Examining local-level factors shaping school nutrition policy implementation in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Increasing numbers of overweight and obese youth draw attention to the school as an important setting for targeted nutrition interventions, given that it is where they spend a majority of their waking time. The objective of the present study was to explore local-level factors shaping the implementation of a school nutrition policy. DESIGN: In-depth, semi-structured interviews were conducted in person or via the telephone (a maximum of 60 min). An interview guide was informed by the Analysis Grid for Environments Linked to Obesity (ANGELO) framework, research objectives and literature. Key themes centred on policy implementation, including facilitators and barriers (i.e. resources, capacity), user satisfaction (i.e. students) and communication strategies. SETTING: Secondary schools in Ontario, Canada. SUBJECTS: Twenty-two participants from local agencies supporting school nutrition programming (n 8) and secondary-school principals, vice principals and teachers (n 14) from nine schools across three Ontario school boards. RESULTS: Results are organized according to environments outlined in the ANGELO framework. The cost of healthy food for sale, revenue loss (economic), proximity of schools to off-site food outlets (physical), the restrictive nature of policy, the role of key stakeholders (political), the role of stigma and school culture (sociocultural) act as local-level barriers to policy implementation. CONCLUSIONS: Gaps in policy implementation include the high cost of food for sale and subsequent revenue generation, the close proximity of internal and external food environments, the need for consultation and communication between stakeholders, and strategies to reduce stigma and improve the school nutrition culture.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 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".