State‐Level School Competitive Food and Beverage Laws Are Associated With Children's Weight Status
Bibliographic record
Abstract
BACKGROUND: This study attempted to determine whether state laws regulating low nutrient, high energy-dense foods and beverages sold outside of the reimbursable school meals program (referred to as "competitive foods") are associated with children's weight status. METHODS: We use the Classification of Laws Associated with School Students (CLASS) database of state codified law(s) relevant to school nutrition. States were classified as having strong, weak, or no competitive food laws in 2005 based on strength and comprehensiveness. Parent-reported height and weight along with demographic, behavioral, family, and household characteristics were obtained from the 2007 National Survey of Children's Health. Bivariate and logistic regression analyses estimated the association between states' competitive food laws and children's overweight and obesity status (body mass index [BMI]-for-age ≥85th percentile). Children (N = 16,271) between the ages of 11-14 years with a BMI for age ≥5th percentile who attended public school were included. RESULTS: Children living in states with weak competitive food laws for middle schools had over a 20% higher odds of being overweight or obese than children living in states with either no or strong school competitive food laws. CONCLUSION: State-level school competitive food and beverage laws merit attention with efforts to address the childhood obesity epidemic. Attention to the specificity and requirements of these laws should also be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".