L’évaluation d’impact sur la santé pour scruter et sculpter les politiques
Bibliographic record
Abstract
Health impact assessment (HIA) is a prospective approach that consists of identifying the potential consequences, both negative and positive, of an intervention on the health of populations with the aim of improving it. Identified as a specific practice in 1999, it rapidly gained in popularity and was progressively deployed on all continents with variations in terms of implementation strategies, area of application, scales of implementation, modes of governance, institutions and actors involved. It is currently booming in France and Quebec, where it is generating real expectations with regard to the issues of health inequalities, democracy and the coordination of sectoral policies. This article, based on our research, provides a brief portrait of HIA in France and introduces questions on the strengths, limitations and added-value of the approach. This special issue sheds light on the practice through applications in different fields and contexts, emphasizes the methodological, political and social issues related to the process as well as the challenges to be met in order to strengthen the potential of HIA to improve decision-making and develop policies and projects that promote health.
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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 | 0.073 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".