Exploring Student Food Behaviour in Relation to Food Retail over the Time of Implementing Ontario’s School Food and Beverage Policy
Bibliographic record
Abstract
Background: Canadian provincial policies, like Ontario’s School Food and Beverage Policy (P/PM 150), increasingly mandate standards for food and beverages offered for sale at school. Given concerns regarding students leaving school to purchase less healthy foods, we examined student behaviours and competitive food retail around schools in a large urban region of Southern Ontario. Methods: Using a geographic information system (GIS), we enumerated food outlets (convenience stores, fast-food restaurants, full-service restaurants) within 500, 1000 and 1500 m of all 389 regional schools spanning years of policy implementation. Consenting grade 6–10 students within 31 randomly selected schools completed a web-based 24-h diet recall (WEB-Q) and questionnaire. Results: Food outlet numbers increased over time (p < 0.01); post-policy, within 1000 m, they averaged 27.31 outlets, with a maximum of 65 fast-food restaurants around one school. Of WEB-Q respondents (n = 2075, mean age = 13.4 ± 1.6 years), those who ate lunch at a restaurant/take-out (n = 84, 4%) consumed significantly more energy (978 vs. 760 kcal), sodium (1556 vs. 1173 mg), and sugar (44.3 vs. 40.1 g). Of elementary and secondary school respondents, 22.1% and 52.4% reported ever eating at fast food outlets during school days. Conclusions: Students have easy access to food retail in school neighbourhoods. The higher energy, sodium and sugar of these options present a health risk.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".