Comment évaluer les effets des évaluations d’impact sur la santé : le potentiel de l’analyse de contribution
Bibliographic record
Abstract
Abstract: Health impact assessments (HIA) allow the potential impact of non-health-related actions (policy, project, program) on health to be analyzed. For example, municipal projects, for urban renewal or urban planning, have an impact on health drivers relating to the created environment, and thus, on public health. In the province of Quebec, the government and affiliated organizations have to comment on potential impacts; they use HIAs to ensure health is factored into public actions. We know little about HIAs’ capacity to influence public policies; every policy and program is different and is often implemented only once, greatly complicating evaluation. Our goal is to analyze the potential of contribution analysis for evaluating the impact of HIAs at the municipal level. To this end, we present the HIA process as implemented in Montérégie, we identify evaluation challenges, and put forth, through contribution analysis, an evaluation strategy to analyze effects.
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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 | 0.378 | 0.606 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".