Developing a Global Community of Practice for Pharmacy Workforce Resilience—Meet GRiT
Bibliographic record
Abstract
Workforce resilience in pharmacy is required to ensure the practice, education, and administrative systems remain viable and sustainable over time and when facing challenges. Whether it is addressing burnout of pharmacists or students, or the structure and policies/procedures of employment and professional organizations, working to increase resilience across all individuals and sectors is essential to relieve pressure and promote better well-being, especially during the recent pandemic. The purpose of this article is to describe the development of a community of practice global group focused on development of resilience within the pharmacy workforce that is inclusive of students, pharmacy interns/preregistration and registered pharmacists. The steering group meets monthly and has representation of 24 members across eight countries. Members meet to discuss pertinent issues they are facing in practice, as well as to share and progress ideas on education, research, and practice initiatives. To date, members have collectively implemented resilience training in pharmacy education, researched burnout and resilience in both students and pharmacists, and facilitated international collaborations both within and outside core group members. Future activities will focus on strengthening the community of practice in order to harness the power of the collective.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.045 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".