Prescription for change: Unveiling burnout perspectives among pharmacy leaders
Bibliographic record
Abstract
Background: Burnout among pharmacists is increasingly pertinent, with growing demand for effective interventions. Burnout can lead to reduced productivity, increased job turnover, medical errors, poor patient satisfaction, and other negative outcomes for patients and providers. Growing attention to burnout in the pharmacy profession highlights the need for personal, organizational, and systemic solutions. However, the uptake and relative efficacy of different approaches remain unclear, particularly within community pharmacy practice. This study sought the viewpoint of community pharmacy leaders (i.e., community pharmacy managers, district managers, franchisees, owners, and executives from various pharmacy organizations) to characterize burnout from their perspectives. Methods: This qualitative study followed a grounded theory approach. Community pharmacy leaders were interviewed using a semistructured format to gather in-depth insights into their experiences and perspectives on burnout and engagement. Results: Sixteen people were interviewed; interviews lasted 30 to 65 minutes, averaging 51 minutes long. Six themes were identified: perceived disconnection between front-line staff and pharmacy decision-makers, overwhelming work demands, cautious optimism toward the expanding scope of pharmacy practice, the importance of employee recognition and appreciation, appropriateness and use of existing work resources, and multimodal, systemic responsibility and solutions to burnout. Conclusion: Addressing burnout requires a multifaceted approach involving personal, organizational, and systemic interventions. Evidence from this study provides valuable insights into the feasibility and efficacy of specific interventions, informing future strategies to enhance workplace well-being and engagement. The study highlights the importance of managing job demands and maximizing resources, emphasizing that personal approaches alone are insufficient and that organizational and systemic interventions are crucial.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".