Burnout Syndrome Among Anesthesia Providers Working in Public Hospitals in Rwanda: A Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Many studies address anesthesia provider burnout in high-income countries; however, there is a paucity of data on burnout for anesthesia providers in low-income countries (LICs). Our objectives were (1) to evaluate the prevalence of burnout among anesthesia providers in Rwandan hospitals and (2) to determine factors associated with burnout among anesthesia providers in Rwandan hospitals. METHODS: A questionnaire was sent to selected Rwandan anesthesia providers working in public hospitals. The questionnaire assessed burnout using the Maslach Burnout Inventory Human Services Survey, a validated 22-item survey used to measure burnout among health professionals. Sociodemographic and work-related factors found to be associated with burnout were also assessed using logistic regression in a Bayesian framework to estimate odds ratios (OR) and associated credible intervals (CrIs). RESULTS: Surveys were distributed to 137 Rwandan anesthesia providers; 99 (72.3%) were returned. Sixty-six (67%) respondents were nonphysician anesthesia providers. Burnout was present in 26 of 99 (26.3%) participants (95% confidence interval [CI], 17.9-36.1). When considering weakly informative priors, we found a 99% probability that not having the right team (OR, 5.36%; 95 CrI, 1.34-23.53) and the frequency of seeing patients with negative outcomes such as death or permanent disability (OR, 9.62; 95% CrI, 2.48-42.84) were associated with burnout. CONCLUSIONS: In a cross-sectional survey of anesthesia providers in Rwanda, more than a quarter of respondents met the criteria for burnout. Lacking the right team and seeing negative outcomes were associated with higher burnout rate. These identified factors should be addressed to prevent the negative consequences of burnout, such as poor patient outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".