Development, reliability and use of a food environment assessment tool in supermarkets of four neighbourhoodsin Montréal, Canada
Bibliographic record
Abstract
INTRODUCTION: The food environment is a promising arena in which to influence people's dietary habits. This study aimed to develop a comprehensive food environment assessment tool for businesses and characterize the food environment of a low-tomedium income area of Montréal, Canada. METHODS: We developed a tool, Mesure de l'environnement alimentaire du consommateur dans les supermarchés (MEAC-S), and tested it for reliability. We used the MEAC-S to assess the consumer food environment of 17 supermarkets in four neighbourhoods of Montréal. We measured the shelf length, variety, price, display counts and in-store positions of fruits and vegetables (FV) and ultra-processed food products (UPFPs). We also assessed fresh FV for quality. Store size was estimated using the total measured shelf length for all food categories. We conducted Spearman correlations between these indicators of the food environment. RESULTS: Reliability analyses revealed satisfactory results for most indicators. Characterization of the food environment revealed high variability in shelf length, variety and price of FV between supermarkets and suggested a disproportionate promotion of UPFPs. Display counts of UPFPs outside their normal display location ranged from 7 to 26, and they occupied 8 to 33 strategic in-store positions, whereas the number of display counts of fresh FV outside their normal display location exceeded 1 in only 2 of the 17 stores surveyed, and they occupied a maximum of 2 strategic in-store positions per supermarket. Price of UPFPs was inversely associated with their prominence (p < .005) and promotion (p < .003). Store size was associated with display counts and strategic in-store positioning of UPFPs (p < .001), but not FV, and was inversely associated with the price of soft drinks (p < .003). CONCLUSION: This study illustrates the variability of the food environment between supermarkets and underscores the importance of measuring in-store characteristics to adequately picture the consumer food environment.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".