Digital Food Retail: Public Health Opportunities
Bibliographic record
Abstract
For over two decades, digital food retail services have been emerging alongside advances in mobile technology and improved access to wi-fi. Digitalization has driven changes within the food environment, complicating an already complex system that influences food-related behaviors and eating practices. Digital food retail services support an infrastructure that enhances commercial food systems by extending access to and availability of highly processed foods, further escalating poor dietary intakes. However, digital food retail services are heterogeneous-food delivery apps, online groceries, and meal kits-and can be feasibly adapted to nutrition interventions and personalized to individual needs. Although sparse, new evidence indicates great potential for digital food retail services to address food insecurity in urban areas and to support healthy eating by making it easier to select, plan, and prepare meals. Digital food retail services are a product of the digital transformation that reflect consumers' constant need for convenience, which must be addressed in future research and interventions. This paper will discuss public health opportunities that are emerging from the global uptake of digital food retail services, with a focus on online groceries, food delivery apps, and meal kits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".