An experimental design and implementation protocol for testing a dashboard for improving sustainable healthy food choice
Bibliographic record
Abstract
Within the last decade, one of the crucial efforts to reduce environmental impact and improve consumers' health has focused on shifting food choices. In our previous study, the authors developed a customizable and adaptable Dashboard for Improving Sustainable Healthy (DISH) food choices. DISH leverages nudge and traffic-light labels to enable consumers to compare and envision the potential environmental, nutritional, and health impacts of their food choices before purchasing. An initial test among 112 individuals through an online survey revealed the potential of the tool to shift purchase intentions among consumers on a university campus. As part of a second phase in a series of consumer evaluations, we provide a step-by-step protocol followed to investigate the effectiveness of a version of DISH (McGill DISH) in stimulating subtle dietary changes on another university campus.•Environmental nutrition information on DISH was communicated in simple but intuitive ways through multiple technological media (self-service kiosks and mobile applications) to stimulate dietary change.•The study participants were randomly separated into treatment and control groups.•We hypothesize that the participants in the treatment group are more likely to engage with food products that are more sustainable and healthier on the DISH application compared to the control group.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".