Evaluation of an e-Learning Program for Community Pharmacists for Dispensing Emicizumab (Hemlibra) in France: Nationwide Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Since June 2021, patients with hemophilia A with antifactor VIII inhibitors and those with severe hemophilia A without antifactor VIII inhibitors treated with Hemlibra have had to choose between a community or hospital pharmacy. The French reference center for hemophilia developed the HEMOPHAR e-learning program for community pharmacists for dispensing emicizumab. OBJECTIVE: This study aims to evaluate the efficiency and safety of this new care pathway by assessing the HEMOPHAR e-learning program. METHODS: The methodology is based on Kirkpatrick's model for evaluating the immediate reaction of trained community pharmacists (level 1), their level of acquired knowledge (level 2), and their professional practice after 3 months of dispensation (level 3). RESULTS: The HEMOPHAR e-learning program reached a large audience, with 67% (337/502) of the eligible community pharmacists following it. The immediate reaction was overall satisfying. High rates of engagement were reported with 63.5% (214/337) to 73.3% (247/337) of completed training modules, along with high rates of success with quizzes of 61.5% (174/337) to 95.7% (244/337). We observed that 83.9% (193/230) of the community pharmacists needed less than 2 attempts to pass the quiz of the module related to professional practice, while the other quizzes required more attempts. Advice on compliance and drug interactions were most frequently provided to patients by the community pharmacists. CONCLUSIONS: This study suggests ways to improve the training of community pharmacists and to optimize coordination with treatment centers. This study also reports on the feasibility of switching to a community pharmacy in a secure pharmaceutical circuit, including in the context of a rare bleeding disease. TRIAL REGISTRATION: ClinicalTrials.gov NCT05449197; https://clinicaltrials.gov/study/NCT05449197. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/43091.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".