Associations of Supermarket Characteristics with Weight Status and Body Fat: A Multilevel Analysis of Individuals within Supermarkets (RECORD Study)
Bibliographic record
Abstract
PURPOSE: Previous research on the influence of the food environment on weight status has often used impersonal measures of the food environment defined for residential neighborhoods, which ignore whether people actually use the food outlets near their residence. To assess whether supermarkets are relevant contexts for interventions, the present study explored between-residential neighborhood and between-supermarket variations in body mass index (BMI) and waist circumference (WC), and investigated associations between brands and characteristics of supermarkets and BMI or WC, after adjustment for individual and residential neighborhood characteristics. METHODS: Participants in the RECORD Cohort Study (Paris Region, France, 2007-2008) were surveyed on the supermarket (brand and exact location) where they conducted their food shopping. Overall, 7 131 participants shopped in 1 097 different supermarkets. Cross-classified multilevel linear models were estimated for BMI and WC. RESULTS: Just 11.4% of participants shopped for food primarily within their residential neighborhood. After accounting for participants' residential neighborhood, people shopping in the same supermarket had a more comparable BMI and WC than participants shopping in different supermarkets. After adjustment for individual and residential neighborhood characteristics, participants shopping in specific supermarket brands, in hard discount supermarkets (especially if they had a low education), and in supermarkets whose catchment area comprised low educated residents had a higher BMI/WC. CONCLUSION: A public health strategy to reduce excess weight may be to intervene on specific supermarkets to change food purchasing behavior, as supermarkets are where dietary preferences are materialized into definite purchased foods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".