Prevalence and Implementation Practices of School Salad Bars Across Grade Levels
Bibliographic record
Abstract
PURPOSE: To assess the prevalence of school salad bars in Arizona and to describe common practices of salad bar use among school nutrition managers across grade level. DESIGN: Cross-sectional web-based surveys. PARTICIPANTS: School nutrition managers from elementary, middle, high, and K-12 schools (N = 648). MEASURES: Prevalence of salad bars; implementation practices such as years with salad bar, salad bar type, location, monitor, and reimbursement practices; and food-related components of salad bars including frequency of items, popular items, and sources of food. ANALYSIS: Descriptive analyses were conducted including Fisher exact test, analysis of variance, and the Kruskal-Wallis test comparing practices across grade level (elementary, middle, high, and K-12 schools). RESULTS: Overall, 61.1% of respondents had a salad bar; there were significant differences in the prevalence across grade level: elementary, middle, high, and K-12 schools had prevalence of 67.3%, 75.0%, 45.5%, and 51.1%, respectively ( P < .001). We observed significant differences in the implementation and food-related components of salad bars across grade levels including type, salad bar location, sources of food, and frequency of serving cut vegetables. CONCLUSION: This study provides insights on the prevalence of salad bars and is the first to report on implementation practices of school salad bars. These results may also guide the development of interventions for nutrition educators to use for the promotion fruit and vegetable consumption via school salad bars.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".