Would Students Prefer to Eat Healthier Foods at School?
Bibliographic record
Abstract
BACKGROUND: This study sought to elucidate students' perceptions of school food environments and to assess correlations between perceptions and purchasing and consumption behaviors at school. METHODS: Seventh and ninth graders (n = 5365) at 19 schools in multiethnic, low-income California communities participating in the Healthy Eating Active Communities program completed questionnaires assessing their attitudes and behaviors regarding school food environments during spring 2006. RESULTS: Most students (69%) reported that fresh fruit was important to be able to buy at school; more than chips (21%), candy (28%), or soda (31%). Reported importance of food offerings was correlated with the consumption of those items. Most students did not perceive foods/beverages offered at school to be healthy; fewer than a quarter reported eating fruits or vegetables (FV) at school. Students eating school lunch were more than twice as likely to consume FV, though if they also purchased from competitive venues, their consumption of candy, chips, and soda was similar to their peers who purchased only competitive foods. CONCLUSION: Students report healthy foods to be important to be able to buy at school, but do not perceive their school food environment to be healthy and consume more unhealthy foods at school. Students served healthy items via school lunch are more likely to consume them; however, they also purchase and consume unhealthy items if available. Findings suggest that modifying school food environments to facilitate consumption of healthy foods and limit unhealthy foods will better match students' preferences and could lead to improved dietary intake.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".