A Mixed-Methods Cohort Study Evaluating the Impact of a One-Day Well-Being Course for Anesthesia Providers Working in Low-Resource Settings
Bibliographic record
Abstract
BACKGROUND: Burnout, depression, and anxiety are increasingly recognized as common among health care providers. Risks for these conditions are exacerbated in low-resource settings by excessive workload, high disease burden, resource shortage, and stigma against mental health issues. Based on discussions and requests to learn more about burnout during the Vital Anaesthesia Simulation Training (VAST), our team developed VAST Wellbeing, a 1-day course for health care providers in low-resource settings to recognize and mitigate burnout and to promote personal and professional well-being. METHODS: This mixed-methods study used quantitative pre- and postcourse surveys using validated mental health measures and qualitative semistructured interviews to explore participants' experience of VAST Wellbeing during and after the course. Quantitative outcomes included burnout and professional fulfillment as measured by the Professional Fulfillment Index and general well-being as measured by the Warwick-Edinburgh Mental Wellbeing Scale. RESULTS: Twenty-six participants from 9 countries completed the study. In the immediate postcourse survey, study participants rated the course overall as "very good" (60.7%) and "excellent" (28.6%). Quantitative analysis showed no statistical differences in levels of work exhaustion, interpersonal disengagement, burnout, professional fulfillment, or general mental well-being 2 months after the course. Five themes on the impact of VAST Wellbeing were identified during qualitative analysis: (1) raising awareness, breaking taboos; (2) not feeling alone; (3) permission and capacity for personal well-being; (4) workplace empowerment; and (5) VAST Wellbeing was relevant, authentic, and needed. CONCLUSIONS: Causes of burnout are complex and multidimensional. VAST Wellbeing did not change measures of burnout and fulfillment 2 months postcourse but did have a meaningful impact by raising awareness, reducing stigma, fostering connection, providing skills to prioritize personal well-being, and empowering people to seek workplace change.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".