Very low micronutrient intake adequacy of young children in rural Bangladesh is explained by limited diversity of nutrientrich foods
Bibliographic record
Abstract
Documentation of micronutrient intake inadequacies among children in developing countries is important for planning food‐based interventions. This study quantified and assessed adequacy of micronutrient intakes of young children in rural Bangladesh. We measured 24‐h dietary intakes on two non‐consecutive days in a representative sample of 480 children ages 24–48 months using weighed food records and recall in homes. We calculated the probability of adequacy of usual intakes of 11 micronutrients, an overall mean probability of micronutrient adequacy (MA), and evaluated dietary diversity by counting the total number of nine food groups consumed. The relationship between overall adequacy of micronutrient intakes and dietary diversity scores or food group indicators was determinate using multivariate regression analyses. The overall mean prevalence of MA was 43% ± 16%. Fewer than 10% of children had adequate intakes of calcium, folate, and vitamin A, and < 50% of children had adequate intakes of iron, riboflavin, and vitamin B–12. Overall MA was primarily explained by energy intake and diet diversity. Food groups that explained variance in MA were dairy, eggs, meats, and vitamin A‐rich fruits and vegetables. Micronutrient adequacy among young children in rural Bangladesh is alarmingly low, and targeted food‐based interventions are warranted.
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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 | 0.002 |
| 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".