Effects of maternal education on birth preparedness and complication readiness among Ethiopian pregnant women: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Birth preparedness and complication readiness are broadly endorsed by governments and international agencies to reduce maternal and neonatal health threats in low income countries. Maternal education is broadly positioned to positively affect the mother's and her children's health and nutrition in low income countries. Thus, this systematic review and meta-analysis aims to estimate the effect of maternal education on birth preparedness and complication readiness. METHODS: test statistics and Egger's test were used to assess heterogeneity and publication bias. Pooled prevalence and pooled odd ratios with 95% confidence intervals were computed. Finally, Duval and Tweedie's nonparametric trim and fill analysis using random-effects meta-analysis was conducted to account for publication bias. RESULTS: In this meta-analysis, 20 studies involving 13,744 pregnant women meeting the inclusion criteria were included, of which 15 studies reported effects of maternal education on birth preparedness and complication readiness. Overall estimated level of birth preparedness and complication readiness was 25.2% (95% CI 20.0, 30.6%). This meta-analysis found that maternal education and level of birth preparedness and complication readiness were positively associated. Pregnant mothers whose level of education was primary and above were more likely to prepare for birth and obstetric emergencies (OR = 2.4, 95% CI: 1.9, 3.1) than non-educated mothers. CONCLUSION: In Ethiopia, the proportion of women prepared for birth and related complications remained low. Maternal education has a positive effect on the level of birth preparedness and complication readiness. Therefore, it is imperative to launch programs at national and regional levels to uplift women's educational status to enhance the likelihood of maternal health services utilization.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".