The Impact of Nutrition Labeling on Menus: A Naturalistic Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To examine the impact of a calorie label intervention on cafeteria menus. METHODS: Exit surveys were conducted in a university cafeteria. Participants were surveyed at baseline and one week after calorie labels were displayed. We assessed changes in noticing and use of nutrition information, the calorie content of food purchased, and estimated calorie consumption. RESULTS: The intervention was associated with significant increases in noticing nutrition information (92.5% vs 39.6%; p < .001), and the use of nutrition information to guide food purchases (28.9% vs 8.8%; p < .001). The calorie content of foods purchased decreased after calorie labels were posted (B = -88.69, p = .013), as did the estimated amount of calories consumed (B = -95.20, p = .006). CONCLUSIONS: Findings suggest that displaying calorie amounts on menus can help reduce excess energy intake.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".