Burnout Syndrome Among Perioperative Healthcare Providers in Rwanda
Bibliographic record
Abstract
BACKGROUND: Many studies address health care provider burnout in high-income countries; however, there is little data on burnout in low-income countries. Our objectives were (1) to estimate the prevalence of burnout among perioperative health care providers and (2) to explore factors associated with burnout among perioperative health care providers in Rwandan public hospitals. METHODS: A cross-sectional study using a survey was conducted among perioperative health care providers working in 22 public hospitals across Rwanda. We used a purposive sampling method to represent all regions (4 provinces and the capital Kigali) and types of public hospitals in Rwanda conducting surgery, excluding major teaching centers. We used the Maslach Burnout Inventory Human Services Survey (MBI_HSS), a validated 22-item survey including 3 dimensions of burnout: (1) emotional exhaustion (EE), (2) depersonalization (DP), and (3) personal achievement (PA). We estimated the prevalence of burnout using Wilson's method and we identified factors associated with burnout using a multivariate analysis. RESULTS: There were 221 responses from 402 surveys sent with a response rate of 53.7% including nurses 106 (47.9%), general practitioners 36 (16.3%), nonphysician anesthetists 33 (14.9%), midwives 25 (11.3%), and specialist surgeons and anesthesiologists 4 (1.8%). Forty-7 (21.3, 95% CI 16.1-27.3)% participants had burnout, 95 (42.9, 95 CI 36.6-49.6)% had high emotional exhaustion, 57 (25.8, 95 CI 20.5-31.9)% had low personal accomplishment, 15 (6.8, 95 CI 4.2-10.9)% had high depersonalization). Three major burnout profiles were identified among participants, including the overextended group 84 (38%), the engaged group 83 (37.6%), and the ineffective group 39 (17.6%). Among postulated predictors of burnout, only a lack of having the right equipment was strongly associated with burnout (adj-OR, 3.21; 95 CI, 1.18-8.73, P = .02). CONCLUSIONS: One in 5 perioperative health care providers in Rwanda report having burnout, which is consistent with previous data. This suggests that burnout is widespread across the Rwandan health care system, across different perioperative professions. The only factor that was associated with burnout was lack of access to essential equipment; however, other factors that have been identified in the literature, which are not statistically significant in this study, should not be overlooked. Addressing equipment shortages may reduce the risk of burnout among perioperative health care providers in low-resource settings, in addition to directly impacting the quality of care.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".