Students’ Perspectives on the Benefits and Challenges of Universal School Meals Related to Food Accessibility, Stigma, Participation, and Waste
Bibliographic record
Abstract
OBJECTIVE: To reveal students' experiences and perspectives related to Universal School Meals (USM) under the federal coronavirus disease 2019 waivers during school years 2021-22. DESIGN: Qualitative; 17 focus groups in June-July 2022. SETTING: Virtual; students from 9 California regions in public and charter schools. PARTICIPANTS: 67 students (n = 31 in high school, n = 36 in middle school) from a racially and economically diverse sample. PHENOMENON OF INTEREST: Students' perceived benefits and drawbacks of USM. ANALYSIS: Thematic analysis using an immersion-crystallization approach. RESULTS: Students appreciated USM for increasing school meals' accessibility, promoting food security by financially supporting families, reducing the stigma associated with school meals, simplifying the payment system, and enhancing school meals convenience. An increase in school meal participation was observed. However, concerns emerged regarding a perceived decline in food quality and quantity and increased food waste. CONCLUSIONS AND IMPLICATIONS: Universal School Meals showed promise in increasing access to meals, reducing food insecurity, stigma, and increasing participation. Addressing food quality, quantity, and waste concerns is critical for its sustained success. Policymakers need to advocate for the expansion and continuous refinement of USM, prioritizing stakeholder feedback. Ensuring adequate funding to balance meal quality and quantity while minimizing waste is essential for an adequate school meal policy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".