Pharmacists as vaccinators: An analysis of their experiences and perceptions of their new role
Bibliographic record
Abstract
OBJECTIVES: In 2012, Ontario pharmacists were granted immunization privileges to administer influenza vaccines to the public. This study explored the experiences and practices of pharmacist vaccinators and the impact of vaccination pain on their practice. METHODS: Semi-structured interviews were conducted with 12 pharmacists in the Greater Toronto Area. Transcribed interview data were coded and analyzed via thematic analysis. KEY FINDINGS: Data analysis revealed 4 main themes: 1) expanded scope of practice as an enhancement and challenge to relationships; 2) professional satisfaction and workload demands; 3) knowledge and attitudes regarding pain and fear; 4) practices regarding pain and fear management. Pharmacists reported satisfaction in their new role, despite the associated increased workload. Pharmacists felt that vaccination pain was not a key consideration when administering vaccines and that pain management was generally not necessary. Fear was identified as more important than pain and vaccinating children was challenging and time intensive. Pharmacists' main focus was on injection techniques in managing pain. CONCLUSION: These findings suggest that pharmacists are accepting of their role as vaccinators but more research is needed to examine impact on workload and pharmacist knowledge and perceptions of injection-related pain and fear.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".