CoChat
Sign Up
Cited by 526Health Policy Implementation ScienceEvaluation and Performance AssessmentSocial Science and Policy Research

Evidence-based policymaking is not like evidence-based medicine, so how far should you go to bridge the divide between evidence and policy?

Open access
Cairney, Paul, Oliver, Kathryn·2017·Health Research Policy and Systems
DOI 10.1186/s12961-017-0192-xPMC PMC5407004
Open this paper in CoChat — free accountOpen PDF

Abstract

There is extensive health and public health literature on the 'evidence-policy gap', exploring the frustrating experiences of scientists trying to secure a response to the problems and solutions they raise and identifying the need for better evidence to reduce policymaker uncertainty. We offer a new perspective by using policy theory to propose research with greater impact, identifying the need to use persuasion to reduce ambiguity, and to adapt to multi-level policymaking systems.We identify insights from secondary data, namely systematic reviews, critical analysis and policy theories relevant to evidence-based policymaking. The studies are drawn primarily from countries such as the United States, United Kingdom, Canada, Australia and New Zealand. We combine empirical and normative elements to identify the ways in which scientists can, do and could influence policy.We identify two important dilemmas, for scientists and researchers, that arise from our initial advice. First, effective actors combine evidence with manipulative emotional appeals to influence the policy agenda - should scientists do the same, or would the reputational costs outweigh the policy benefits? Second, when adapting to multi-level policymaking, should scientists prioritise 'evidence-based' policymaking above other factors? The latter includes governance principles such the 'co-production' of policy between local public bodies, interest groups and service users. This process may be based primarily on values and involve actors with no commitment to a hierarchy of evidence.We conclude that successful engagement in 'evidence-based policymaking' requires pragmatism, combining scientific evidence with governance principles, and persuasion to translate complex evidence into simple stories. To maximise the use of scientific evidence in health and public health policy, researchers should recognise the tendency of policymakers to base judgements on their beliefs, and shortcuts based on their emotions and familiarity with information; learn 'where the action is', and be prepared to engage in long-term strategies to be able to influence policy; and, in both cases, decide how far you are willing to go to persuade policymakers to act and secure a hierarchy of evidence underpinning policy. These are value-driven and political, not just 'evidence-based', choices.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12961-017-0192-x

Cites

101 of 101 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Policy decisions are typically not solely based on technical evidence, and the power of persuasion and in some cases manipulation plays a critical role in legislative processes.

    “Additionally, while ML predictions can provide useful information, policy decisions are typically not solely based on technical evidence (Parkhurst, 2016), and the power of persuasion and in some cases manipulation, plays a critical role in legislative processes (Zahariadis, 2003; Cairney and Oliver, 2017).”

    From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Cairney and Oliver, 2017

    In “How to maximise the use of evidence in policy” (page unavailable), Cairney and Oliver say persuasion and framing can influence policymakers and describe strategies that manipulate people’s biases. They discuss policymaking broadly, however, rather than establishing that persuasion and manipulation play a critical role specifically in legislative processes. The sentence cites Parkhurst, not Cairney and Oliver, for the separate point about decisions not relying solely on technical evidence. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 13

  1. [3]Cairney P. The politics of evidence-based policymaking. London: Palgrave Pivot; 2016.
  2. [14]Cairney P. How can policy theory have an impact on policy making? Teaching Public Administration. 2015;33(1):22–39.
  3. [16]Kahneman D Thinking Fast and Slow (UK edition) 2012 London Penguin
  4. [18]Lewis P. Policy thinking, fast and slow. American Political Science Association 2013 Annual Meeting. 2013. http://ssrn.com/abstract=2300479.
  5. [25]Zahariadis N. The multiple streams framework. In: Sabatier P, editor. Theories of the policy process. Cambridge: Westview; 2007.
  6. [26]True JL, Jones BD, Baumgartner FR. Punctuated equilibrium theory. In: Sabatier P, editor. Theories of the policy process. 2nd ed. Cambridge: Westview Press; 2007.
  7. [28]Pielke RA The honest broker 2007 Cambridge Cambridge University Press
  8. [65]John P. Analysing public policy. 2nd ed. London: Routledge; 2012.
  9. [66]Sabatier P. The need for better theories. In: Sabatier P, editor. Theories of the policy process 2. Cambridge: Westview; 2007.
  10. [67]Birkland T After Disaster 1997 Washington Georgetown University Press
  11. [68]Hall P. Policy paradigms, social learning, and the state. Comp Politics. 1993;25(2):275–96.
  12. [72]Cartwright N Hardie J Evidence-based policy 2012 Oxford Oxford University Press
  13. [82]Petticrew M Roberts H Systematic reviews in the social sciences 2006 Oxford Blackwell

Cited by

Sorted by citations

Related

Authors
Cairney, P., & Oliver, K.
Affiliations
  • University of Stirling (GB)
  • University of Oxford (GB)
Corresponding author
Cairney, Paul
ORCID
Venue
Health Research Policy and Systems
Publisher
Springer Science and Business Media LLC
Published
2017-04-26
Volume
15
Issue
1
Pages
35-35
ISSN
1478-4505 (ISSN-L)
DOI
10.1186/s12961-017-0192-x
PMC
PMC5407004
OpenAlex
W2609112051
Cited by
526
References
101
Citations by year
2026: 49 · 2025: 60 · 2024: 55 · 2023: 62 · 2022: 74 · 2021: 78 · 2020: 69 · 2019: 41 · 2018: 29 · 2017: 9
FWCI
65.33
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Health Research Policy and Systems (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Stirling Online Research Repository (University of Stirling) (submittedVersion, cc-by)
Funders
Economic and Social Research Council (ES/L003325/1)
Topics
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
  • Social Sciences › General Social Sciences › Social Science and Policy Research
Keywords
evidence-based policy, evidence-policy gap, evidence-based medicine, policy theory, persuasion, emotional appeal, evidence hierarchy, health policy, public health policy
MeSH terms
Australia, Canada, United Kingdom, Health Policy, Humans, New Zealand, Policy Making, United States, Evidence-Based Medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review