Health impact assessment—insights from the experience of Québec
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health Impact Assessment (HIA) is an approach used to evaluate policies, programs, and projects from the perspective of their potential effects on the health of individuals, communities, and vulnerable population groups. HIA is generally applied to proposals in fields that do not specifically target health such as urban and transportation planning, natural resources, and large infrastructure. The use of HIA has been growing in Canada but several studies indicate that there are gaps in legislation, policy, and regulation that inhibit its consistent application. The objective of this paper is to review the experience in the Province of Québec, where HIA has been embedded in legislation since 2002 and explore the way that it has influenced the advancement of HIA in that province and identify lessons that could be applied to other Canadian jurisdictions. Particular attention is paid to the institutionalization of HIA in public health agencies. The insights are considered for other provinces, territories, and municipalities as well as in the context of the Federal Impact Assessment Act (2019).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 it