Community Pharmacists and Influenza Vaccination: Opportunities and Challenges From a Public Health Perspective
Bibliographic record
Abstract
Context: In Quebec, Bill 31, adopted on March 18, 2020, extended vaccination to pharmacists. Despite many advantages, this new practice comes with public health issues reinforced in the context of COVID-19. Therefore, it is essential to understand the opportunities and challenges of the participation of community pharmacists in influenza vaccination, from a public health perspective by (i) describing the year of 2020-2021 influenza vaccination offer, (ii) its opportunities and challenges, and (iii) its impact on the accessibility of this service newly offered by pharmacists to the most vulnerable people. Methods: This research is a case study from one of the most affected areas by COVID-19 in Canada: Laval. Our method combines documentary analysis and semi-structured interviews with health professionals and public health actors (n = 23). Researchers used a thematic analysis to analyze these results. Results: Most partners (pharmacists, public health administrators) underlined multiple opportunities of this new practice, ie, pharmacists who can vaccinate, particularly for chronically ill patients. However, structural and strategical challenges remain. More specifically, vaccination seemed to only rely on a “first come, first served” basis, which questions public health objectives of vaccination, such as equitable access. Conclusion: The introduction of new actors, such as pharmacists, represents a major opportunity to improve vaccination coverage and reduce the burden of COVID-19 on the health system. However, this delegation of a public health activity to the private sector undoubtedly requires closer coordination with public health institutions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".