Leveraging the role of pharmacists in vaccine education and uptake among newcomers to Ontario
Bibliographic record
Abstract
Background: Canada's newcomer population has grown substantially over the past several years and will continue to increase. Newcomers are known to have lower vaccination coverage compared with the general population as they face unique barriers to vaccine education and access. This contributes to increased risk of vaccine-preventable diseases among newcomers. Pharmacists are the most accessible health care provider in the community, placing them in an ideal position to assist newcomers with vaccine education, access, and uptake. Methods: Using a qualitative descriptive methodology, we sought to understand the perceptions, barriers, and facilitators pharmacists experience when providing vaccine services to newcomers. We completed semistructured interviews with 12 pharmacists who practice patient care in Ontario. Results: The following 3 major themes were uncovered: (1) pharmacists are accessible health care providers in the community who are willing and motivated to provide vaccine services to newcomers to Ontario; (2) pharmacists do not proactively engage in vaccine education, but they can capitalize on opportunities to provide vaccine education to newcomers by incorporating conversations into routine pharmacy services, and (3) educational materials can support pharmacists and newcomers by addressing barriers and facilitators pharmacists encounter when providing these services. Discussion: The main findings from our research study indicate that pharmacists have the potential to improve vaccine education, access, and uptake among newcomers. Educational materials can support pharmacists by addressing the barriers and facilitators they encounter when providing these services to the newcomer population. Conclusion: 2025;158:xx-xx.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".