Patient Perceptions on Receiving Vaccination Services through Community Pharmacies
Bibliographic record
Abstract
(1) Introduction: Pharmacists are medical professionals who play an active role in the protection of public health. Since 2021, pharmacists with an appropriate certification have been authorised to administer vaccines against COVID-19. (2) Objective: The objective of this study was to ascertain the perceptions of patients about receiving vaccinations through community pharmacies. (3) Material and methods: This study was conducted in 2021. The research tool was an anonymous questionnaire published on the websites of patient organisations. Ultimately, 1062 patients participated in this study. (4) Results: This study shows that most of the respondents find community pharmacies more accessible than outpatient clinics (85.3%). Sixty-one percent of the respondents stated that getting vaccinated at pharmacies would be less time consuming than at outpatient clinics. Nearly every third respondent (29.5%) declared that they would get vaccinated if they received such a recommendation from a pharmacist. Fifty-six percent of the respondents were of the opinion that the administration of vaccines by pharmacists would relieve the burden on medical staff and the healthcare system. (5) Conclusions: Polish patients participating in the study have a positive attitude towards the implementation of vaccination services in community pharmacies as an effective way of combating infectious diseases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".