Towards a comprehensive school food environment audit tool in Canada: a systematic review of school food environment measurements and nutrition determinants
Bibliographic record
Abstract
BACKGROUND: School food programs (SFPs) support children's health, learning, and well-being, yet Canada remains one of the few high-income countries without a nationally coordinated SFP. Instead, a patchwork of independent programs has created disparities in access, funding, and quality. In 2024, the federal government released Canada's first National School Food Policy built on principles of accessibility, health promotion, inclusivity, flexibility, sustainability, and accountability, and committed $1 billion over five years. However, no clear frameworks exist for implementation or evaluation. This review examines existing measurement tools to identify captured dimensions of school food environments and student nutrition determinants, and assess alignment with Canada's National School Food Policy to inform the development of a comprehensive monitoring and evaluation tool for Canada's forthcoming national SFP. METHODS: A systematic search of peer reviewed literature published prior to 2024 was conducted to identify measurement tools used to assess school food environments. Tools were categorized using three complementary frameworks: INFORMAS (food environment), the CDC School Nutrition Environment Framework (school policies/practices), and the Graziose Framework (student behaviors). A sub-analysis of Canadian tools assessed alignment with the National School Food Policy. RESULTS: Of 695 articles screened, 101 met the inclusion criteria. Most tools used quantitative methods (61%), while others used qualitative (15%) or mixed (34%) methods. No single tool captured all relevant dimensions of school food environments or factors influencing students' nutrition behaviors. The physical dimension was most commonly captured (92%) and the economic dimension the least (26%). School meals (75%) and Smart snacks (60%) were commonly measured, while staff role modelling was rarely included (10%). Most studies measured school policy (83%) and meal-specific factors (72%). Of the 7 Canadian tools, none captured all six principles of Canada's National School Food Policy. Most tools were rated as 'medium' (48%) or 'low' quality (35%). CONCLUSIONS: Existing tools show methodological gaps and are limited coverage, highlighting the need for more comprehensive and high-quality audit tools. For Canada, such a tool must also capture all six principles of the National School Food Policy to support implementation, evaluation, and accountability of the forthcoming national SFP. TRIAL REGISTRATION: CRD42023492602.
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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 | 0.061 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.041 | 0.050 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".