Prevalence and predictors of infant and young child feeding practices in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: This study assessed the prevalence and predictors of minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD) in sub-Saharan Africa (SSA). METHODS: A sample of 87 672 mother-child pairs from the 2010-2020 Demographic and Health Surveys of 32 countries in SSA was used. Multilevel binary logistic regression analysis was carried out to examine the predictors of MDD, MMF, and MAD. Percentages and adjusted odds ratios (aORs) with a 95% confidence interval (CI) were used to present the findings. RESULTS: The prevalence of MDD, MMF, and MAD in SSA were 25.3% (95% CI 21.7 to 28.9), 41.2% (95% CI 38.8 to 43.6), and 13.3% (95% CI 11.6 to 15.0), respectively. Children aged 18-23 months were more likely to have MDD and MAD but less likely to have MMF. Children of mothers with higher education levels were more likely to have MDD, MMF, and MAD. Children who were delivered in a health facility were more likely to have MDD and MAD but less likely to have MMF. CONCLUSIONS: Following the poor state of complementary feeding practices for infants and young children, the study recommends that regional and national policies on food and nutrition security and maternal and child nutrition and health should follow the internationally recommended guidelines in promoting, protecting, and supporting age-appropriate complementary foods and feeding practices for infants and young children.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".