Healthy Eating Guidelines for a School Jurisdiction: Collaborative Design and Implementation
Bibliographic record
Abstract
PURPOSE: Healthy eating is a determinant of optimal growth, and schools provide an ideal setting in which to influence students' diets. The Healthy Eating Guidelines Initiative (HEGI) was a partnership among education, health, and community stakeholders to develop and implement healthy eating guidelines across a school jurisdiction. An evaluation was conducted to examine the potential impact of the HEGI on the school food environment and students' self-reported diets. METHODS: All schools in the jurisdiction were invited to participate in the evaluation. Participating schools included elementary, middle, high, and mixed grades schools. A school environment assessment and a student questionnaire were used to collect data before and after the HEGI. RESULTS: Twenty-two (71%) of 31 schools participated in the evaluation. The guidelines were successfully implemented in 17 of these 22 schools. Overall, a greater proportion of students reported healthier eating behaviours at the conclusion of the HEGI. In particular, a greater proportion of students in schools with cafeteria-style food service showed significantly improved self-reported dietary behaviours. These changes were not seen among students at schools with limited or no on-site food service. CONCLUSIONS: The findings are consistent with those of previous studies, and indicate that guidelines for a school jurisdiction can have a positive impact on the school food environment and students' food intake. The HEGI shows promise as a strategy to promote healthy eating among students.
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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 | 0.102 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".