Shaping Policy Change in Population Health: Policy Entrepreneurs, Ideas, and Institutions
Bibliographic record
Abstract
Political realities and institutional structures are often ignored when gathering evidence to influence population health policies. If these policies are to be successful, social science literature on policy change should be integrated into the population health approach. In this contribution, drawing on the work of John W. Kingdon and related scholarship, we set out to examine how key components of the policy change literature could contribute towards the effective development of population health policies. Shaping policy change would require a realignment of the existing school of thought, where the contribution of population health seems to end at knowledge translation. Through our critical analysis of selected literature, we extend recommendations to advance a burgeoning discussion in adopting new approaches to successfully implement evidence-informed population health policies.
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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 | 0.101 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.033 | 0.027 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".