Adaptation and Validation of a Nutrition Environment Measures Survey for University Grab-and-Go Establishments
Bibliographic record
Abstract
PURPOSE: To adapt and validate a survey instrument to assess the nutrition environment of grab-and-go establishments at a university campus. METHODS: A version of the Nutrition Environment Measures Survey for grab-and-go establishments (NEMS-GG) was adapted from existing NEMS instruments and tested for reliability and validity through a cross-sectional assessment of the grab-and-go establishments at the University of Toronto. Product availability, price, and presence of nutrition information were evaluated. Cohen's kappa coefficient and intra-class correlation coefficients (ICC) were assessed for inter-rater reliability, and construct validity was assessed using the known-groups comparison method (via store scores). RESULTS: Fifteen grab-and-go establishments were assessed. Inter-rater reliability was high with an almost perfect agreement for availability (mean κ = 0.995) and store scores (ICC = 0.999). The tool demonstrated good face and construct validity. About half of the venues carried fruit and vegetables (46.7% and 53.3%, respectively). Regular and healthier entrée items were generally the same price. Healthier grains were cheaper than regular options. Six establishments displayed nutrition information. Establishments operated by the university's Food Services consistently scored the highest across all food premise types for nutrition signage, availability, and cost of healthier options. CONCLUSIONS: Health promotion strategies are needed to address availability and variety of healthier grab-and-go options in university settings.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".