Effectiveness of targeted social and behavior change communication on maternal health knowledge, attitudes, and institutional childbirth: a cluster-randomized trial in Jimma Zone, Ethiopia
Bibliographic record
Abstract
Maternal mortality remains a critical global health challenge, with 95% of deaths occurring in low-income countries. While progress was made from 2000 to 2015, regions such as Ethiopia continue to experience high maternal mortality rates, impeding the achievement of the sustainable development goal to reduce maternal deaths to 70 per 100 000 live births by 2030. This study evaluated the effectiveness of a Social and Behavior Change Communication (SBCC) intervention to improve maternal health behaviors. A community-randomized trial was conducted in three districts of Jimma Zone, rural Ethiopia, involving 5057 women. Sixteen primary healthcare units were randomly assigned to either the intervention (SBCC) or control (standard care) group. Data on socio-demographics, antenatal care (ANC) visits, maternal health knowledge, attitudes, and institutional childbirth rates were collected at baseline and endline. Statistical analyses included t-tests, effect sizes, and generalized estimating equations. The intervention group demonstrated significant improvements. Maternal health knowledge increased from 5.68 to 7.70 (P < .001, effect size = 0.34), attitudes improved from 37.49 to 39.73 (P < .001, effect size = 0.29), and ANC visits rose from 3.27 to 4.21 (P < .001, effect size = 0.50). Institutional childbirth rates increased from 0.52 to 0.71 (P < .001, effect size = 0.18). ANC attendance (B = 0.082, P = .002) and positive attitudes (B = 0.055, P < .001) were significant predictors of institutional childbirth. The SBCC intervention significantly enhanced maternal health knowledge, attitudes, ANC utilization, and institutional childbirth rates, highlighting the value of community-based strategies in improving maternal health behaviors.
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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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".