Quelle stratégie peut développer une Agence régionale de santé pour réduire les inégalités sociales de santé ?
Bibliographic record
Abstract
Reducing social inequalities in health (SIH) is a key priority for the Provence Alpes Côte d'Azur Regional Health Agency (Paca ARS). The actions and objectives defined in the regional health project were divided into a two-way table (determinants/policies by target population) to verify the consistency and extent of such measures. Sustaining actions of the ARS, alone or in partnership on the determinants of SIH and their effects, target three distinct levels of intervention: in the scope of its own jurisdiction, as a resource for other actors, including support of action research and finally in the context of partnership approaches. It has developed fine measurement and monitoring tools and has supported the development of a continuing education e-learning programme developed with partners in Paca and Quebec. However, further efforts are needed to develop actions on fundamental determinants, including environmental determinants and more effective implementation of this policy. The objectives of certain territorial health programmes designed to make local primary care structures responsible for the population concerned are very promising.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".