Building obesity in Canada: understanding the individual- and neighbourhood-level determinants using a multi-level approach
Bibliographic record
Abstract
The objective of this paper was to identify heterogeneities associated with the relationships between the body mass index (BMI) and individual as well as socio-environmental correlates at the individual- and area-levels. The data sources used were: (i) the 2003 Canadian Community Health Survey; (ii) the 2001 Canadian Census; and (iii) the Enhanced Points of Interest (EPOI) database from the Desktop Mapping Technologies Inc. Participants were adults (≥ 20 years; n = 12,836; based on a survey weight scheme N(weighted) = 5,418,218) from Toronto and Vancouver census metropolitan areas with no missing BMI records. In addition to conventional 1 km-buffers, we constructed activity-space-buffers to better assess the walkability and potentially increased BMI of individuals. Multi-level analysis was then applied to estimate the relative effects of both individual- and area-level risk-factors for increased BMI. The findings demonstrate a negative association between BMI and energy expenditure, mixed land uses, residential density and average value of dwellings, while a positive association was found with low educational attainment. Relationships were independent of individual characteristics such as age and ethnicity. Although the majority of the variation in these outcomes was found to be due to individual-level differences, this study did show significant differences at the area-level as well. The activity-space-buffers presented a vast improvement compared to the conventional 1 km-buffers. The results presented support the rationale that targeting high-risk individuals will only address a portion of the increasing BMI problem; it is essential to also address the characteristics of places that compel individuals to make unhealthy choices.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".