Monitoring the availability of healthy and unhealthy foods and non‐alcoholic beverages in community and consumer retail food environments globally
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".