Body Mass Index in Urban Canada: Neighborhood and Metropolitan Area Effects
Bibliographic record
Abstract
OBJECTIVES: We investigated the influence of neighborhood and metropolitan area characteristics on body mass index (BMI) in urban Canada in 2001. METHODS: We conducted a multilevel analysis with data collected from a cross-sectional survey of men and women nested in neighborhoods and metropolitan areas in urban Canada during 2001. RESULTS: After we controlled for individual sociodemographic characteristics and behaviors, the average BMIs of residents of neighborhoods in which a large proportion of individuals had less than a high school education were higher than those BMIs of residents in neighborhoods with small proportions of such individuals (P< .01). Living in a neighborhood with a high proportion of recent immigrants was associated with lower BMI for men (P<.01), but not for women. Neighborhood dwelling density was not associated with BMI for either gender. Metropolitan sprawl was associated with higher BMI for men (P=.02), but the effect was not significant for women (P= .09). CONCLUSIONS: BMI is strongly patterned by an individual's social position in urban Canada. A neighborhood's social condition has an incremental influence on the average BMI of its residents. However, BMI is not influenced by dwelling density. Metropolitan sprawl is associated with higher BMI for Canadian men, which supports recent evidence of this same association among American men. Individuals and their environments collectively influence BMI in urban Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".