Post-pandemic opportunities for Canadian pharmacists: tackling mental health challenges and policy gaps through a social-ecological lens
Bibliographic record
Abstract
Background: The COVID-19 pandemic was a stressful time for healthcare workers, including pharmacists. The pandemic brought new challenges compounded by pre-existing ones. As Canadian pharmacists assume greater responsibilities with the expansion of their scope of practice, it is essential to examine their mental health needs to ensure their success in the post-pandemic era. Guided by the Social Ecological Model, this qualitative study explored the mental health needs of pharmacists. Methods: Registered pharmacists across Canada were involved in one-on-one interviews, dyadic interviews, or focus groups. Data were transcribed and then analyzed using reflexive thematic analysis. Results: A total of 22 pharmacists across Canada were interviewed for this study. At the individual level, the need to prioritise mental health and maintain boundaries has emerged as a prominent theme. At the organisational level, (1) the need for employee retention strategies and quality staff and (2) the need to improve internal and external communication were two emerging themes. The need to perceive pharmacists' roles beyond dispensing was the central theme at the community level. Finally, the primary theme at the policy level was the need to integrate pharmacies within the broader healthcare system. Conclusion: With adequate resources, structural support, and targeted investments, pharmacists are well-positioned to alleviate healthcare pressures and expand their roles in meaningful and sustainable ways.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.024 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".