Charge de travail en pharmacie clinique chez les patients médicaux et chirurgicaux : effet selon la répartition des patients, la complexité des pathologies et la classification des diagnostics de la maladie
Bibliographic record
Abstract
Jean-Francois Bussieres, B.Pharm., M.Sc., MBA, FCSHP, est chef du Departement de pharmacie et de l’Unite de recherche en pratique pharmaceutique (URPP) du CHU Sainte-Justine et professeur titulaire de clinique a la Faculte de pharmacie de l’Universite de Montreal (en moyenne 35 lits par pharmacien au TNH contre en moyenne 39 lits par pharmacien au WH) sur le quart de travail de jour du lundi au vendredi. On a prealablement confirme par observation directe le nombre d’annees d’experience des pharmaciens (c’est-a-dire recemment diplome jusqu’a 20 annees de pratique). Les pharmaciens etaient affectes aux unites de soins en tenant compte des besoins de l’organisation sans tenir compte de la realisation de cette etude.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".