Increased Maternal Education and Knowledge of Nutrition and Reductions in Poverty are Associated with Dietary Diversity and Meal Frequency in an Observational Study of Indonesian Children
Bibliographic record
Abstract
Background: Optimal infant and young child feeding during the first two years of life is essential to optimum child development and health. While the link between feeding practices and child health outcomes is well documented, little is known about the determinants of these feeding practices in Indonesia. The purpose of this study was to better understand factors associated with appropriate child feeding among Indonesian children 6–23 months of age. Methods: Interviewers conducted interviews with 1498 mothers of children 6–23 months of age to identify practices. Measures of feeding practices included dietary diversity, meal frequency, and minimum acceptable diet. Multivariate logistic regression was used to identify factors associated with dietary diversity and separately with meal frequency. Results: After adjusting for covariates, increased maternal education was associated with improved dietary diversity. Age of child [OR=1.11], knowledge of stunting [OR=1.80], and having ever received nutrition information [OR=1.89] were also associated with greater dietary diversity. Wealth [OR=0.86] and age of child [OR=0.92] were inversely associated with meal frequency. Maternal education, age of child, being a male child, knowledge of stunting, and having received nutrition information increased the odds of the child consuming a minimum acceptable diet. Conclusion: Increasing maternal education, knowledge of stunting, and knowledge of nutrition may improve dietary diversity while poverty alleviation has the potential to improve minimum meal frequency. These findings corroborate similar studies and confirm the importance of government efforts that help girls stay in school, improve families’ understanding of nutrition, and reduce poverty
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".