Monitoring the availability of healthy and unhealthy foods and non‐alcoholic beverages in community and consumer retail food environments globally
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Retail food environments are increasingly considered influential in determining dietary behaviours and health outcomes. We reviewed the available evidence on associations between community (type, availability and accessibility of food outlets) and consumer (product availability, prices, promotions and nutritional quality within stores) food environments and dietary outcomes in order to develop an evidence-based framework for monitoring the availability of healthy and unhealthy foods and non-alcoholic beverages in retail food environments. Current evidence is suggestive of an association between community and consumer food environments and dietary outcomes; however, substantial heterogeneity in study designs, methods and measurement tools makes it difficult to draw firm conclusions. The use of standardized tools to monitor local food environments within and across countries may help to validate this relationship. We propose a step-wise framework to monitor and benchmark community and consumer retail food environments that can be used to assess density of healthy and unhealthy food outlets; measure proximity of healthy and unhealthy food outlets to homes/schools; evaluate availability of healthy and unhealthy foods in-store; compare food environments over time and between regions and countries; evaluate compliance with local policies, guidelines or voluntary codes of practice; and determine the impact of changes to retail food environments on health outcomes, such as obesity.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it