Assessment of service readiness for maternity care in primary health centres in rural Nigeria: implications for service improvement.
Bibliographic record
Abstract
INTRODUCTION: several scientific reports from studies across Nigeria revealed a higher incidence of maternal mortality in rural parts of the country as compared to the urban areas. Part of the reasons is the paucity of health care infrastructure and personnel. This study was designed as part of an intervention program with the goal to improve the access of pregnant women to skilled pregnancy care in rural Nigeria. The specific objective of the study was to determine the nature and readiness of Primary Health Centres (PHCs) in two Local Government Areas (LGAs) in rural parts of Edo State, Southern Nigeria to deliver effective maternal and child health services. METHODS: the study was conducted in 12 randomly selected PHCs in the two LGAs. Data were obtained with a semi-structured questionnaire administered on health workers and through direct observation and verification of the facilities in the PHCs. The results obtained were compared with the national standards established for PHCs in Nigeria by the National Primary Health Care Development Agency (NPHCDA). Descriptive statistics were used to analyze the data. RESULTS: the results showed severe deficits in buildings and premises, rooms, medical equipment, essential drugs, and personnel. Only 40% of items recommended by the NPHCDA were available for buildings; 41% of the PHCs had facilities available in the labour ward; while less than 30% had the recommended facilities in the antenatal care rooms. Only one PHC had a laboratory space, with only one item (a dipstick for urine analysis) identified in the laboratory. None of the PHCs had ambulances, mobile phones, internet or computers. There was no nurse/midwife in 4 PHCs; only one nurse/midwife each were available in 8 PHCs; while there was no Environmental/Medical Records Officer in any PHC. About 26% of the essential drugs were not available in the PHCs. CONCLUSION: we conclude that PHCs in Edo State, Nigeria have severe deficits in infrastructural facilities, equipment, essential drugs and personnel for the delivery of maternal and child health care. Efforts to improve these facilities will help increase the quality of delivery of maternal and child health, and therefore reduce maternal and child mortality in the country.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".