Comparing Diet Quality of School Meals versus Food Brought from Home
Bibliographic record
Abstract
Purpose: Consuming nutritious food is essential to learning. The purpose of this research was to determine the diet quality of elementary school lunches, both those in meal programs and those bringing food from home, in urban and rural locations in Saskatchewan. Methods: Using a School Food Checklist and digital photography we compared food group servings and diet quality in 3 school types: urban schools with a meal program and urban and rural schools without a meal program. The total sample was 773 students. Results: Only 55% of students brought the minimum number of servings for grain products and meat and alternatives, with fewer bringing the minimum for vegetables and fruit (25.6%–34.9%), whole grains (24.1%), and milk and alternatives (14.1%). Students bringing food from home had significantly more calories in their lunches from minimally nutritious foods. Students in meal programs had the highest diet quality scores using the Healthy Eating Index adapted for school hours. Conclusions: The diet quality of elementary students’ lunches needs improvement, although students in meal programs have healthier diets. Interventions targeting what children eat at school should focus on increasing the number of students meeting recommendations for healthy foods while decreasing minimally nutritious foods brought to school.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".