Assessment of the Quality of Antenatal and Postnatal Care Services in Primary Health Centres in Rural Nigeria
Bibliographic record
Abstract
Maternal mortality ratio in Nigeria is estimated to be 512 deaths per 100,000 live births. As with other low-income countries, a higher proportion of these deaths occur among women living in rural areas and in poor communities where access to maternal health care is limited by several barriers including quality of care in health facilities. The objective of this study was to assess the quality of antenatal and postnatal care in Primary Health Centres (PHCs) in two rural Local Government Areas of Edo State in Southern Nigeria. The data were obtained from exit interviews with 177 women after completion of antenatal and postnatal care in eight randomly selected PHCs. The interview questionnaire was adapted from the 2017 results-based financing exit interviews conducted by the World Bank in collaboration with the Federal Ministry of Health and the National Bureau of Statistics. It consisted of questions on the treatment received by women. The data were analysed with descriptive statistics and logistic regression. The results showed the self-reporting by women of sub-optimal offerings of 20 signal antenatal treatments and 8 signal postnatal care treatments. Close to half (45.6%) of the respondents for antenatal care reported receiving sub-optimal antenatal treatments compared to about a third of postnatal care attendees. The predictors of sub-optimal offerings of standard PHC care included local government area, marital status and previous childbirths. We conclude that concerted actions by health providers and policymakers in the PHCs to develop policies and interventions will improve the quality of delivery of antenatal and postnatal services in rural PHCs in Nigeria.
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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 | 0.008 |
| 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 | 0.000 |
| 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".