Resources and interventions to support psychological health and wellbeing in the pharmacy workforce: Analysis and use of a health worker ‘burnout’ toolkit
Bibliographic record
Abstract
Background: Pharmacists have experienced declines in psychological health and wellbeing post-pandemic. The phenomena of moral distress, disengagement and burnout are associated with workforce attrition, unfitness to practice and inferior quality of patient care. A working group of the Canadian Pharmacists Association (CPhA) was formed to identify resources and interventions (R&I) for occupational psychological health and wellbeing. Objective: To characterize R&I from an evidence-based national health worker 'burnout' Toolkit with potential to support the pharmacy workforce. Methods: All R&I included within a draft 'burnout' Toolkit from the Canadian Health Workforce Network (CHWN) were screened to determine relevancy and usefulness for the pharmacy workforce. R&I with higher grades were data-charted to capture information on topic and content delivery. Final R&I were determined through consensus meetings where 'highly rated' R&I were discussed and selected. Results: in the pharmacy workforce. Of those 53 R&I, 28 (20% of original) were final selections. The majority of R&I at each stage were focused on 'preventing burnout' and 'promoting mental health' (>60%) rather than 'addressing burnout', 'supporting recovery' or managing specific issues in the workplace (i.e. stigma, discrimination, bullying, hostility, workload). No R&I were specifically developed or studied within the pharmacy workforce. Conclusions: Health professions may benefit from the CHWN Toolkit and the knowledge translation activity described here. R&I relevant and useful to the pharmacy workforce generally require adaptation for dissemination and/or implementation. The set of final R&I form the basis for orchestrated plans to support the pharmacy workforce with respect to psychological health and wellbeing. There is a relative lack of R&I devoted to addressing and recovering from burnout and workload management issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".