‘We are in for a culture change’: continuing professional development leaders’ perspectives on COVID-19, burn-out and structural inequities
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic positioned healthcare systems in North America at the epicentre of the crisis, placing inordinate stress on clinicians. Concurrently, discussions about structural racism, social justice and health inequities permeated the field of medicine, and society more broadly. The confluence of these phenomena required rapid action from continuing professional development (CPD) leaders to respond to emerging needs and challenges. METHODS: In this qualitative study, researchers conducted 23 virtual semistructured interviews with CPD leaders in Canada and the USA. Interview audiorecordings were transcribed, deidentified and thematically analysed. RESULTS: This study revealed that the CPD leaders attributed the pandemic as illuminating and exacerbating problems related to clinician wellness; equity, diversity and inclusion; and health inequities already prevalent in the healthcare system and within CPD. Analysis generated two themes: (1) From heroes to humans: the shifting view of clinicians and (2) Melding of crises: an opportunity for systemic change in CPD. DISCUSSION: The COVID-19 pandemic increased recognition of burn-out and health inequities creating momentum in the field to prioritise and restrategise to address these converging public health crises. There is an urgent need for CPD to move beyond mere discourse on these topics towards holistic and sustainable actionable measures.
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.037 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".