Where Should We Eat? Lunch Source and Dietary Measures Among Youth During the School Week
Bibliographic record
Abstract
PURPOSE: To examine lunch sources during the school week among students and the associations with fruits and vegetable (F&V) and sugar-sweetened beverage (SSB) consumption. METHODS: Students (n = 23 680) from 43 Ontario, Canada, secondary schools completed a health behaviour survey in the Year 1 COMPASS study. Analysis used generalized linear mixed effects models. RESULTS: The most frequently reported lunch source was home (2.9 days per school week), then the school cafeteria (1.1) and fast-food places or restaurants (FFRs) (0.9). Eating a home lunch was associated with having less spending money, white ethnicity, and females; whereas cafeteria lunch was associated with more spending money, lower school grade, and females. A FFR lunch was associated with males, more spending money, and higher physical activity. Greater frequency of a home lunch was associated with greater F&V consumption. Greater frequency of a FFR lunch was associated with more frequent SSB consumption. Cafeteria lunches were associated with increases in both SSB and F&V. CONCLUSIONS: Eating a lunch obtained from outside of the home is a regular behaviour among students. Sources of school-week lunches may have an important influence on dietary intake among youth. These findings reinforce the need for strategies to promote healthier lunch sources and healthier food options.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".