Behind the Counter: Exploring Pharmacists’ Stressors and Lessons Learned During the Pandemic in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The onset of the COVID-19 pandemic has contributed to increased stress among healthcare professionals. Among these healthcare providers are Ontario pharmacists, who are facing new and pre-existing challenges and new stressors since the pandemic. Objectives: This study aimed to understand the stressors and lessons learned by Ontario pharmacists during the pandemic through their lived experiences. Methods: In this descriptive qualitative study, we conducted semi-structured one-on-one interviews with Ontario pharmacists virtually to learn about their stressors and lessons learned during the pandemic. Interviews were transcribed verbatim, then analyzed using thematic analysis. Findings: We reached data saturation after 15 interviews and identified 5 main themes: (1) Communication/miscommunication with the public and other care providers; (2) high workload due to staff shortage and low appreciation/acknowledgement; (3) mismatch in market demand and supply; (4) informational gaps pertaining to the COVID-19 pandemic along with rapid protocol changes; and (5) lessons learned to improve the future of pharmacy practice in Ontario. Discussion: Our study helped us gain a better understanding of the stressors pharmacists faced, their contributions, and the opportunities that arose due to the pandemic. Conclusion: Drawing on these experiences, this study provides recommendations to improve pharmacy practice and increase preparedness for future emergencies.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it