Associations between the neighbourhood characteristics and body mass index, waist circumference, and waist-to-hip ratio: Findings from Alberta's Tomorrow Project
Bibliographic record
Abstract
This study estimated the associations between neighbourhood characteristics and self-reported body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) risk categories among Canadian men and women. Using data from the Alberta's Tomorrow Project (n = 14,550), we estimated 3- and 4-way intersections, business destinations, population count, and normalized difference vegetation index (NDVI) within a 400 m radius of participant's home. Intersections, business destinations, and population count (z-scores) were summed to create a walkability score. Four-way intersections and walkability were negatively associated with overweight and obesity. Walkability was negatively associated with obesity. NDVI was negatively associated with high-risk WHR and population count and walkability positively associated with high-risk WHR. Among men, population count and walkability were negatively associated with obesity, and business destinations and walkability were negatively associated with overweight and obesity. Among women, NDVI was negatively associated with overweight (including obesity), obesity, and high-risk WC. Interventions promoting healthy weight could incorporate strategies that take into consideration local built environment characteristics.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".