Pour une formation continue basée sur des preuves
Bibliographic record
Abstract
Resume Le succes d’une pratique pharmaceutique repose en partie sur la capacite des pharmaciens de mettre a jour leurs connaissances et leurs habiletes, compte tenu de l’evolution rapide de la science du medicament. L’objectif du present article est de se pencher sur les activites de formation continue des pharmaciens et de presenter un outil de documentation de ces activites en etablissement de sante. L’utilisation d’un bilan de formation facilite la documentation des activites de formation. Il peut contribuer a accroitre la diffusion des connaissances et autres elements retenus lors de cette participation et peut faire partie des outils de gestion d’un departement de pharmacie. Abstract Given the fast evolution of the science of drugs, the success of a pharmaceutical practice lies mainly on the pharmacists’ ability to keep their knowledge and expertise up to date. This article will take a closer look at continuing education activities for pharmacists and introduce a documenting tool for health establishments with regards to these activities. The use of an educational assessment process eases the documentation of training activities. This process can contribute to increase knowledge and other learned information transmission, in the course of this involvement, and can be part of a pharmacy department’s management tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".