Urban Retail Food Environments: Relative Availability and Prominence of Exhibition of Healthy vs. Unhealthy Foods at Supermarkets in Buenos Aires, Argentina
Bibliographic record
Abstract
There is growing evidence that the food environment can influence diets. The present study aimed to assess the relative availability and prominence of healthy foods (HF) versus unhealthy products (UP) in supermarkets in Buenos Aires, Argentina and to explore differences by retail characteristics and neighborhood income level. We conducted store audits in 32 randomly selected food retails. Food availability (presence/absence, ratio of cumulative linear shelf length for HF vs. UP) and prominence inside the store (location visibility) were measured based on the International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS) protocol. On average, for every 1 m of shelf length for UP, there was about 25 cm of shelf length for HF (HF/UP ratio: 0.255, SD 0.130). UP were more frequently available in high-prominence store areas (31/32 retails) than HF (9/32 retails). Shelf length ratio differed across commercial chains (p = 0.0268), but not by store size or type. Retails in the lower-income neighborhoods had a lower HF/UP ratio than those in the higher-income neighborhoods (p = 0.0329). Availability of the selected HF was overcome largely by the UP, particularly in high prominence areas, and in neighborhoods with lower income level, which may pose an opportunity for public health interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".