Willingness of Canadian community pharmacists to adopt a proactive life-course approach to vaccination services
Bibliographic record
Abstract
BACKGROUND: Most Canadian jurisdictions authorize pharmacists to administer vaccines, with differences in vaccine and patient age eligibility. Vaccination rates could be further optimized if pharmacists took a more proactive role in life-course vaccine screening and administration. Health professional vaccine fatigue following the COVID-19 pandemic may negatively impact service delivery. OBJECTIVES: To assess vaccine fatigue among Canadian pharmacists, understand their willingness to provide proactive life-course vaccination services and identify associated vaccine practice facilitators. METHODS: One-on-one interviews were conducted with pharmacists recruited through a national community pharmacist Facebook group. Purposive sampling was used to select a diverse sample considering gender, province, and years of practice. Online interviews were conducted using a semi-structured guide with questions about vaccination experiences, perceptions of assuming a more proactive vaccinator role for adults and children, and current level of fatigue related to offering vaccination services. Interviews were audio-recorded, transcribed, and coded independently by 2 researchers; content analysis was used to identify themes. RESULTS: In spring 2023, interviews were conducted with 24 pharmacists from 5 Canadian provinces. Participants were receptive to a more proactive vaccinator role, feeling that vaccine fatigue had lessened, but strongly advocated for system and practice modifications to facilitate successful implementation. They emphasized the need for patient vaccination history access, the ability to administer all publicly funded vaccines, and fair compensation. Participants requested the development of electronic tools that connected to pharmacy systems that helped them navigate complex vaccine guidelines and clinical decision making, and the required documentation/billing. They also spoke of logistical concerns related to the incorporation of vaccination into their workflow and adequate staffing. Most participants were willing to vaccinate younger children if legislated age limits were lowered, provided they were trained and compensated appropriately. CONCLUSION: Pharmacists are interested in furthering their vaccination services offerings, including proactive screening and vaccination of young children.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".