Insights from consumers' exposure to environmental nutrition information on a dashboard for improving sustainable healthy food choices
Bibliographic record
Abstract
Over the last decade, there has been a growing demand for tools to support sustainable healthy lifestyles, including food choices. Through a survey, this study examined the influence of environmental nutrition information conveyed with aids such as nudges and traffic light labels through a Dashboard for Improving Sustainable Healthy (DISH v1.0) food choices on consumer purchase intentions. DISH is an application that enables end-users to envisage and compare the potential impacts of their choices before purchasing. In the early stage of the technological development of DISH, the environmental nutrition information of two fast-food menus, plant-based and animal-based burgers, was tested among 112 respondents from a university campus. The results suggested that with an environmental nutrition score, less cognitive processing was required to make sustainable healthy choices. Among the 90.2% of respondents with a predisposed purchase intention for animal-based burgers, 56.9% reported a purchasing intent for plant-based burgers. More than 83% attributed their decision to the environmental nutrition information provided on DISH. 64.3% of respondents rated DISH as 4 stars or 5 stars, suggesting the perceived usefulness of the application. A statistical investigation of the results indicated that features of the DISH application, nudges, and awareness considerably influenced sustainable choices (sig<0.001). The results support digital innovations as essential drivers for reinforcing environmental nutrition messages and stimulating subtle dietary changes. These preliminary results have served as a precursor for ongoing studies on other university campuses and corporate institutions testing the long-term impact of DISH v2.0 in stimulating dietary change.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".