Implementing Elementary School Nutrition Policy: Principals’ Perspectives
Bibliographic record
Abstract
PURPOSE: We assessed principals' perceptions about the level of school nutrition policy (SNP) implementation in Prince Edward Island elementary schools, objectively evaluated how closely elementary schools are following SNP regulations for types and frequency of foods offered at school, and explored principals' beliefs about the key enablers and barriers to SNP implementation. METHODS: Phase I involved a cross-sectional survey of principals' assessment of perceived and actual adherence to SNP components. Phase II included in-depth interviews to explore principals' perceptions about factors influencing policy adherence. Descriptive statistics were generated. Thematic content analysis was used to identify themes. RESULTS: Forty-one (93%) principals participated in Phase I, and nine of these participated in Phase II. The level of implementation of SNP components varied. Seventy-four percent of all foods sold were categorized as allowed by the SNP; 68% of schools sold at least one "not allowed" food. Key barriers included lost revenue, a higher cost of healthy foods, and limited availability of policy-allowed foods. Enablers were a high level of community support, ready access to food suppliers, and active parent volunteers. CONCLUSIONS: While schools are making progress in implementing the SNP, challenges remain. Identifying and communicating strategies for healthy fundraising activities and finding ways to involve parents in SNP implementation are recommended.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".