Améliorer la performance en SST : les résultats vs les prédicteurs
Bibliographic record
Abstract
Malgré leur intérêt initial, les indicateurs réactifs traditionnels de mesures en SST (e.g. taux de fréquence et de gravité) se révèlent souvent inappropriés, dans certains cas inexacts, voire même contreproductifs pour améliorer la performance en matière de SST des établissements. Cet article propose plutôt de mesurer un ensemble de variables prédictives qui s’inscrivent dans un modèle hiérarchique de progression des préoccupations en SST en s’appuyant sur une philosophie d’apprentissage plutôt qu’une philosophie de performance. L’instrument de mesure développé prend la forme d’un questionnaire d’autodiagnostic qui s’intéresse aux variables prédictives susceptibles avoir une influence sur les résultats en matière de SST. L’intérêt suscité par le questionnaire au sein des milieux qui l’ont utilisé justifie la poursuite de travaux visant à procéder à sa validation scientifique.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".