L’évaluation d’impact sur la santé (EIS) : une démarche intersectorielle pour l’action sur les déterminants sociaux, économiques et environnementaux de la santé
Bibliographic record
Abstract
Health Impact Assessment (HIA) is a practice that has grown in popularity worldwide, since the end of the 1990s. Originally used in the framework of Environmental Impact Assessments (EIAs), HIA has become enriched through the addition of knowledge and principles based on the social determinants of health and the tackling of health inequalities, and has been brought to bear on the policy-planning process at all levels of government. HIA has three overlapping objectives: to assess the potential effects of a policy on health, to encourage citizen and stakeholder participation in the impact analysis process, and to inform the decision-making process. This article briefly defines HIA; defines its standardized process in successive steps, which allows users to give structure to their actions and to establish the steps to be followed (detection, framing, analysis, recommendations and evaluation); and offers three examples of HIA in three different situations: the Geneva canton of Switzerland; Rennes, France; and in the Montérégie region of Quebec, Canada. Together, these illustrations show that HIA is a promising strategy to influence local decisions and to integrate health into projects and policies at the local and regional levels.
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.
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 | 0.104 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| 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 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".