Measuring the preparedness of health facilities to deliver emergency obstetric care in a South African district
Bibliographic record
Abstract
Improving the delivery of emergency obstetric care (EmNOC) remains critical in addressing direct causes of maternal mortality. United Nations (UN) agencies have promoted standard methods for evaluating the availability of EmNOC facilities although modifications have been proposed by others. This study presents an assessment of the preparedness of public health facilities to provide EmNOC using these methods in one South African district with a persistently high maternal mortality ratio.Data collection took place in the final quarter of 2014. Cross-sectional surveys were conducted to classify the 7 hospitals and 8 community health centres (CHCs) in the district as either basic EmNOC (BEmNOC) or comprehensive EmNOC (CEmNOC) facilities using UN EmNOC signal functions. The required density of EmNOC facilities was calculated using UN norms. We also assessed the availability of EmNOC personnel, resuscitation equipment, drugs, fluids, and protocols at each facility. The workload of skilled EmNOC providers at hospitals and CHCs was compared.All 7 hospitals in the district were classified as CEmNOC facilities, but none of the 8 CHCs performed all required signal functions to be classified as BEmNOC facilities. UN norms indicated that 25 EmNOC facilities were required for the district population, 5 of which should be CEmNOCs. None of the facilities had 100% of items on the EmNOC checklists. Hospital midwives delivered an average of 36.4±14.3 deliveries each per month compared to only 7.9±3.2 for CHC midwives (p<0.001).The analysis indicated a shortfall of EmNOC facilities in the district. Full EmNOC services were centralised to hospitals to assure patient safety even though national policy guidelines sanction more decentralisation to CHCs. Studies measuring EmNOC availability need to consider facility opening hours, capacity and staffing in addition to the demonstrated performance of signal functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".