The design and development of a dashboard for improving sustainable healthy food choices
Bibliographic record
Abstract
Over the last decade, several digital tools have been designed to provide consumers with nutritional and environmental impact information about their food choices post-consumption. Many of these tools lack behavioral change modules, have low user engagement, and ignore inherent environmental nutrition trade-offs to stimulate dietary change. This study presents the design and development of a decision support system to enhance consumer health while meeting sustainability goals from a pre-consumption perspective. The proposed decision support system, Dashboard for Improving Sustainable Healthy (DISH) food choices, employs behavioral features, traffic light labels, and nudges to inform end-users about the nutritional health performance and environmental impact of meals. DISH uses a simple metric that allows end-users to explore the potential minutes of healthy and productive life gained or lost from consuming 100 kcal of a meal. The metric combines the positive or negative nutritional health effects (μ-DALYs) of consuming a meal and environmental damage (endpoint impact expressed in DALYs) on human health. In the DISH application, end-users are rewarded or deducted EnCoins, which represent the number of silver or gold coins lost or gained based on the cost ($) of environmental damage (midpoint impacts) of a meal compared to reference sustainable healthy and unsustainable and unhealthy meal. DISH's gamification module enables end-users to track the potential minutes of healthy and productive life gained/lost and gold or silver rewards or deductions from consuming 100 kcal of a selected meal through cumulative minutes gained or lost and EnCoins. In promoting a sustainable diet culture, the gamification module enables users to create groups and communities where friends and families can track their sustainability performance through meal decisions. The DISH application is currently available online and can be accessed by an end-user through any device. Further pilot studies will focus on testing the technology in partner campus cafeterias. • This study presents the development of a dashboard for improving sustainable healthy food choices. • DISH uses a metric, minutes of healthy and productive life gained or lost, to communicate the health effect of a meal. • DISH uses EnCoins to help consumers know the extent of environmental savings or the detriment of meals. • DISH helps friends and families track their sustainability performance through meal choices.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".