Comparing food environment and food purchase in areas with low and high prevalence of obesity: data from a mapping, in-store audit, and population-based survey
Bibliographic record
Abstract
Our study aimed to compare key aspects of the food environment in two low-income areas in the city of Campinas, São Paulo State, Brazil: one with low and the other with high prevalence of obesity. We compared the availability of retail food establishments, the types of food sold, and the residents' eating habits. Demographic and socioeconomic data and eating habits were obtained from a population-based health survey. We also analyzed local food environment data collected from remote mapping of the retail food establishments and audit of the foods sold. For comparison purposes, the areas were selected according to obesity prevalence (body mass index - BMI ≥ 30kg/m²), defined as low prevalence (< 25%) and high prevalence (> 45%). Only 18 out of the 150 points of sale for food products sold fruits and vegetables across the areas. Areas with high obesity prevalence had more grocery stores and shops specialized in fruits and vegetables, as well as more supermarkets that sold fruits and vegetables. With less schooling, residents in the areas with high obesity prevalence reported purchasing food more often in supermarket chains and specialized shops with fruits and vegetables, although they consumed more sodas when compared with residents of areas with low obesity prevalence. Our results suggest interventions in low-income areas should consider the diverse environmental contexts and the interaction between schooling and food purchase behaviors in settings less prone to healthy eating.
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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) | 0.000 | 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 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".