Scientific evidence and public policy: a systematic review of barriers and enablers for evidence-informed decision-making
Bibliographic record
Abstract
Introduction This systematic review synthesizes empirical research on the integration of scientific evidence into public policy formulation across diverse governance contexts. While global support for evidence-informed policymaking is increasing, persistent institutional barriers, political resistance, and limited science-policy interaction continue to constrain the effective use of research in decision-making. Methods Guided by the PRISMA 2020 framework, the review identified 119 peer-reviewed articles from Scopus and Web of Science databases. Eligible studies included empirical analyses on the mobilization, translation, and institutionalization of scientific knowledge in policy processes. A thematic synthesis was conducted, classifying studies into six categories: science-policy participation, institutional capacity, political dynamics, trust and legitimacy, political support, and international collaboration. Results Major barriers included fragmented advisory systems, limited data infrastructures, and weak communication between researchers and policymakers. Key enabling factors comprised dedicated scientific advisory bodies, knowledge brokerage mechanisms, international cooperation, and co-production of knowledge. Most studies focused on the health policy sector, with a geographic concentration in high-income countries such as the United Kingdom, the United States, and Canada. Discussion Findings highlight the urgent need to institutionalize scientific evidence in policy formulation through formal governance frameworks, sustained stakeholder engagement, and robust science-policy interfaces. Advancing transparent, inclusive, and evidence-based governance will require cross-sector collaboration, epistemic trust, and political leadership committed to bridging the gap between research and public policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.109 | 0.386 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".