A Qualitative Exploration of Parent and School Staff Perceptions of Freshly Prepared Meals and Food Waste Implications in California Elementary Schools
Bibliographic record
Abstract
OBJECTIVE: Explore parent and school staff perceptions of freshly prepared school lunches. DESIGN: Cross-sectional qualitative study, occurring May through June, 2024. SETTING: Urban California elementary schools that recently introduced freshly prepared lunches. PARTICIPANTS: Parents (n = 23) participated in 5 virtual focus groups, convenience sampled; school staff (n = 19) participated in interviews, purposively sampled. PHENOMENON OF INTEREST: Perceptions of scratch cooking. ANALYSIS: Verbatim transcripts were coded and analyzed using the framework method. RESULTS: Participants believed the freshly prepared meals transition was successful, citing new menu items, helpful foodservice staff, and universal school meals as beneficial. Freshly prepared meals were positively perceived as creating healthier options and increasing the cultural diversity of the menu. Participants were concerned about food waste implications, and identified reasons including requirements to serve all entree components and large serving sizes, limited lunch time, students changing their minds on lunch order, foods not cooked properly or served at the right temperature, and menu criticism. CONCLUSIONS AND IMPLICATIONS: Parents and school staff broadly endorse more freshly prepared school lunch options. Our findings support investment in on-site kitchen infrastructure and training for foodservice staff in scratch-cooking techniques to support elementary schools in providing more desirable, fresh foods and reducing waste from school lunch.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".