Examining the associations between the food environment and dietary intake in British Columbia: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To characterise neighbourhood food environments in British Columbia (BC) and determine whether food environment characteristics are associated with fruit and vegetable (FV) intake. DESIGN: A cross-sectional study using geospatial linkage of food environment measures within 1 km residential buffers, analysed with mixed-effects models SETTING: Urban neighbourhoods in BC, Canada. PARTICIPANTS: Approximately 25 000 adults aged 35-69 years from the BC Generations Project cohort. OUTCOME MEASURES: FV intake as a continuous variable (servings/day) and as a binary measure (<5 or ≥5 servings/day). RESULTS: Approximately 50% of participants lived in neighbourhoods without chain grocery stores, fast-food outlets or convenience stores within walking distance. Neighbourhoods in the highest density category for fast-food outlets were associated with lower odds of consuming ≥5 servings of FV per day (OR=0.89, 95% CI 0.80 to 0.98). Associations between chain grocery stores, convenience stores and FV intake were attenuated after adjusting for neighbourhood characteristics including walkability, and material and social deprivation. CONCLUSIONS: 5 servings of FV per day. Further studies are needed to better understand the null findings and additional factors that may be associated with 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".