Unwalkable Neighborhoods, Poverty, and the Risk of Diabetes Among Recent Immigrants to Canada Compared With Long-Term Residents
Bibliographic record
Abstract
OBJECTIVE: This study was designed to examine whether residents living in neighborhoods that are less conducive to walking or other physical activities are more likely to develop diabetes and, if so, whether recent immigrants are particularly susceptible to such effects. RESEARCH DESIGN AND METHODS: We conducted a population-based, retrospective cohort study to assess the impact of neighborhood walkability on diabetes incidence among recent immigrants (n = 214,882) relative to long-term residents (n = 1,024,380). Adults aged 30-64 years who were free of diabetes and living in Toronto, Canada, on 31 March 2005 were identified from administrative health databases and followed until 31 March 2010 for the development of diabetes, using a validated algorithm. Neighborhood characteristics, including walkability and income, were derived from the Canadian Census and other sources. RESULTS: Neighborhood walkability was a strong predictor of diabetes incidence independent of age and area income, particularly among recent immigrants (lowest [quintile 1 {Q1}] vs. highest [quintile 5 {Q5}] walkability quintile: relative risk [RR] 1.58 [95% CI 1.42-1.75] for men; 1.67 [1.48-1.88] for women) compared with long-term residents (Q1 to Q5) 1.32 [1.26-1.38] for men; 1.24 [1.18-1.31] for women). Coexisting poverty accentuated these effects; diabetes incidence varied threefold between recent immigrants living in low-income/low walkability areas (16.2 per 1,000) and those living in high-income/high walkability areas (5.1 per 1,000). CONCLUSIONS: Neighborhood walkability was inversely associated with the development of diabetes in our setting, particularly among recent immigrants living in low-income areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".