Impact of universal home visits on maternal and infant outcomes in Bauchi state, Nigeria: protocol of a cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Maternal mortality in Nigeria is one of the highest in the world. Access to antenatal care is limited and the quality of services is poor in much of the country. Previous research in Bauchi State found associations between maternal morbidity and domestic violence, heavy work in pregnancy, lack of knowledge about danger signs, and lack of spousal communication about pregnancy and childbirth. This cluster randomized controlled stepped-wedge trial will test the impact of universal home visits to pregnant women and their partners, and the added value of video edutainment. METHODS: The trial will take place in six wards of Toro Local Government Area in Bauchi State, Nigeria, randomly allocated into three waves of two wards each. Home visits will begin in wave 1 wards immediately; in wave 2 wards after one year; and in wave 3 wards after a further year. In each wave, one ward, randomly allocated, will receive video edutainment during the home visits. Female home visitors will contact all households in their catchment areas of about 300 households, register all pregnant women, and visit them every two months during pregnancy, after delivery and one year later. They will use android handsets to collect information on pregnancy progress, send this to a central server, and discuss with the women the evidence about household factors associated with higher maternal risks, using video clips in the edutainment wards. Male home visitors will contact the partners of the pregnant women and discuss with them the same evidence. We will compare outcomes between wave 1 and wave 2 wards at about one year, between wave 2 and wave 3 wards at about two years, and finally between wards with and without added edutainment. Primary outcomes will be complications in pregnancy and delivery, and child health at one year. Secondary outcomes include knowledge and attitudes, use of health services, knowledge of danger signs, and household care of pregnant women. DISCUSSION: Demonstrating an impact of home visits and understanding potential mechanisms could have important implications for reducing maternal morbidity and mortality in other settings with poor access to quality antenatal care services. TRIAL REGISTRATION: Registration number: ISRCTN82954580 . Registry: ISRCTN. Date of registration: 11 August 2017. Retrospectively registered.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".