Postinfancy growth, schooling, and cognitive achievement: Young Lives
Bibliographic record
Abstract
BACKGROUND: Early life growth failure and resulting cognitive deficits are often assumed to be very difficult to reverse after infancy. OBJECTIVE: We used data from Young Lives, which is an observational cohort of 8062 children in Ethiopia, India, Peru, and Vietnam, to determine whether changes in growth after infancy are associated with schooling and cognitive achievement at age 8 y. DESIGN: We represented the growth by height-for-age z score at 1 y [HAZ(1)] and height-for-age z score at 8 y that was not predicted by the HAZ(1). We also characterized growth as recovered (stunted at age 1 y and not at age 8 y), faltered (not stunted at age 1 y and stunted at age 8 y), persistently stunted (stunted at ages 1 and 8 y), or never stunted (not stunted at ages 1 and 8 y). Outcome measures were assessed at age 8 y. RESULTS: The HAZ(1) was inversely associated with overage for grade and positively associated with mathematics achievement, reading comprehension, and receptive vocabulary. Unpredicted growth from 1 to 8 y of age was also inversely associated with overage for grade (OR range across countries: 0.80-0.84) and positively associated with mathematics achievement (effect-size range: 0.05-0.10), reading comprehension (0.02-0.10), and receptive vocabulary (0.04-0.08). Children who recovered in linear growth had better outcomes than did children who were persistently stunted but were not generally different from children who experienced growth faltering. CONCLUSIONS: Improvements in child growth after early faltering might have significant benefits on schooling and cognitive achievement. Hence, although early interventions remain critical, interventions to improve the nutrition of preprimary and early primary school-age children also merit consideration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".