Exploring the School Nutrition Policy Environment in Canada Using the ANGELO Framework
Bibliographic record
Abstract
Excess body weight has become a major public health issue. Given the link between poor nutrition, obesity, and chronic disease in youth, increasing attention is being paid to the school as an ideal setting for promoting nutritious eating practices. Informed by the ANGELO (Analysis Grid for Environments Linked to Obesity) framework, we employ a documentary analysis to investigate the context of school nutrition in Canada, particularly the relationship between regional- and upper-level policies. In doing so, we examine policy documents and technical reports across three levels. We used mixed methods to analyze relevant English language policy documents and technical reports across Canada (n = 58), published between 1989 and 2011. Results reveal distinct differences across federal, provincial, and regional levels. The availability of nutritious food in schools and having nutrition education as part of the curriculum were key components of the physical environment across federal and provincial levels. Federal and provincial priorities are guided by a health promotion framework and adopting a partnership approach to policy implementation. Gaps in regional-level policy include incorporating nutrition education in the curriculum and making the link between nutrition and obesity. Policy implications are provided, in addition to future research opportunities to explore the connections between these environments at the local level.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".