Patient experiences with influenza immunizations administered by pharmacists
Bibliographic record
Abstract
Influenza vaccination is the most effective way to reduce influenza infection and related complications. Unfortunately, vaccination coverage remains suboptimal. The addition of pharmacists as immunizers may assist in improving vaccine coverage. The experiences of patients who have received influenza vaccines from pharmacists is an important consideration for jurisdictions considering the addition of pharmacists as immunizers. We describe the reported experiences of recipients of influenza vaccinations by pharmacists in the community pharmacy setting in Nova Scotia, Canada. During the 2013-2014 influenza season, a paper-based quality assurance questionnaire was provided to interested vaccine recipients to assess their previous vaccination experiences and current experience at the pharmacy. More than 6,500 vaccine recipients completed questionnaires. The majority of respondents cited convenience as a main reason for receiving the vaccine in the pharmacy, with 50% indicating the service was better in the pharmacy and another 40% that the service was as good as elsewhere. Respondents also reported a positive environment in the pharmacy (e.g., less stressful, less exposure to sick people) as well as professionalism and knowledge of the pharmacists. Areas for improvement identified included better communication around the paperwork required (e.g., consent forms) and the wait time post-vaccination. This evaluation demonstrated that people who chose to be vaccinated by community pharmacists reported positive experiences and convenience was the primary factor for selecting a pharmacy as the site for vaccination.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".