California Middle and High School Students Report Wanting Fresh and Healthy School Lunch in the Context of Universal School Meals
Bibliographic record
Abstract
OBJECTIVE: To evaluate students' perceptions of school lunches served when they were offered free of charge to all students. DESIGN: Cross-sectional qualitative study using focus groups. SETTING: California students interviewed virtually. PARTICIPANTS: Middle school (n = 36) and high school (n = 31) students from a racially and economically diverse sample. MAIN OUTCOME MEASURE: Students' perceptions of school lunch. ANALYSIS: Thematic analysis using immersion-crystallization methodology. RESULTS: Students desire fresh and healthy school lunches. Students defined fresh as food prepared on-site, from scratch, and not prepackaged or frozen, and healthy as food that contains fruits and vegetables. Many students perceived the main entrees to be the least healthy and fresh part of school lunch and fruits and vegetables to be the most healthy and fresh; however, some students reported the fruits and vegetables were not always fresh or palatable. CONCLUSIONS AND IMPLICATIONS: Students value fresh and healthy free school lunches, but they have somewhat limited definitions of what constitutes healthy. Schools can better meet student preferences for fresh and healthy foods to ensure that meals served free of charge are nourishing and palatable to all students while improving nutrition education such that students understand the components of a healthy meal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".