School Nutrition Policy
Bibliographic record
Abstract
Background. School policies limiting the availability of sweetened beverages are often considered to be effective interventions for improving children’s diet and weight-related health. This study was designed to examine the effectiveness of the Rhode Island Healthier Beverage Policy in reducing consumption of unhealthy beverages and in producing changes in children’s weight status. Method. Students in 2 public middle schools in Rhode Island completed self-reported measures of dietary intake and were measured for height and weight prior to and 1 year following the implementation of a state-mandated healthier beverage policy. An inventory of beverages available in vending machines after the beverage policy was implemented provided a measure of adherence with the statewide policy. Results. Both surveyed schools demonstrated compliance with the beverage policy (ie, greater than 70% of available beverages complied). Self-reported consumption of sweetened beverages did not change significantly following policy implementation. Neither average BMI percentile for age and gender nor frequency of children in each weight category changed significantly 1 year after the policy was implemented. Conclusions. Although the healthier beverage policy was effectively implemented, it did not result in changes in self-reported sweetened beverage consumption or weight status 1 year later. Additional school policy and individual-level changes appear to be necessary to effect change in weight and dietary outcomes for children.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.010 |
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".