Choix et limites de l’allocation des ressources en santé. Pourquoi est-ce nécessaire ? Pourquoi est-ce si difficile ?
Bibliographic record
Abstract
L’équité devrait être considérée comme la valeur centrale de tout système de santé. Les implications de cette valeur sont nombreuses. L’une d’entre elles, probablement la moins avouée et la plus épineuse, est de déterminer les limites acceptables de l’allocation des ressources en santé dans un monde de rareté où il faut de plus en plus faire des choix. Cet article présente un certain nombre de justifications générales en faveur d’un rationnement équitable des ressources en santé et rappelle les difficultés pratiques et théoriques qui peuvent expliquer pourquoi, en ces matières, il nous reste encore un long chemin à parcourir.
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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 | 0.046 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| 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".