Démarche pour la mise à niveau de soins pharmaceutiques en établissement de santé : l'exemple de l'immunisation
Bibliographic record
Abstract
AIM: This article describes an approach to upgrading pharmaceutical care in healthcare facilities. METHODS: This is a descriptive study supporting the upgrade of pharmaceutical care in the field of immunization [blinded for review], in a 500-bed mother-child university hospital. Our approach consisted of 3 steps: (1) a review of the literature, (2) a description of the profile of the sector and (3) a description of upgrading of pharmacists' practices in immunization. RESULTS: A total of 19 articles were reviewed. No specific pharmaceutical activity based on very good quality data was identified (A).However, eight pharmaceutical activities based on good quality data (B) or with an insufficient level of proof (D) related to immunization practices were identified. A review of pharmaceutical activities (2013-2014) accounted for an annual expenditure of $ CAN 4,227 for vaccines compared to $ SCAN 27,633,944 for all drugs. A total of 9,254 doses of vaccines were prescribed for 3,544 patients. The planned revision of immunization activities includes a medication reconciliation process targeting immunization requirements, systematic consultation of pharmacy dispensing records for patients hospitalized for more than one month to ensure adherence to the Quebec Immunization Protocol, systematic reporting of vaccine adverse reactions, and implementation of information reviews about new vaccines. CONCLUSION: Few data are available concerning the impact of pharmacists in immunization. This descriptive study proposes a number of steps designed to upgrade pharmaceutical practices in a university hospital.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".