"Greenlight Study": A Controlled Trial of Low-Literacy, Early Childhood Obesity Prevention
Bibliographic record
Abstract
Children who become overweight by age 2 years have significantly greater risks of long-term health problems, and children in low-income communities, where rates of low adult literacy are highest, are at increased risk of developing obesity. The objective of the Greenlight Intervention Study is to assess the effectiveness of a low-literacy, primary-care intervention on the reduction of early childhood obesity. At 4 primary-care pediatric residency training sites across the US, 865 infant-parent dyads were enrolled at the 2-month well-child checkup and are being followed through the 24-month well-child checkup. Two sites were randomly assigned to the intervention, and the other sites were assigned to an attention-control arm, implementing the American Academy of Pediatrics' The Injury Prevention Program. The intervention consists of an interactive educational toolkit, including low-literacy materials designed for use during well-child visits, and a clinician-centered curriculum for providing low-literacy guidance on obesity prevention. The study is powered to detect a 10% difference in the number of children overweight (BMI > 85%) at 24 months. Other outcome measures include observed physician–parent communication, as well as parent-reported information on child dietary intake, physical activity, and injury-prevention behaviors. The study is designed to inform evidence-based standards for early childhood obesity prevention, and more generally to inform optimal approaches for low-literacy messages and health literacy training in primary preventive care. This article describes the conceptual model, study design, intervention content, and baseline characteristics of the study population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".