Secondary School Nutrition Policy Compliance in Ontario and Alberta, Canada: A Follow-Up Study Examining Vending Machine Data from the COMPASS Study
Bibliographic record
Abstract
(1) Objective: To longitudinally assess food and beverages sold in vending machines in secondary schools (grades 9–12) participating in the COMPASS study (2015/2016 and 2018/2019) and (2) to examine if patterns and trends observed in previous years (2012/2013 to 2014/2015) are consistent with lack of policy compliance in Ontario and Alberta, Canada. (2) Methods: Policy compliance was assessed through comparing nutritional information on drink (e.g., sports drinks) and snack (e.g., chocolate bars) products in vending machines to Policy and Program Memorandum (P/PM) 150 in Ontario (required policy) and the Alberta Nutrition Guidelines for Children and Youth (recommended policy). Longitudinal results and descriptive statistics were calculated. (3) Results: Longitudinal results indicate that between Y4 (2015/2016) and Y7 (2018/2019), snack and drink vending machines remained mostly non-compliant in Ontario and Alberta, with a small proportion of Ontario drink machines changing from non-compliant to compliant. At the school level, descriptive results indicate the proportion of Ontario schools with policy-compliant snack and drink machines decreased between Y4 and Y7. Alberta schools were non-compliant for drink and snack machines. (4) Conclusions: Secondary schools continue to be non-compliant with provincial policies. School nutrition policies need to be simplified in order to make it easier for schools to be compliant. Enforcement of compliancy is also an area that deserves consideration.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".