Does attitude towards wife beating determine infant feeding practices during diarrheal illness in sub-Saharan Africa?
Bibliographic record
Abstract
BACKGROUND: Inappropriate feeding practices of children during illness remains a public health problem globally, particularly in sub-Saharan Africa (SSA). One strategy to improve child health outcomes is through women empowerment-measured by wife beating attitude. However, the role of attitude towards wife beating in child feeding practices has not been comprehensively studied. Therefore, we investigated the association between women's attitude towards wife beating and child feeding practices during childhood diarrhea in 28 countries in SSA. METHODS: We analyzed data from the Demographic and Health Survey on 40,720 children under 5 years. Bivariate and multivariate binary logistic regression analyses were applied to assess the association between women's attitude towards wife beating and child feeding practices. The results were presented using adjusted odds ratio (aOR) with 95% confidence intervals (CIs). RESULTS: The pooled results showed that appropriate feeding practices during diarrheal illness among under-five children was 9.3% in SSA, varying from 0.4% in Burkina Faso to 21.1% in Kenya. Regarding regional coverage, the highest coverage was observed in Central Africa (9.3%) followed by East Africa (5.5%), Southern Africa (4.8%), and West Africa (4.2%). Women who disagreed with wife-beating practices had higher odds of proper child feeding practices during childhood diarrhea compared to those who justified wife-beating practices (aOR = 2.02, 95% CI; 1.17-3.48). CONCLUSION: The findings suggest that women's disagreement with wife beating is strongly associated with proper child feeding practices during diarrheal illness in SSA. Proactive measures and interventions designed to change attitudes towards wife-beating practices are crucial to improving proper feeding practices in SSA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".