Quelle plus-value de la démarche d’évaluation d’impact sur la santé pour les politiques urbaines ?
Bibliographic record
Abstract
L’évaluation d’impact sur la santé (EIS) suscite aujourd’hui en France un réel intérêt et la pratique ne cesse de progresser, motivée par une préoccupation commune des villes et des institutions régionales de la santé, de contribuer ensemble à l’amélioration de la santé et à la réduction des inégalités sociales de santé. Cette démarche a pour but d’identifier les conséquences potentielles de politiques ou projets sur la santé des populations afin de proposer des mesures destinées à atténuer les impacts négatifs et renforcer les impacts positifs. L’objectif de cet article est d’apprécier dans quelle mesure et par quels mécanismes l’EIS facilite l’intégration de la dimension santé dans les politiques urbaines.
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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 | 0.058 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".