Evaluation of the effects of health impact assessment (hia) practice in monteregie
Bibliographic record
Abstract
Abstract This study explores the effects of the collaborative model of health impact assessment (HIA), as deployed in Monteregie (Quebec), on the development, adoption and implementation of municipal projects that include health considerations. Nine HIA processes were studied in nine territories and 35 individuals were interviewed. Data collection was based on the six steps of contribution analysis, and included document analysis, semi-structured interviews, and on-site observations. The study results showed that the HIAs had varying results. First, the actors involved acquired new knowledge. However, the HIAs had little impact in terms of increasing the municipal actors’ awareness of health issues. Rather, it helped them acquire arguments for raising awareness among and convincing their municipal council members of the merits of certain actions and their potential positive impacts on citizens’ health. In fact, the HIAs were generally undertaken by municipal actors already aware of the importance of promoting citizen health. Second, in a few of the HIAs, some recommendations were integrated into planning documents, but usually the HIA report constituted an additional planning document and was not merged with the original planning documents. Lastly, following the HIAs, most of the municipal actors continued to include health considerations in their subsequent planning of public policies and projects. Prerequisites for effective HIA include the presence of municipal actors who are aware of the importance of their role in their local population’s health, municipal policies that include health considerations, and the municipality’s active participation in the HIA process. This study sheds light on the complexity of the factors that ensure HIA impact on municipal decision making and decisions. Key messages HIA have a significant impact to change municipality practice. The HIA process is crucial in order to have an impact on municipal projects that include health considerations.
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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 | 0.057 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".