Helping Québec Pharmacists Seize the Vaccination Service Opportunity: The Pharmacy Best Practice Workshops
Bibliographic record
Abstract
Vaccinations are a safe and effective way to protect against infectious diseases. The World Health Organization estimates vaccines have saved more lives than any other interventions and every year about two to three million deaths are averted worldwide through immunization. To improve vaccination coverage, pharmacists have been increasingly involved in immunization roles in their communities-as advocates, educators, and immunizers. Community pharmacy-based vaccination services have increased both in the number of immunization providers and the number of sites where patients can receive immunizations. In Canada, health care is under provincial legislation-and so, there are distinct differences in scope of pharmacist practice across the country. Prior to the COVID-19 outbreak in early 2020, in Québec, Canada's second-largest province, pharmacists did not have the authority to administer vaccines. To help prepare pharmacists in Québec to become immunizers, we developed and deployed a series of accredited workshops. In these facilitated workshops, pharmacists were able to share best practices that may lead to providing effective vaccination services, identify common competency gaps, discuss effective patient communication skills, and determine how to target the most vulnerable population groups. Participants were also asked to evaluate the workshop. Our results indicate the evaluation was very reliable in measuring participant satisfaction (Cronbach's α = 0.94) and pharmacists commented that the workshops' learning outcomes exceeded their expectations, and the topics covered were relevant and applicable. The evaluation also asked participants to identify weaknesses of training, so future educational interventions can be planned accordingly. We believe this work will contribute to the continual growth and advancement of the pharmacy profession in Canada.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".