Exploring the well-being of community pharmacy professionals, turnover intention and patient safety: Time to include operational responsibility
Bibliographic record
Abstract
Background: The COVID-19 pandemic added significant occupational pressures on community pharmacists. The objective of this research project was to investigate the level of distress and burnout among community pharmacy professionals and its association with their retention within their occupation as well as patient safety outcomes. Method: We conducted a cross-sectional study on 722 community pharmacy professionals from all Canadian provinces using an online survey, including scientifically validated measures. The data were analyzed using multiple regression analysis. Results: In Canada, 85% of community pharmacy professionals reported their mental health had suffered since the COVID-19 pandemic. Younger pharmacy professionals and those paid hourly reported a worsening level of mental health and an increasing level of turnover intention. Pharmacists with more dynamic/disrupted work schedules and those working for a large pharmacy chain (more than 25 pharmacies in Canada) reported lower levels of mental health quality. Pharmacy professionals working in pharmacies that are open more than 70 hours a week reported a lower level of patient safety culture. Pharmacists' mental health was the significant predictor of their turnover intention, implying a heightened risk to professional effectiveness and retention. Compassion satisfaction was positively associated with patient safety culture and safety behaviour, while compassion fatigue and secondary traumatic stress were significantly associated with pharmacists' level of risk-taking behaviours. Conclusion: This study emphasized the importance of prioritizing the mental health and well-being of community pharmacy professionals and demonstrated individual and systemic factors predicting the well-being and turnover intention of community pharmacists, as well as patient safety culture within their pharmacy. This research makes a case to consider actions to shift the monitoring focus from community pharmacists (also known as "individual responsibility") to community pharmacies (also known as "operational responsibility") for managing patient safety. Additionally, community pharmacists should be provided with the professional autonomy to affect their working conditions and alleviate the stress that has the potential to negatively affect the delivery of care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".