Essential human and material resources for emergency care in the district hospitals of Burundi
Bibliographic record
Abstract
Burundi, like many African nations, faces challenges in providing accessible emergency care. The aim of this study was to assess the type of staff training, accessibility to imaging, and availability of essential equipment in the district hospitals of Burundi in order to inform strategic planning for healthcare delivery. In June 2022 an online survey was sent to each district hospital of the country. Complete responses were analysed and, where appropriate, significance determined by chi-square analysis, with p<0.05 considered significant. Forty of 45 district hospitals completed the survey, of which 35 were rural (matching national demographics). The majority of district hospitals (21/40) had ready access to ≥4/5 critical drugs while few (5/40) were equipped with ≥4/5 key material. One quarter had 24/7 physician coverage and X-ray available. Only 3 had continuous access to ultrasound studies despite most district hospitals having ultrasound machines. Trained emergency room staff were almost totally absent from the field, with only 6 nurses, 4 generalists, and 1 specialist reported across 9 sites. Even a single EM-trained staff member was significantly correlated with being better equipped for emergencies (p<0.01). Burundi needs a strategic investment in emergency preparedness and care. Policy initiatives and technology purchases have demonstrated reasonable penetration down to the district hospital level, however, trained personnel are essential to develop sustainable emergency capacity.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".