Concordance and determinants of mothers’ and children’s diets in Nigeria: an in-depth study of the 2018 Demographic and Health Survey
Bibliographic record
Abstract
OBJECTIVES: Improving the diversity of the diets in young children 6-23 months is a policy priority in Nigeria and globally. Studying the relationship between maternal and child food group intake can provide valuable insights for stakeholders designing nutrition programmes in low-income and middle-income countries. DESIGN: test, and the determinants of child minimum dietary diversity (MDD-C) including women MDD (MDD-W) using hierarchical multivariable probit regression modelling. SETTING: Nigeria. PARTICIPANTS: 8975 mother-child pairs from the Nigeria DHS. PRIMARY AND SECONDARY OUTCOME MEASURES: MDD-C, MDD-W, concordance and discordance in the food groups consumed by mothers and their children. RESULTS: MDD increased with age for both children and mothers. Grains, roots and tubers had high concordance in mother-child dyads (90%); discordance was highest for legumes and nuts (36%), flesh foods (26%), and fruits and vegetables (39% for vitamin-A rich and 57% for other). Consumption of animal source food (dairy, flesh foods, eggs) was higher for dyads with older mothers, educated mothers and more wealthy mothers. Maternal MDD-W was the strongest predictor of MDD-C in multivariable analyses (coef 0.27; 95% CI 0.25 to 0.29, p<0.000); socioeconomic indicators including wealth (p<0.000), mother's education (p<0.000) were also statistically significant in multivariable analyses and rural residence (p<0.000) was statistically significant in bivariate analysis. CONCLUSION: Programming to address child nutrition should be aimed at the mother-child dyad as their food consumption patterns are related and some food groups appear to be withheld from children. Stakeholders including governments, development partners, non-governmental organizations, donors and civil society can act on these findings in their efforts to address undernutrition in the global child population.
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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 | 0.003 |
| 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 | 0.001 |
| 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".