<i>Developing School Nutrition Policies:</i> Enabling and Barrier Factors
Bibliographic record
Abstract
PURPOSE: The study was conducted to identify the enabling and barrier factors in the development of nutrition policies in Prince Edward Island elementary and consolidated schools. METHODS: A document review and in-depth interviews were conducted with key stakeholders (n=12). RESULTS: Principals were identified as important champions for change. Working group members created an interface between the school world and the nutrition world, and drew upon common philosophical ground to work together to lead the change process. Successfully navigating the process of policy development required building a case for change, testing policies in the real world, integrating healthy eating within school life, offering support to schools, engaging participants, and acknowledging the need to weigh the costs and benefits of the change. At times, external pressures on schools and available foods varied in the extent to which they enabled or challenged policy development. Finally, resource limitations, competing issues, and the use of unhealthy food as rewards were identified as the primary barriers. CONCLUSIONS: The use of a consultative approach that engages key stakeholders early in the process is critical to the successful development of school nutrition policies. This approach also may be an important predictor of the long-term success of such initiatives.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".