Exploring Implementation of the Ontario School Food and Beverage Policy at the Secondary-School Level: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore the implementation of the Ontario School Food and Beverage Policy (P/PM 150) from the perspective of secondary-school students. METHODS: This research, informed by the ANGELO framework, undertook three focus groups with secondary students (n = 20) in 2 school boards representing both high- and low-income neighbourhoods in fall 2012. Focus groups were transcribed verbatim for subsequent analysis. Key themes were generated deductively from the research objectives and inductively as they emerged from transcripts. RESULTS: Perceived impacts of P/PM 150 included high-priced policy-compliant food for sale, lower revenue generation, and food purchased off-campus. Limited designated eating spaces, proximity to external, nonpolicy-compliant food, and time constraints acted as key local level barriers to healthy eating. CONCLUSIONS: Pricing strategies are needed to ensure that all students have access to nutritious food, particularly in the context of vulnerable populations. Recognition of the context and culture in which school nutrition policies are being implemented is essential. Future research to explore the role of public health dietitians in school nutrition policy initiatives and how to leverage local resources and stakeholder support in low income, rural and remote populations is needed.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".