A diet related prompt to reduce meat consumption - a field study in two staff restaurants
Bibliographic record
Abstract
Introduction: High meat consumption in developed countries contributes to climate change and causes health issues. Objective: The objective of the study was to test an intervention to reduce meat consumption in staff restaurants. Materials and Methods: The intervention was a diet-related prompt that suggested choosing vegetarian options more often and visualized that the Swiss population consumes more meat than the national dietary guidelines recommend. The study took place simultaneously in two staff restaurants, with a two-week baseline period and a two-week intervention period. One staff restaurant’s customers worked on topics unrelated to nutrition, and the other’s customers worked on food- or health-related issues. Participants (N = 131) photographed their food choices using a camera with a depth sensor. The amount of meat was measured using an artificial intelligence-based dietary assessment system called goFOODTM. Results: During the intervention, participants in the staff restaurant with customers who work on topics unrelated to nutrition preferred a vegetarian option over a meat menu option more often than at baseline. They mainly reduced their meat consumption by switching from the meat menu to the vegetarian menu rather than taking less meat fromthe buffet. A positive attitude toward environmental protection increased this effect. In the other staff restaurant—customers had already consumed lower amounts of meat at baseline—the intervention did not further reduce meat consumption. Conclusions: The intervention could also reduce meat consumption in other restaurants where customers are not too knowledgeable about dietary issues, especially among customers with positive environmental attitudes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".