Adolescent mothers’ anthropometrics and grandmothers’ schooling predict infant anthropometrics in Ethiopia, India, Peru, and Vietnam
Bibliographic record
Abstract
We investigated intergenerational associations of adolescent mothers' and grandmothers' anthropometrics and schooling with adolescent mothers' offspring's anthropometrics in Ethiopia, India, Peru, and Vietnam. We examined birthweight (n = 283), birthweight Z-score (BWZ), conditional growth in weight-for-age Z-score (cWAZ, residuals from a regression of WAZ at last survey round on BWZ, sex, and age), and height-for-age Z-score (HAZ) of children born to older cohort adolescent girls in the Young Lives study. Our key independent variables were adolescent mothers' body size: HAZ and body-mass-index-for-age Z-score (BMIZ) at age 8, conditional HAZ (cHAZ, residuals from a regression of HAZ at the end of a growth period on prior HAZ, age, and sex), conditional BMIZ growth (cBMIZ, calculated analogously), and grandmaternal BMIZ, HAZ, and schooling. We adjusted for child, maternal, and household characteristics. Adolescent mothers' cHAZ (ages 8-15) predicted birthweight (β = 130 g, 95% confidence interval (CI) 31-228), BWZ (β = 0.31, CI 0.09-0.53), and cWAZ (β = 0.28, CI 0.04-0.51). Adolescent mothers' BMIZ at age 8 predicted birthweight (β = 79 g, CI 16-43) and BWZ (β = 0.22, CI 0.08-0.36). Adolescent mothers' cBMIZ (ages 12-15) predicted child cWAZ and HAZ. Grandmothers' schooling predicted grandchild birthweight (β = 22 g, CI 1-44) and BWZ (β = 0.05, CI 0.01-0.10).
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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 | 0.000 | 0.000 |
| 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".