Développement d’un système de gestion de la performance des soins dans un centre hospitalier universitaire suisse
Bibliographic record
Abstract
Studies show high variability in the quality of care and a significant incidence of adverse events. The care management direction of a university hospital center (CHU) has developed a care performance measuring system. The aim of the article is to present the different development stages of this system. The authors used May's Normalization Process Theory, which focuses on factors influencing the engagement of individuals, groups, and organizations in sustaining change.The CHU's approach led to the following results : 1) reaching a consensus on performance concept and identifying five areas of performance, 2) selection of 12 priority indicators to assess performance, 3) measures development, 4) setting up the method of collecting information 5) creation of a mechanism for analyzing the results by care teams and 6) dissemination of results via dashboards.The approach focuses on strategies for mobilizing managers and health care teams. Specific recommendations relate to the need to provide expert resources, review clinical guidance and ensure accountability of health care providers.
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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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".