Can women empowerment boost dietary diversity among children aged 6–23 months in sub-Saharan Africa?
Bibliographic record
Abstract
BACKGROUND: The empowerment of women has implications on the health and dietary needs of children. Using the survey-based women's empowerment index (SWPER), we examined the association between women's empowerment and dietary diversity among children aged 6-23 months in sub-Saharan Africa. METHODS: Data from the Demographic and Health Surveys of 21 countries were utilized. Descriptive spatial map was used to present the proportions of dietary diversity among the children. Multilevel binary logistic regression was used to examine the association between SWPER and dietary diversity. RESULTS: Overall, 22.35% of children aged 6-23 months had adequate minimum dietary diversity (MDD) in sub-Saharan Africa. The countries with the highest proportions of adequate MDD were Angola, Benin, Madagascar, Rwanda, Sierra Leone, and South Africa. South Africa had the highest proportion of MDD (61.00%), while Liberia reported the least (9.12%). Children born to mothers who had high social independence were more likely to have adequate MDD compared to those with low social independence [aOR = 1.31, 95% CI 1.21, 1.41]. In addition, children born to women with medium [aOR = 1.12; 95% CI 1.03, 1.21] and high decision-making [aOR = 1.25, 95% CI 1.14, 1.37] were more likely to receive MDD than those with low decision-making. CONCLUSIONS: Insufficient dietary diversity is evident among children aged 6-23 months in sub-Saharan Africa. MDD in children is influenced by women's empowerment. Policies and interventions promoting women's empowerment can enhance MDD, especially for vulnerable groups in rural and poorer households. It is crucial to leverage media and poverty reduction strategies to improve MDD among children in sub-Saharan African countries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".