Growth and development among infants and preschoolers in rural India
Bibliographic record
Abstract
Economic inequities are common in low and middle-income countries (LMIC), and are associated with poor growth and development among young children. The objectives are to examine whether maternal education and home environment quality: 1) protect children by attenuating the association between economic inequities and children’s growth and development, or 2) promote children’s growth and development, regardless of economic inequities. The sample includes 512 infants and 321 preschoolers in 26 villages in rural India (Project Grow Smart). Data for children: physical growth (weight and length/height measured) and development (Mullen Scales of Early Learning); for mothers/households: economic inequities measured by household assets, education, depressive symptoms, and home environment (HOME Inventory). Data are analyzed with linear mixed models (LMM) for infants and preschoolers separately, adjusted for village/preschool clustering, including asset-by-education/home interactions. Among infants, but not preschoolers, the education/home factor attenuates relations between assets and growth, eliminating differential relations in infant growth between high/low-asset families, suggesting protection. Among infants and preschoolers, the education/home factor is significantly or marginally associated with most child development scales, regardless of economic inequities, suggesting promotion. Strategies to enhance maternal education and home environment quality may protect infants in low-asset families from poor growth, promote development among infants and preschoolers, and prevent early disparities.
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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 | 0.001 |
| 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".