Réflexions sur un modèle de gestion et d’approbation des ordonnances collectives en établissements de santé
Bibliographic record
Abstract
Resume Objectif : Cet article decrit les pistes de reflexion sur un modele de gestion et d’approbation des ordonnances collectives en etablissements de sante. Mise en contexte : A la lumiere de la revue de la documentation et de la situation locale, on presente les resultats d’une enquete aupres de 13 etablissements de sante et on propose un modele conceptuel des ordonnances liees aux soins et a la recherche et des ordonnances collectives incluant dix recommandations propres a la gestion du medicament. Conclusion : Il existe peu de donnees concernant les modeles de gestion et d’approbation des ordonnances collectives en etablissements de sante. Abstract Objective: This article describes the thinking that underlies a model for the management and the approval of collective prescriptions in healthcare establishments. Context: In light of a review of existing documentation and the local situation, we present the results from a survey of 13 healthcare establishments. We propose a conceptual model for orders related to research and to the care of the patients and for collective prescriptions including 10 recommendations for medication management. Conclusion: Few data exist concerning models for the management and approval of collective prescriptions in healthcare establishments. Key Words: collective prescription; pharmacy and therapeutics committee; Council of physicians; dentists and pharmacists.
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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 | 0.049 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".