Improving Patient Safety Culture in Conflict‐Affected Zones: A Cross‐Sectional Survey of North Kivu Surgical Personnel in the Democratic Republic of the Congo
Bibliographic record
Abstract
BACKGROUND: Patient safety culture significantly impacts outcomes in surgery, where preventable errors can occur. This study assessed patient safety culture and its determinants in operating rooms across North Kivu, a conflict-affected province in the eastern Democratic Republic of the Congo (DRC). METHODS: A descriptive multicenter cross-sectional study was conducted from July to September 2024 in five urban and six rural hospitals. The French version of the Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire was administered to 328 operating room healthcare professionals. RESULTS: The response rate was 78% (256 completed surveys). Urban hospitals accounted for 55.5% of respondents, who were 73.4% male and 62.5% under the age of 40. The overall composite score for patient safety culture was 63.2%. Teamwork (81.1%) and management support for patient safety (77.7%) received the highest positive responses, whereas error reporting (39.9%) and patient safety event reporting (50%) scored lower. Half (49.6%) of the respondents rated patient safety as excellent or very good. There were no significant differences in overall mean composite scores between urban and rural hospitals (p = 0.677) and between medical and paramedical staff (p = 0.694). CONCLUSIONS: The patient safety culture rating in North Kivu falls below international standards, highlighting an urgent need for improvement, particularly in error response and event reporting. Developing a tailored patient safety bundle for the region is essential to enhance overall health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".