The Elaboration of an Intersectoral Partnership to Perform Health Impact Assessment in Urban Planning: The Experience of Quebec City (Canada)
Bibliographic record
Abstract
Health impact assessments (HIA) allow evaluation of urban interventions' potential effects on health and facilitate decision-making in the urban planning process. However, few municipalities have implemented this method in Canada. This paper presents the approach developed with partners, the process, and the outcomes of HIA implementation after seven years of interinstitutional collaborations in Quebec City (ten HIA). Using direct observation and meeting minutes, information includes: perceived role of each institution taking part in HIA beforehand, how the HIA process was implemented, if it was appreciated, and which outcomes were observed. The intersectoral interactions contributed to the development of a common language, which sped up the HIA process over time and fostered positive collaborations in unrelated projects. It was an effective tool to share concerns and responsibilities among independent institutions. This experience resulted in the creation of an informal group of stakeholders from four different institutions that perform HIA to this day in collaboration with researchers.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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".