Staff burnout and its risk factors at King Faisal Hospital Rwanda: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: There is limited research on burnout among healthcare providers in sub-Saharan Africa. One prior study of Rwanda anesthesia providers found a moderate level of burnout, and several qualitative studies have highlighted significant stressors. This study aims to evaluate the prevalence of professional fulfillment, burnout, and fatigue among healthcare providers at King Faisal Hospital Rwanda (KFH), a tertiary-level teaching hospital in Kigali, Rwanda. METHODS: A cross-sectional, quantitative study was conducted from August to November 2023 at KFH. Participants included all staff employed at KFH at the time of survey distribution, including both non-clinical and clinical staff. Burnout, fatigue, and professional fulfillment were assessed using validated tools (Professional Fulfillment Index, Burnout Scale, and Fatigue Assessment Scale). FINDINGS: Two hundred ninety-four respondents completed the survey. 47.1% reported professional fulfillment, while over half (57.0%) experienced burnout, and the majority experienced fatigue (71.0%). Years of experience and profession were found to be risk factors for burnout and low fulfillment. Age and profession were risk factors for fatigue. The highest levels of burnout were among doctors, nurses, and midwives. DISCUSSION: Burnout rates and fatigue were high among healthcare providers. They were highest among those professions with direct patient contact. There are several evidence-based institutional interventions for burnout, but most evidence comes from settings outside of sub-Saharan Africa. Future research should assess the effectiveness of interventions specific to this setting.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".