Traditional medicine used in childbirth and for childhood diarrhoea in Nigeria's Cross River State: interviews with traditional practitioners and a statewide cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Examine factors associated with use of traditional medicine during childbirth and in management of childhood diarrhoea. DESIGN: Cross-sectional cluster survey, household interviews in a stratified last stage random sample of 90 census enumeration areas; unstructured interviews with traditional doctors. SETTING: Oil-rich Cross River State in south-eastern Nigeria has 3.5 million residents, most of whom depend on a subsistence agriculture economy. PARTICIPANTS: 8089 women aged 15-49 years in 7685 households reported on the health of 11,305 children aged 0-36 months in July-August 2011. PRIMARY AND SECONDARY OUTCOME MEASURES: Traditional medicine used at childbirth and for management of childhood diarrhoea; covariates included access to Western medicine and education, economic conditions, engagement with the modern state and family relations. Cluster-adjusted analysis relied on the Mantel-Haenszel procedure and Mantel extension. RESULTS: 24.1% (1371/5686) of women reported using traditional medicine at childbirth; these women had less education, accessed antenatal care less, experienced more family violence and were less likely to have birth certificates for their children. 11.3% (615/5425) of young children with diarrhoea were taken to traditional medical practitioners; these children were less likely to receive BCG, to have birth certificates, to live in households with a more educated head, or to use fuel other than charcoal for cooking. Education showed a gradient with decreasing use of traditional medicine for childbirth (χ(2) 135.2) and for childhood diarrhoea (χ(2) 77.2). CONCLUSIONS: Use of traditional medicine is associated with several factors related to cultural transition and to health status, with formal education playing a prominent role. Any assessment of the effectiveness of traditional medicine should anticipate confounding by these factors, which are widely recognised to affect health in their own right.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".