Satisfaction with student pharmacists administering vaccinations in the University of Alberta annual influenza campaign
Bibliographic record
Abstract
OBJECTIVE: To evaluate University of Alberta staff and students' acceptance of and satisfaction with receiving influenza vaccinations from student pharmacists during the university's annual influenza campaign. MATERIAL AND METHODS: A patient survey was created to collect patient demographics, influenza history and feedback on the services provided by pharmacy students and to measure willingness to receive vaccinations from a pharmacist in a community pharmacy. The 13-question survey was distributed to patients who received an influenza vaccination from a student pharmacist during the influenza campaign. KEY FINDINGS: A total of 1555 staff and students completed the satisfaction survey. Almost all (n = 1533, 99%) survey participants were satisfied or very satisfied with the service provided by student pharmacists. A total of 1437 (92%) participants agreed or strongly agreed that based on this experience, they would be willing to receive vaccinations from a pharmacist in a community pharmacy and 1526 (98%) participants rated their overall experience at the flu clinic as very good or excellent. CONCLUSIONS: Positive responses to the survey suggest that University of Alberta staff and students are satisfied with the service provided by student pharmacists. Their willingness to receive vaccines from a pharmacist in a community pharmacy highlighted public acceptance of the expanding role of pharmacists as immunizers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".