A tailored approach for health impact assessment in a context of uncertainty and resource constraints: a case study in Québec
Bibliographic record
Abstract
Health impact assessment (HIA) can magnify the positive impacts of health promotion in practice, but practitioners face many challenges in implementing it, from the difficulties of intersectoral work to a lack of resources. These challenges require flexibility and sometimes involve adapting best practices in health promotion and the typical HIA process. This article presents an agile approach to conducting HIA when the window of opportunity is short, resources are scarce, and the expected value of appraisal is uncertain. Accordingly, this approach prioritizes the recommendations step over the appraisal step, while proposing an iterative and co-constructive process involving a limited number of stakeholders. The approach is exemplified through a case study of an HIA conducted in Québec on a guide promoting green, active and safe transportation networks. We argue that the adapted method can, in certain circumstances, lead to better results than a typical HIA. The article also suggests contextual criteria that allow HIA practitioners to assess whether this approach is suitable for their needs.
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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 | 0.001 | 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".