Examining the associations between the food environment and dietary intake in British Columbia: A cross-sectional study
Bibliographic record
Abstract
Abstract This study aimed to characterize neighbourhood food environments in British Columbia (BC) and to determine if food environment characteristics were associated with fruits and vegetables (FV) intake. A cross-sectional study was conducted using geospatial food environment (Can-FED) and neighbourhood environment measures from the BC Generations Project, a cohort of males and females aged 35-69. Food environment measures (chain grocery stores, fast food outlets, convenience stores, and two relative measures of food outlets) were categorized according to density: class 0 (no presence) to class 4 (highest density). Multivariable mixed-effect models adjusted for sociodemographics and neighbourhood characteristics were constructed to assess the associations between food environments and FV intakes as a continuous variable and as <5 or ≥ 5 servings of FV/day. Approximately 50% of participants lived in neighbourhoods without chain grocery stores, fast food outlets or convenience stores within walking distance. An inverse association between neighbourhoods with the highest density of fast-food restaurants and FV consumption was observed (Odds ratio (OR) =0.89, 95% CI: 0.80, 0.98). Associations between chain grocery stores, convenience stores and FV consumption were attenuated with adjustment for neighbourhood characteristics: walkability, material and social deprivation. The findings suggest the presence of food deserts across urban areas in BC. Participants living in neighbourhoods with greater density of fast-food restaurants were less likely to consume ≥5 servings of FV per day. Further studies are needed to understand the lack of associations between other characteristics of the food environment and dietary intake.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".