Stunting, dietary diversity and household food insecurity among children under 5 years in ethnic communities of northern Thailand
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to describe stunting in infants and young children in the ethnic communities of northern Thailand and to explore associations with dietary diversity and household factors including food security. METHODS: A cross-sectional survey of households with children under 5 years from eight villages. Adult respondents provided information on foods consumed by each child and details of the household. Heights and weights of children were measured. RESULTS: Adults from 172 households and 208 children participated. Overall, 38% of children were stunted. Exclusive breastfeeding was rare, but the proportion consuming breastmilk at 24 months (75%) was high. Few children (7%) aged 6-11 months met minimum dietary diversity. Stunted children were less likely than non-stunted children to meet minimum dietary diversity (63 versus 82%). Widespread food insecurity did not discriminate between stunted and non-stunted children. Stunting was elevated when households had little land and few animals. CONCLUSIONS: Stunting was widespread in children under 5 years of age, in part reflecting poor dietary diversity, especially at age 6-11 months. Stunting was worst in households with least assets. Small increases in land or animals, or equivalent resources, appear to be required to improve child nutrition in extremely poor families.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".