State regulation of school food environments is not enough
Bibliographic record
Abstract
US States have adopted policies to decrease the availability of competitive foods using regulation, procedures, or resolutions in hopes of preventing obesity. Regulation is often cited as the strongest policy choice, but little is known about which policy instruments will be most effective. We used the Early Childhood Longitudinal Study Kindergarten cohort 2004 and 2007 panels to examine whether children in states that adopted policy instruments by 2004, including regulation, observed subsequent reductions in: 1) school‐level availability, 2) child‐level purchase and consumption of competitive foods and 3) reduction in risk of obesity. We used logistic regression to control for individual and state‐level confounders. We found that no policy instrument, including regulation, was associated with less availability or fewer purchases in school. Furthermore, children in states that passed regulation increased their consumption of sweetened beverages more and had higher prevalence of obesity. States that had the greatest need of policy action because of high obesity rates adopted policies focusing on the school food environment, but the policies did not appear to alter children's consumption or obesity risk. More research is needed into contextual factors that may prevent proper design and implementation of policies.
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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 | 0.007 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".