HEALTH IMPACT ASSESSMENT PRACTICES AND THEIR INFLUENCE ON PUBLIC HEALTH IN MONTÉRÉGIE
Bibliographic record
Abstract
This study examines the effects of the collaborative Health Impact Assessment (HIA) model implemented in Monteregie, Quebec, on the development, adoption, and implementation of municipal projects incorporating health considerations. Nine HIA processes across nine territories were analyzed, involving 35 participants engaged at various stages. Using a cross-sectional design, data were collected through document analysis, semi-structured interviews, and on-site observations, guided by the six-step Contribution Analysis framework. Each HIA was assessed at least six months post-completion to evaluate its influence. Findings indicate varied outcomes. While participants gained new knowledge, the process had limited success in raising municipal actors’ awareness of health issues. HIAs primarily provided stakeholders with stronger arguments to advocate for health-focused actions within their councils. HIAs were often led by actors already aware of the importance of health promotion. Some recommendations were incorporated into planning documents, but reports more often remained supplementary rather than fully integrated into core planning materials. Nonetheless, many municipal actors continued to consider health implications in future policy and project planning. Key prerequisites for effective HIAs include engaged municipal actors aware of their impact on community health, existing municipal policies incorporating health considerations, and active municipal participation throughout the HIA process. This study highlights the complexity of factors influencing HIA effectiveness and underscores the unique dynamics within each process that shape its impact on municipal decision-making.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".