Nutritional Status and Mental Development of Children Under 5 Years of Age in Amhara Region of Ethiopia
Bibliographic record
Abstract
Poor iodine nutrition is associated with impaired mental development. This study examined the relationship between indicators of malnutrition and child mental development in an iodine‐deficient region. 1553 children 54–60 mo of age were randomly selected from 60 villages in the Amhara Region, Ethiopia. Height, weight, goitre status, and socio‐demographic information of participants were taken in a cross‐sectional survey. Wechsler Preschool and Primary School Intelligence and School Readiness scales assessed child mental development. McGill University and local institutions provided ethics approval. Mean scores for family assets (4.3 out of 12), maternal education (0.5 y) and dietary diversity (2.2 out of 7) were low. There was a high prevalence of goitre, underweight, stunting, and wasting (45.9%, 28.9%, 42.6%, and 5.7%, respectively). The matrix reasoning, similarity, and school readiness scores (maximum 19 each) were 7.6, 9.2, and 4.0, respectively; weight‐for‐age (WAZ) and heightfor‐ age (HAZ) positively correlated with all three scores (r=0.13 to 0.20; p<0.0001). Child goitre was associated with matrix reasoning, school readiness, goitre in family, WAZ, and HAZ (r=0.1 to 0.38; p<0.01). Inadequate nutritional status and mental development of Ethiopian children are of public health concern and will affect later school performance. Grant Funding Source : Micronutrient Initiative, Canada
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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 | 0.000 | 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".