Evaluation of the Effects of Health Impact Assessment (Hia) Practice in Monteregie
Bibliographic record
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 design is cross-sectional design were every HIA was analysed at least six month after completion. The individuals interviewed where those implicated into the HIA process (no matter at what point of the process). No exclusion criteria were applied considering that all points of view were important for this analysis. The Contribution Analysis (CA) was used to analyze the data. The study results emerged form by the interviews, the field observations and document analysis. They 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, as observed in the document, in a few of the HIAs, some recommendations were integrated into planning documents, but usually, as reported by the actor, the HIA report constituted an additional planning document and was not merged with the original planning documents. Lastly, following the HIAs, document analysis and interviews showed that 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. The particularities of each HIA process play a major role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".