A qualitative exploration of pharmacists' roles in centralised vaccination centres during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic prompted rapid implementation of public health interventions aimed at protecting population health. In Ireland, mass vaccination was integral to the national response. Pharmacists played key roles in the safe delivery of vaccines within centralised vaccination centres (CVCs), particularly medicines management and vaccine stewardship. This study aimed to explore pharmacists' motivations for taking on these roles, explore their experiences and identify how their learning may inform the future development of pharmacy practice and education in Ireland, with a view to strengthening preparedness for future public health emergencies. Methods: A qualitative case study methodology design was employed, using semi-structured interviews for data collection. Fourteen pharmacists were recruited, with eleven included in the final analysis. Interviews were transcribed verbatim and analysed thematically.Findings.Three primary themes were developed from the data: (1) A Sense of Duty and Opportunity, (2) Navigating the Frontline: Challenges and Adaptation, and (3) Professional Growth and Future Directions. Pharmacists reported some challenges working in high-pressure, fast-changing, multidisciplinary environments. Their experiences highlighted the evolving scope of pharmacy practice and the unique contribution pharmacists can make within multidisciplinary teams (MDTs) in national-level public health responses. Conclusions: Pharmacists' involvement in CVCs provided opportunities to develop and showcase their professional competencies in MDTs, notwithstanding challenges associated with the role. The findings also emphasise the importance of effective multidisciplinary teamwork and mutual respect among healthcare professionals. A continued focus on interprofessional learning and practice, alongside expansion and recognition of the pharmacists' roles, may enhance preparedness for future public health emergencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".