The Equity Impact of Universal Home Visits to Pregnant Women and Their Spouses in Bauchi State, Nigeria: Secondary Analysis From a Cluster Randomised Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Socio-economically disadvantaged women have poor maternal health outcomes. Maternal health interventions often fail to reach those who need them most and may exacerbate inequalities. In Bauchi State, Nigeria, a recent cluster randomised controlled trial (CRCT) showed an impressive impact on maternal health outcomes of universal home visits to pregnant women and their spouses. The home visitors shared evidence about local risk factors actionable by households themselves and the program included specific efforts to ensure all households in the intervention areas received visits. PURPOSE: To examine equity of the intervention implementation and its pro-equity impact. RESEARCH DESIGN AND STUDY SAMPLE: The overall study was a CRCT in a stepped wedge design, examining outcomes among 15,912 pregnant women. ANALYSIS: We examined coverage of the home visits (three or more visits) and their impact on maternal health outcomes according to equity factors at community, household, and individual levels. RESULTS: Disadvantaged pregnant women (living in rural communities, from the poorest households, and without education) were as likely as those less disadvantaged to receive three or more visits. Improvements in maternal knowledge of danger signs and spousal communication, and reductions in heavy work, pregnancy complications, and post-natal sepsis were significantly greater among disadvantaged women according to the same equity factors. CONCLUSIONS: The universal home visits had equitable coverage, reaching all pregnant women, including those who do not access facility-based services, and had an important pro-equity impact on maternal health.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".