Évaluation biomécanique des contraintes physiques associées aux préparations stériles dans un département de pharmacie hospitalière
Bibliographic record
Abstract
RESUME Historique : Il n’existe pas de donnees publiees sur l’ergonomie des manipulations steriles en pharmacie. Objectif : Analyser les contraintes physiques associees aux preparations steriles en pharmacie. Methode : Etude observationnelle des contraintes physiques associees aux manipulations de preparations steriles de medicaments en pharmacie hospitaliere au CHU Sainte-Justine a Montreal a l’aide d’un systeme electromyogramme, couple a des enregistrements video en sequence ; nous avons calcule le pourcentage d’utilisation musculaire (PUM) associe a des activites. Resultats : Un total de 876 manipulations de 36 types differents ont ete evaluees. Des 23 types ayant fait l’objet de plus de 10 observations, 831 manipulations ont ete utilisees pour le calcul du PUM. Le PUM moyen est inferieur a 5 % dans 39,9 % des cas, il varie entre 5 % et 10 % dans 54,2 % des cas et est superieur a 10 % dans 5,9 % des cas. On observe que les pourcentages d’utilisation musculaire moyens sont superieurs a 10 % dans les microenvironnements a manchon et membrane souple (7,1 % des mesures) ou rigide (10,9 % des mesures), mais qu’ils ne le sont pas dans le microenvironnement a fenetre rigide. Conclusion : Cette etude decrit les contraintes physiques et les pourcentages d’utilisation musculaire associes a differents types de manipulations au sein de trois types d’equipements. D’autres etudes seront necessaires afin de comparer l’ergonomie du travail dans les differentes enceintes disponibles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".