Behind the Counter: Exploring Pharmacists’ Stressors and Lessons Learned During the Pandemic in Ontario, Canada
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".