Perception de la pharmacovigilance par les futurs pharmaciens hospitaliers belges, français, québécois et suisses
Bibliographic record
Abstract
Comparer la perception de la pharmacovigilance par les résidents en pharmacie hospitalière belges, français, québécois et suisses.Étude descriptive prospective sous forme de sondage sur surveymonkey.comexpédié par courriel en mars 2014 à 229 résidents en pharmacie hospitalière de 4 pays francophones : Belgique, France, Québec et Suisse.Identification des variables pertinentes à partir d'une revue de la littérature.Choix de 18 questions fermées et 1 question ouverte, organisées en 5 sections : données démographiques (2 questions), formation et pratique (8 questions), attitude face à un EIM (6 questions), obstacles à la déclaration d'EIM (1 question) et mesures pour améliorer le taux de déclaration (2 questions).Validation par pré-test de 5 résidents en pharmacie hospitalière et relecture par un panel de pharmaciens hospitaliers.Prise en compte des suggestions pour modifier le questionnaire avant administration.Questionnaire et traitement des réponses strictement anonymes.Science d'observation et de surveillance des effets indésirables médicamenteux (EIM), la pharmacovigilance repose sur la notification spontanée des professionnels de santé.Intégrée à la pratique des pharmaciens, cette activité devrait être une partie incontournable de la formation des résidents en pharmacie hospitalière.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".