Point-of-Purchase Labels and Reward Cards Improve Sales of Healthy Foods in University Dining Halls
Bibliographic record
Abstract
PURPOSE: To compare sales of Food Resources and Education for Student Health (FRESH) Approved versus non-FRESH Approved menu cycle items pre- and postimplementation of the FRESH program. METHODS: Sales data from 2011-2015 of FRESH Approved versus non-FRESH Approved menu items were analyzed. Fruit and milk items sold, net sales, and the cost of free fruit and milk redeemed through the FRESH Reward Card (FRC) program, were also analyzed. RESULTS: FRESH Approved items sold more often than non-FRESH Approved items in the latter 2 years (P = 0.01). Prices of FRESH Approved menu items were significantly lower than non-FRESH Approved items for all years (e.g., $1.52 ± $0.94 vs $2.21 ± $1.02 per serving in 2014-2015; P < 0.001). Across all FRESH implementation years, FRESH Approved menu items were found more often on the 6-week menu (P < 0.05). The number of fruit items sold increased from a baseline of 143 052 to 170 954, and net sales increased from $135 450 to $154 248 after 3 years of the FRC implementation. CONCLUSIONS: FRESH Approved items were less expensive, available more often, and had higher sales. The FRC increased net fruit sales despite the cost of free fruit. Highlighting and reducing the cost of healthy foods are promising practices to improve campus food environments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".