Influenza vaccination in community pharmacy: A cross-sectional survey of Canadian adults’ knowledge, attitude and beliefs
Bibliographic record
Abstract
Background: In Canada, influenza vaccination rates are below recommended targets, with pharmacies the leading setting for vaccine administration. This work aimed to determine the Canadian public's current knowledge, attitudes and practices related to pharmacy-based influenza vaccination services. Methods: We surveyed 3000 Canadian residents aged ≥18 years using a cross-sectional, self-reported, online structured questionnaire between December 5 and 21, 2022. A representative survey population was recruited from the Léger Opinion (LEO) consumer panel. Data were weighted by age, region and gender, based on 2021 census data. Results: During the 2022-2023 season, 56.6% (95% confidence interval [CI], 54%-59.2%) of respondents reported receiving an influenza vaccine at a pharmacy, including 57.5% (95% CI, 54.2%-60.8%) of respondents considered to be at high risk of complications from influenza. Among respondents previously vaccinated at a pharmacy, 94.1% (95% CI, 91%-97.2%) were satisfied with the experience, citing convenience, accessibility and availability as factors influencing their decision. Among all respondents, 29.3% (95% CI, 27.5%-31.1%) reported that a pharmacist's recommendation for the influenza vaccine would affect their decision to be vaccinated, yet only 10.4% (95% CI, 5.9%-15%) who had discussions with a pharmacist specifically discussed the importance of influenza vaccination. Conclusion: Canadians are satisfied with pharmacy-based influenza vaccinations and value pharmacist recommendations. Pharmacists have an opportunity to boost influenza vaccination coverage in Canada by providing counselling on the importance of influenza vaccination to those seeking their advice on other health care needs, including younger adults and those with risk factors for serious illness from influenza.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".