Integrating nutrition and early child‐development interventions among infants and preschoolers in rural India
Bibliographic record
Abstract
This article describes the development, design, and implementation of an integrated randomized double-masked placebo-controlled trial (Project Grow Smart) that examines how home/preschool fortification with multiple micronutrient powder (MNP) combined with an early child-development intervention affects child development, growth, and micronutrient status among infants and preschoolers in rural India. The 1-year trial has an infant phase (enrollment age: 6-12 months) and a preschool phase (enrollment age: 36-48 months). Infants are individually randomized into one of four groups: placebo, placebo plus early learning, MNP alone, and MNP plus early learning (integrated intervention), conducted through home visits. The preschool phase is a cluster-randomized trial conducted in Anganwadi centers (AWCs), government-run preschools sponsored by the Integrated Child Development System of India. AWCs are randomized into MNP or placebo, with the MNP or placebo mixed into the children's food. The evaluation examines whether the effects of the MNP intervention vary by the quality of the early learning opportunities and communication within the AWCs. Study outcomes include child development, growth, and micronutrient status. Lessons learned during the development, design, and implementation of the integrated trial can be used to guide large-scale policy and programs designed to promote the developmental, educational, and economic potential of children in developing countries.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".