WOMEN’S EDUCATION AND UTILIZATION OF MATERNAL HEALTH SERVICES IN AFRICA: A MULTI-COUNTRY AND SOCIOECONOMIC STATUS ANALYSIS
Bibliographic record
Abstract
There is an abundant literature on the relationship between women's education and maternal and child outcomes, including antenatal and postnatal care, onset of antenatal care and skilled birth attendance. However, few studies have adopted the 'equity' lens, despite increasing evidence that inequities between rich and poor are increasing although maternal and child mortality is declining. This study examined the differential effects of women's education within different socioeconomic strata in Africa. The most recent Demographic and Health Surveys (DHS) conducted in the Democratic Republic of the Congo, Egypt, Ghana, Nigeria and Zimbabwe were used. In each country, the original sample was stratified into three socioeconomic groups: poor, middle and rich. For each maternal health service utilization variable, the gross and net effects of women's education, controlling for age, parity, religion, marital status, health insurance, access to health facilities, partner's education and current place of residence, were estimated using logistic regression, taking into account the complex sampling design of the DHS. The findings revealed country-specific variations in maternal health service utilization, and for most indicators there was a clear gradient among socioeconomic strata: women living in better-off households exhibited greater access to, and utilization of, maternal health services. Multivariate analyses revealed that women's education had a positive association with type of antenatal care provider, timing and frequency of antenatal care visits, place of delivery and presence of a skilled birth attendant at delivery. Many other factors were found to be significantly associated with maternal health service utilization. For instance, parity had a negative and significant association with timing of first antenatal care visit. Likewise, partner's education was positively and statistically associated with timing of first antenatal care visit. It is argued that an over-generalization of the association between women's education and maternal health service utilization can be misleading. Efforts to improve maternal health service utilization in Africa must adopt an 'equity' approach, taking into account the specific needs of sub-populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".