Policy diffusion theory, evidence-informed public health, and public health political science: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Our aim was to synthesize published scholarship that applies policy diffusion-a theory of the policy process that considers the interdependence of government-level public health policy choices. We paid particular attention to the role of scientific evidence in the diffusion process, and to identifying challenges and gaps towards strengthening the intersection of public health, public policy, and political science. METHODS: We systematically searched 17 electronic academic databases. We included English-language, peer-reviewed articles published between 2000 and 2021. For each article, we extracted the following information: public health policy domain, geographic setting, diffusion directions and mechanisms, the role of scientific evidence in the diffusion process, and author research discipline. SYNTHESIS: We identified 39 peer-reviewed, primary research articles. Anti-smoking and tobacco control policies in the United States (n = 9/39) were the most common policy domain and geographic context examined; comparatively fewer studies examined policy diffusion in the Canadian context (n = 4/39). In terms of how policies diffuse, we found evidence of five diffusion mechanisms (learning, emulation, competition, coercion, and social contagion), which could moreover be conditional on internal government characteristics. The role of scientific evidence in the diffusion process was unclear, as only five articles discussed this. Policy diffusion theory was primarily used by public policy and political science scholars (n = 19/39), with comparatively fewer interdisciplinary authorship teams (n = 6/39). CONCLUSION: Policy diffusion theory provides important insights into the intergovernmental factors that influence public health policy decisions, thus helping to expand our conceptualization of evidence-informed public health. Despite this, policy diffusion research in the Canadian public health context is limited.
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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 | 0.062 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.034 | 0.035 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".