Associations between the traditional and novel neighbourhood built environment metrics and weight status among Canadian men and women
Bibliographic record
Abstract
OBJECTIVES: Neighbourhood characteristics can impact the health of residents. This study investigated associations between objectively derived neighbourhood characteristics, including novel space syntax metrics, and self-reported body mass index (BMI) among Canadian men and women. METHODS: Our study included survey data collected from a random cross-section of adults residing in Calgary, Alberta (n = 1718). The survey, conducted in 2007/2008, captured participants' socio-demographic characteristics, health, and weight status (BMI). Participants' household postal codes were geocoded and 1600-m line-based network buffers estimated. Using a geographical information system, we estimated neighbourhood characteristics within each buffer including business destination density, street intersection density, sidewalk length, and population density. Using space syntax, we estimated street integration and walkability (street integration plus population density) within each buffer. Using adjusted regression models, we estimated associations between neighbourhood characteristics and BMI (continuous) and BMI categories (healthy weight vs. overweight including obese). Gender-stratified analysis was also performed. RESULTS: Business destination density was negatively associated with BMI and the odds of being overweight. Among men, street intersection density and sidewalk length were negatively associated with BMI and street intersection density, business destination density, street integration, and space syntax walkability were negatively associated with odds of being overweight. Among women, business destination density was negatively associated with BMI. CONCLUSION: Urban planning policies that impact neighbourhood design have the potential to influence weight among adults living in urban Canadian settings. Some characteristics may have a differential association with weight among men and women and should be considered in urban planning and in neighbourhood-focussed public health interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".