A Cross-Sectional Survey to Determine the Prevalence of Burnout Syndrome Among Anesthesia Providers in Zambian Hospitals
Bibliographic record
Abstract
BACKGROUND: Burnout is a psychological syndrome that results from chronic exposure to job stress. It is defined by a triad of emotional exhaustion, depersonalization, and reduced personal accomplishment. In research, mostly from high-income countries, burnout is common in health care professionals, especially in anesthesiologists. Burnout can negatively impact patient safety, the physical and mental health of the anesthetist, and institutional efficiency. However, data on burnout for anesthesia providers in low- and middle-income countries are poorly described. This study sought to determine the prevalence of burnout syndrome among all anesthesia providers (physician and nonphysician) working in Zambian hospitals and to determine which sociodemographic and occupational factors were associated with burnout. METHODS: A questionnaire was sent to all Zambian anesthesia providers working in private and public hospitals. The questionnaire assessed burnout using the Maslach Burnout Inventory Human Services Survey, a validated 22-item survey widely used to measure burnout among health professionals. Sociodemographic and occupational factors postulated to be associated with burnout were also assessed. RESULTS: Surveys were distributed to all 184 anesthesia providers in Zambia; 160 were returned. This resulted in a response rate representing 87% of all anesthesia providers in the country. Eighty-six percentage of respondents were nonphysician anesthesia providers. Burnout was present in 51.3% (95% confidence interval [CI], 43.2-59.2) of participants. Logistic regression analysis revealed that "not having the right team to carry out work to an appropriate standard" (odds ratio, 2.91, 95% CI, 1.33-6.39; P = .008), and "being a nonphysician" (odds ratio, 3.4, 95% CI, 1.25-12.34; P = .019) were significantly associated with burnout in this population. CONCLUSIONS: In a cross-sectional survey of anesthesia providers in Zambia, >50% of the respondents met the criteria for burnout. The risk was particularly high among nonphysician providers who typically work in isolated rural practice. Efforts to decrease burnout rates through policy and educational initiatives to increase the quantity and quality of training for anesthesia providers should be considered.
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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 | 0.001 | 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".