Between the Dynamics of Quality of Care Improvement and the Administrative Conformity: Behaviours of Participating Hospitals in the French Pay-for-Performance (P4P) Program
Bibliographic record
Abstract
Le paiement à la performance (P4P) continue de se développer dans les systèmes de santé des pays industrialisés, malgré des preuves encore assez limitées de son efficacité. Cette étude propose de comprendre le comportement des établissements de santé face à ce nouveau mode de paiement en se basant sur l'expérimentation de P4P hospitalier conduite en France. Nous avons, pour cela, combiné une approche quantitative basée sur un questionnaire auprès des établissements participants et une analyse qualitative dans neuf établissements afin de mieux identifier les processus à l'œuvre. L'étude montre que des actions correctives ont été réalisées dans certains établissements mais que les effets du programme sur l'organisation restent en fait assez limités puisqu'ils s'opèrent davantage à la marge. Les comportements semblent être essentiellement le reflet d'une volonté de conformation des organisations aux attentes de la tutelle, sans transformations organisationnelles majeures. Il sera toutefois intéressant de voir comment des perceptions différentes structurent ces comportements sur le long terme.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".