School Lunch Environmental Factors Impacting Fruit and Vegetable Consumption
Bibliographic record
Abstract
OBJECTIVE: Assess impact of school lunch environmental factors on fruit and vegetable (F&V) consumption in second and third grade students. DESIGN: Cross-sectional observations in 1 school year. PARTICIPANTS: Students from 14 elementary schools in 4 New York City boroughs (n = 877 student-tray observations). MAIN OUTCOME MEASURE(S): Dependent variables were F&V consumption collected by visual observation. Independent variables included school lunch environmental factors, and individual-level and school-level demographics. ANALYSIS: Hierarchical linear modeling was used with F&V consumption as the outcome variable, and relevant independent variables included in each model. RESULTS: Slicing or precutting of fruits and having lunch after recess were positively associated (P < .05) with .163- and .080-cup higher fruit consumption across all students, respectively. Preplating of vegetables on lunch trays, having 2 or more vegetable options, and having lunch after recess were positively associated (P < .05) with .024-, .009-, and .007-cup higher vegetable consumption across all students, respectively. CONCLUSIONS AND IMPLICATIONS: Although there was a small increase in intake, results of the study support that some school lunch environmental factors affect children's F&V consumption, with some factors leading to more impactful increases than others. Slicing of fruits seems most promising in leading to greater fruit consumption and should be further tested.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".