Enhancing Surgical Safety in Conflict Zones: Implementing the WHO Checklist in North Kivu
Bibliographic record
Abstract
Background: The WHO Surgical Safety Checklist (WHO Checklist) has been shown to effectively reduce surgical complications worldwide. However, implementing this checklist in conflict-affected regions like North Kivu, Democratic Republic of Congo (DRC), presents unique challenges. This study investigates the utilization of the WHO Checklist in hospitals across North Kivu to identify barriers and opportunities for improvement. Methods: A cross-sectional study was conducted across 11 hospitals (5 urban and 6 rural) in North Kivu. Surveys were administered to healthcare professionals, including surgeons, anesthesiologists, and nurses, to assess their knowledge, usage, and perceptions of the WHO Checklist. Data were analyzed using SPSS version 26. Results: The response rate was 80.3%, with a majority (59.2%) from urban hospitals. The use of the WHO Checklist was inconsistent; 60.1% reported it was not utilized in the operating room. No significant differences in checklist usage were found between urban and rural hospitals (p=0.516). Training significantly correlated with the completion rate of checklist phases (p<0.001) but not with overall usage (p=0.057). Furthermore, there were no significant differences regarding the need for further training to enhance checklist use across rural and urban hospitals (p=0.334) or among different professions (p=0.321). Conclusion: While the WHO Checklist has the potential to enhance surgical safety in conflict zones like North Kivu, targeted training programs and tailored interventions are essential for improving its use in similar resource-limited contexts globally.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".