et al. portrayed their results in such a way that correlated to the four components of the HPT framework [ 109 ]. While the authors did mention the policy triangle framework as a talking point in their discussion section, they failed to explicitly reference it in their methodology and results paragraphs. This led to the exclusion of their study from the review. It is not known why these studies didn't appropriately reference the utilisation of the HPT framework when its application was apparent. It is possible that more policy analysis studies which exist in the recent literature could be presented in a similarly ambiguous manner.

5. Limitations

The included articles were mostly qualitative in nature albeit other study designs were also utilised. Limitations inherent to such study designs may present a bias in the quality of the included articles. Grey literature including reports may have provided important sources of information regarding the application of the HPT framework to health-related (public) policy decisions. However, given the difficulty associated with designing internet search strategies, the heterogenous nature of grey literature documents and the additional time required, it was excluded from the review [ 110 ]. It was decided to only include primary English-language published literature on this topic from January 2015 to January 2020. It is recommended that additional reviews of other language literature be conducted in association with a wider time frame. This review does not claim to be a fully comprehensive summary of all policy analysis studies which utilised the HPT framework between 2015 and 2020. Further consultation with additional experts, citation searching methods, and handsearching of key journals may produce more relevant articles for inclusion. However, given that the majority of studies analysed thematically in this review are qualitative in nature, it can be argued that it is not necessary to locate every available study for such purposes [ 31 , 111 ]. In addition, it is known that some of the doctoral theses and unpublished material in the field are already represented within the published literature included here. Sometimes, the components of the HPT framework i.e. actors, content, context, process are described as such in the literature without exclusively referring to the HPT framework itself. Thus, these studies would not have been detected using the search strategy chosen for this review (online appendix 2). Finally, when compared to other research designs (e.g. systematic reviews), narrative reviews of the literature are more susceptible to bias e.g. the included articles were not evaluated for their quality [ 112 ].

6. Conclusion

This narrative review of the recent literature sought, retrieved and summarised the application of the HPT framework to health-related (public) policy decisions. Based on the findings of the review, it appears that the use of this framework appears to be ubiquitous in the health policy literature where many researchers supplement with additional health policy frameworks to further enhance their analysis. Notwithstanding a previous debate which disputes that there is a dearth of theoretical and conceptual approaches to analysis of the processes of health policy in low and middle-income countries [ 6 , 104 ], this review demonstrates that the shortage of health policy analysis studies now appears to come from high income countries. The finding suggests the need for additional health policy analyses to be conducted in such settings, or if this is already happening, the demand to publish more. In relation to the types of health policies being scrutinised, almost all were positioned at national or international level and primarily concerned public health issues. However, given its universal presence in the literature, and its unique adaptability and generalisability to many varied health policy topics, future research applying the HPT framework to smaller scale health policy decisions being investigated at local and regional levels, could be beneficial.

This research project was funded by Irish Research Council (GOIPG/2016/635). The funders had no part in the design of the review; the collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the article for publication.

Ethical approval

Ethical approval was not required.

Author contributions

Gary L O'Brien (GLOB), Sarah-Jo Sinnott (SJS), Stephen Byrne (SB), Valerie Walshe (VW), and Mark Mulcahy (MM): GLOB was responsible for protocol design, study selection, data extraction, drafting of the manuscript and approval of the final manuscript. GLOB conceived the study idea. GLOB, SJS and SB decided on the database selection. GLOB carried out data collection. GLOB analysed and interpreted the data. GLOB wrote the final manuscript; SJS, MM, VW and SB revised the manuscript. All authors read and approved the final manuscript.

Declaration of competing interest

The authors have no conflicts of interest to declare.

Acknowledgements

GLOB would like to acknowledge Professor John Browne, School of Public Health, University College Cork, Ireland, who teaches PG7016, a postgraduate module in Systematic Reviews in the Health Sciences. Undertaking this module proved extremely beneficial to the completion of this review. GLOB would also like to acknowledge Ms. Donna Ó Doibhlin, Liaison Librarian, Medicine & Health Sciences, Boston Scientific Health Sciences Library, Brookfield Complex, University College Cork, Ireland, for her assistance in devising the search strategy for this narrative review.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.hpopen.2020.100016 .

Online supplementary data

Supplementary material

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