Unmasking the Unrecognized: Exploring Registered Pharmacy Technicians’ Stressors During COVID-19 Through a Demands-Resources Inquiry and Looking Ahead
Bibliographic record
Abstract
Canadian registered pharmacy technicians (RPTs) were vital in supporting pharmacy operations during the pandemic. However, they have received little attention during or pre-pandemic. This study aimed to identify and understand the stressors experienced by Canadian RPTs during the pandemic and gain insights on lessons learned to help improve the profession. Through a descriptive qualitative design, virtual semi-structured focus groups were conducted with RPTs who were recruited through various sampling methods across Canada. Data were inductively analyzed and then deductively; themes were categorized using the Job Demands-Resources (JD-R) model. We reached data saturation after 4 focus group sessions with a total of 16 participants. As per the JD-R model, job demands included: (1) increased work volume and hours to meet patient demand; (2) drug shortages and managing prescriptions increased due to influx of orders coinciding with restricted access to medications; (3) fear of the unknown nature of COVID-19 met with frequent change in practices due to protocol changes and ineffective communication; and, (4) the pandemic introduced several factors leading to increased staff shortages. Themes pertaining to resources included: (1) poor incentives and limited access to well-being resources; (2) limited personal protective equipment delaying work operations; (3) and a general lack of knowledge or appreciation of the profession impacting work morale. Lessons learned from the pandemic were also provided. Overall, our findings revealed an imbalance where RPTs experienced high job demands with limited resources. Improved leadership within pharmacies, including improved communication between team members, is required. Furthermore, efforts to highlight and recognize the work of RPTs to the public is important to help improve enrollment, especially with their recent scope of practice expansion.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".