Dietary Diversity among Children Aged 6-23 Months in Bangladesh: Determinants and Inequalities
Bibliographic record
Abstract
Inadequate dietary intake is one of the causes of childhood undernutrition and associated morbidity and mortality in many low and middle-income countries, including Bangladesh. The study aims to identify the prevalence, associated factors, and socio-economic inequalities in minimum dietary diversity, minimum meal frequency, and minimum acceptable diet among 6-23 month-children in Bangladesh. This study uses data from the latest round of the Bangladesh Demographic and Health Survey (BDHS) 2017-18. Descriptive analyses have been conducted to report frequencies and percentages of the socio-demographic and economic characteristics of 6-23 months aged children. Bivariate and multiple logistic models are used to identify the predictors of each dietary indicator. In addition, we estimate concentration indices and use Wagstaff-based decomposition analysis to identify socio-economic inequalities in dietary diversity and their contributing factors. The study finds the prevalence of minimum dietary diversity, minimum meal frequency, and minimum acceptable diet as 38%, 81%, and 36%, respectively. Education of mothers is a significant predictor of all three dietary indicators. In addition, household wealth status and administrative division are significant predictors of minimum dietary diversity and minimum acceptable diet. Children of working mothers are found to have higher odds of having minimum meal frequency and minimum acceptable diet compared to their counterparts. We find concentration indices for minimum dietary diversity as 0.21 (p<0.001), for minimum meal frequency as 0.08 (p<0.05), and for minimum acceptable diet as 0.19 (p<0.001). Wealth status of household, mother’s and father’s education levels, and exposure to mass media are the major contributing factors to these inequalities. Therefore, policymakers and other stakeholders need to give prior attention to enhancing household wealth status, empowering women, and awareness-raising initiatives to improve the feeding practices of children in Bangladesh.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".