Promoting access to fresh fruits and vegetables through a local market intervention at a subway station
Bibliographic record
Abstract
OBJECTIVE: Alternative food sources (AFS) such as local markets in disadvantaged areas are promising strategies for preventing chronic disease and reducing health inequalities. The present study assessed how sociodemographic characteristics, physical access and fruit and vegetable (F&V) consumption are associated with market use in a newly opened F&V market next to a subway station in a disadvantaged neighbourhood. DESIGN: Two cross-sectional surveys were conducted among adults: (i) on-site, among shoppers who had just bought F&V and (ii) a telephone-based population survey among residents living within 1 km distance from the market. SETTING: One neighbourhood in Montreal (Canada) with previously limited F&V offerings. SUBJECTS: Respectively, 218 shoppers and 335 residents completed the on-site and telephone-based population surveys. RESULTS: Among shoppers, 23 % were low-income, 56 % did not consume enough F&V and 54 % did not have access to a car. Among all participants living 1 km from the market (n 472), market usage was associated (OR; 95 % CI) with adequate F&V consumption (1·86; 1·10, 3·16), living closer to the market (for distance: 0·86; 0·76, 0·97), having the market on the commute route (2·77; 1·61, 4·75) and not having access to a car (2·96; 1·67, 5·26). CONCLUSIONS: When implemented in strategic locations such as transport hubs, AFS like F&V markets offer a promising strategy to improve F&V access among populations that may be constrained in their food acquisition practices, including low-income populations and those relying on public transportation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".