A Study of School Lunch Food Choice and Consumption among Elementary School Students
Bibliographic record
Abstract
Background: School lunches that are part of the National School Lunch Program must meet specific nutrition requirements. It is unknown whether students eat school lunches in a balanced manner. This study examined which lunch food items children eat/toss, and consumption differences between sexes, and between students at a Non-Title 1 school where most students purchase school lunches and a Title 1 school where most students receive free or reduced-priced lunches. Methods: Students in both schools were observed unobtrusively for five consecutive days. Trained observers recorded the discarded food items on a checklist listing the menu items for the day. The final analyses (t- tests, z tests) included data from 2,826 student-meals. Results: Entrees similar to those offered in fast food restaurants (i.e., chicken nuggets, pizza, nachos, corndogs) were favored by most students. Fresh fruits and vegetables were not selected or consumed frequently. Boys consumed more food in the fast food, starches, and dessert categories, while girls consumed more soup, salads, and vegetables. Title 1 school students consumed more food in all categories except bread, pasta, and sandwiches than Non-Title 1 school students. Conclusions: Students’ preference of unhealthy lunch items may decrease the health benefits that school lunches attempt to provide.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".