How to study the implementation of health policy interventions with integrative frameworks?
Bibliographic record
Abstract
Given the biomedical and epidemiological history of health research, it is still too often the question of effectiveness and efficiency that attracts evaluand attention. Yet, what is the point of explaining the achievement of health policy objectives if we need to account for how this was achieved? The study of health policy implementation is, therefore, essential. But there are many ways to study the implementation of health policy. Thus, in this chapter, we have brought together several (single and multiple) case studies from around the world (Burkina Faso, Kenya, France, Mali, Senegal) to show how it is often relevant to use integrative analytical frameworks from rigorous conceptual bricolage to study the implementation. Students of health policy will thus find a source of inspiration from several lessons learned from international contexts rarely highlighted in the scientific literature.
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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 | 0.043 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".