Pharmacists’ Mental Health during the First Two Years of the Pandemic: A Socio-Ecological Scoping Review
Bibliographic record
Abstract
Healthcare workers have been under a great deal of stress and have been experiencing burnout throughout the COVID-19 pandemic. Among these, healthcare workers are pharmacists who have been instrumental in the fight against the pandemic. This scoping review examined the impact of the pandemic on pharmacists' mental health and their antecedents using three databases (CINAHL, MEDLINE, and PsycINFO). Eligible studies included primary research articles that examined the mental health antecedents and outcomes among pharmacists during the first two years of the pandemic. We used the Social Ecological Model to categorize antecedents per outcome. The initial search yielded 4165 articles, and 23 met the criteria. The scoping review identified pharmacists experiencing poor mental health during the pandemic, including anxiety, burnout, depression, and job stress. In addition, several individual, interpersonal, organizational, community, and policy-level antecedents were identified. As this review revealed a general decline in pharmacists' mental health during the pandemic, further research is required to understand the long-term impacts of the pandemic on pharmacists. Furthermore, we recommend practical mitigation strategies to improve pharmacists' mental health, such as implementing crisis/pandemic preparedness protocols and leadership training to foster a better workplace culture.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".