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 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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".