Healthcare providers’ awareness, attitudes, beliefs, and practices surrounding pharmacist-administered vaccination: Insights after a two-year intervention
Bibliographic record
Abstract
Despite the expanding scope of pharmacists’ practice to include immunization, vaccine coverage among adults in Canada remains below national targets. We conducted online surveys of healthcare providers (HCPs) that provide vaccines in Nova Scotia and New Brunswick, Canada, to determine their perceptions about pharmacists as immunizers. A validated and pre-tested cross-sectional survey was distributed to pharmacists, physicians, and nurses via professional associations, health authorities, and social media. Of the 223 respondents, 41.3% were pharmacists, 24.7% nurses and 34.1% physicians. Pharmacists described experiencing more social (81.0%) and professional (62.0%) pressure to administer vaccines, as compared to nurses (58.2% and 27.9%) and physicians (60.0% and 9.1%) (p = .004 and p < .001). While pharmacists were well-informed and motivated to vaccinate, they faced logistical barriers including working in a solo practice setting (58.9%) and lack of tools to identify unvaccinated individuals (76.0%), as compared to nurses (20.0%, 51.7%) and physicians (26.6%, 25.0%) (p < .001). Support for pharmacists’ role in administering vaccines for different age groups was highest among pharmacists (88.0% for adults, 88.0% for 5–18-year-olds, and 62.7% for children <5 y), followed by nurses (71.4%, 57.1%, and 46.9%), and physicians (60.0%, 47.1%, and 28.6%) (p ≤ .001). Our findings suggest that pharmacists’ roles as immunizers may be facilitated by policies supporting: a pharmacist-accessible vaccine registry, adequate compensation and staffing support, specialized training for vaccinating young children, and advocacy from physicians and nurses. Research into pharmacists’ evolving professional identity and how their growing roles as vaccinators are perceived by other HCPs is recommended.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".