L’évaluation d’impact sur la santé, un outil pour promouvoir des politiques climatiques favorables à la santé
Bibliographic record
Abstract
INTRODUCTION: Potential impacts of climate change on health are increasingly studied due to the diversity of the associated risks (heatwaves, air pollution, water- and vector-borne diseases). Consequently, adaptation and mitigation strategies, including tools, have been developed by different cities, states, and organizations to assess the effects of climate change on health. OBJECTIVE: Health impact assessment (HIA) is a tool that could be used to assess the potential health impacts of climate change policies before their implementation. The objective of this study is therefore to analyze the way HIA is used in the development of these policies. METHOD: A scoping review of grey and scientific literature in French and English (period: 1990-2019) allowed us to identify 35 articles and reports, with 6 using HIA specifically. The areas of HIA application related to transport, urban planning or the building sector. The main health issues addressed in these HIAs concerned air, noise, physical activity, urban heat islands, green spaces, and functional diversity. RESULTS: These studies have shown that HIA is an approach that can facilitate cross-sectoral collaboration, and its flexibility allows for its application to adaptation and mitigation policies, as well as at several spatial scales (cities, regions). DISCUSSION: The principal limitation in this approach relates to uncertainties associated with quantifying projected impacts.
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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 | 0.122 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".