The Healthfulness of Eateries at the University of Waterloo: A Comparison across 2 Time Points
Bibliographic record
Abstract
Purpose: The aim of this study was to assess the healthfulness of a sample of campus eateries at 2 time points, 2 years apart. Methods: Five eateries at the University of Waterloo were audited using the Nutrition Environment Measures Survey adapted for university campuses (NEMS-UC) in 2015 and late 2017–early 2018. Based on the availability of healthy options and facilitators of and barriers to healthy eating, possible NEMS-UC scores ranged from −5 to 23 points. Results: Scores were low, ranging from 7 to 14 (mean = 10.8, SD = 2.59) points in 2015 and 7 to 13 (mean = 9.6, SD = 2.19) points in 2017–2018. For all eateries except 1 residence cafeteria, scores at time 2 were the same or lower than scores at time 1. All venues carried whole fruit and vegetable options and lower-fat milks, and most offered whole-wheat options. However, healthier items were often located in low-traffic areas, priced higher than less healthy options, and sometimes limited to prepackaged items. Misleading health messaging was also evident. Conclusions: Increased availability, accessibility, and visibility of healthy offerings is needed to enhance campus food environments and support healthy eating patterns, while barriers such as contradictory messaging should be minimized.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".