Parent Perceptions of School Meals Influence Student Participation in School Meal Programs
Bibliographic record
Abstract
OBJECTIVE: To evaluate if parent perceptions of school meals influence student participation. DESIGN: In May 2022, an online survey was used to evaluate parents' perceptions of school meals and their children's participation. PARTICIPANTS: A total of 1,110 California parents of kindergarten through 12th-grade students. MAIN OUTCOME MEASURES: Student participation in school lunch and breakfast. ANALYSIS: Principal component analysis and Poisson regression models. RESULTS: Three groups of parental perceptions were identified: (1) positive perceptions (eg, liking school meals and thinking that they are tasty and healthy), (2) perceived benefits to families (eg, school meals save families money, time, and stress), and (3) negative (eg, concerns about the amount of sugar in school meals and stigma). More positive parental perceptions about school meals and their benefits to families were associated with greater student meal participation. In contrast, more negative parental perceptions were associated with reduced student participation in school meals (P < 0.05). CONCLUSION AND IMPLICATIONS: Parent perceptions of school meals may affect student participation in school meal programs. Working to ensure parents are familiar with the healthfulness and quality of school meals and the efforts schools are making to provide high-quality, appealing meals may be critical for increasing school meal participation rates.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".