A Comparative Analysis of Health Impact Assessment Implementation Models in the Regions of Montérégie (Québec, Canada) and Nouvelle-Aquitaine (France)
Bibliographic record
Abstract
Many countries have introduced health impact assessment (HIA) at the national, regional, or local levels. In France and in Québec, there is increasing interest in using HIA to inform decision-makers and influence policies, programs, and projects. This paper aims to compare HIA implementation models in two regions: Nouvelle-Aquitaine (France) and Montérégie (Québec, Canada) using a case study methodology. The objective is to gain a better understanding of the similarities and differences in the approaches used to achieve the operationalization of HIA. The methodological approach involves four steps: (1) design of an analytical framework based on the literature; (2) exchanges within the research team and review of documents concerning the two implementation strategies under study; (3) development of the case studies based on the proposed framework; and (4) cross-comparison analysis of the case studies. The findings show that the two regions share certain similarities, including the strong commitment and political will of the public health organizations involved and a well-established culture of engaging in intersectoral action with municipal partners. Differences mainly concern their different approaches to implementing HIAs in accordance with the regional policies and the organizational and administrative contexts in place. This study identifies potential avenues for supporting the practice of HIA at the municipal level.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".