Bibliographic record
Abstract
Resume Objectifs : Decrire la demarche du pharmacien qui participe a l’elaboration d’un plan organisationnel visant la preparation optimale de vaccins, lorsqu’une main-d’oeuvre diversifiee doit en produire de grandes quantites en peu de temps, tout en assurant la qualite et la securite du vaccin. Mise en contexte : La campagne de vaccination antigrippale annuelle a permis de vivre un exercice de vaccination de masse en vue de se preparer a une pandemie. Afin d’optimiser l’utilisation des ressources humaines disponibles et d’assurer une productivite accrue dans la preparation des doses de medicaments, les pharmaciens devaient entamer une reflexion de fond sur la technique de preparation ainsi que la securite et la qualite des doses preparees. De plus, des divergences existent entre la pratique habituelle lors des campagnes de vaccination annuelle ou scolaire et les recommandations emises par les diverses instances officielles. Conclusion : Le pharmacien s’investit encore peu dans les campagnes de vaccination. Pourtant, son approche visant a assurer des medicaments de qualite, son experience de travail et son expertise particuliere dans la preparation des medicaments et du circuit du medicament, notamment quant a la preparation, a l’entreposage et au transport de ces derniers, lui permettent d’apporter des solutions innovatrices et efficaces face au defi de taille que represente la vaccination de masse. De plus, la pharmacie offre une main-d’oeuvre qualifiee supplementaire.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".