Describing Food Availability in Schools Using Different Healthy Eating Guidelines: Moving Forward with Simpler Nutrition Recommendations
Bibliographic record
Abstract
PURPOSE: Internationally, there is debate on whether a nutrient or a food-based approach to policy is more effective. This study describes the food/beverage availability in schools in Nova Scotia through a comparison of a traditional nutrient classification ("Maximum/Moderate/Minimum"), currently used in the provincial school policy and a simplified food-based system ("Core/Extra"). METHODS: School food environment audits were conducted in schools (n = 25) to record the food and beverages available. Registered dietitians categorized information using both the nutrient-based and simplified food-based classification systems. Number and percent in each category were described for items. RESULTS: Food and beverage items consisted of breakfast, lunch, snacks, beverages, and vending of which 81% were permissible by the policy, whereas only 54% were categorized as Core. Many snacks and vending items classified as Extra fell within either Moderate (45% and 35%, respectively) or Minimum (29% and 33%, respectively) categories. CONCLUSIONS: Dietitians have a role to support interpretation of classification systems for school nutrition policies. The nutrient-based classification used in the policy permitted some items not essential to a healthy diet as defined by the Extra food-based classification. However, the food-based Core/Extra categorization had less detail to classify nutrients.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".