Food Literacy while Shopping: Motivating Informed Food Purchasing Behaviour with a Situated Gameful App
Bibliographic record
Abstract
Establishing healthy eating patterns early in life is critical and has implications for lifelong health. Situated interventions are a promising approach to improve eating patterns. However, HCI research has emphasized calorie control and weight loss, potentially leading consumers to prioritize caloric intake over healthy eating patterns. To support healthy eating more holistically, we designed a gameful app called Pirate Bri's Grocery Adventure (PBGA) that seeks to improve food literacy—meaning the interconnected combination of food-related knowledge, skills, and behaviours that empower an individual to make informed food choices— through a situated approach to grocery shopping. Findings from our three-week field study revealed that PBGA was effective for improving players' nutrition knowledge and motivation for healthier food choices and reducing their impulse purchases. Our findings highlight that nutrition apps should promote planning and shopping based on balance, variety, and moderation.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".