Exploring the social and neighbourhood predictors of diabetes: a comparison between Toronto and Chicago
Bibliographic record
Abstract
OBJECTIVES: This report examined the impact and extent that spatial access to primary care physicians (PCPs) and social neighbourhood-/community-level factors have on diabetes prevalence for Toronto and Chicago. METHODS: The two-step floating catchment area method was used to compute spatial access scores. Bivariate correlation and multivariate linear regression identified the factors that were associated with, and/or predicted, diabetes prevalence. RESULTS: Potential spatial access to PCPs had no strong associations with diabetes prevalence. Low socio-economic status factors and certain ethnic groups were strongly associated with diabetes prevalence for both cities. For Toronto, South American place of birth, households below poverty and high school-level education predicted diabetes prevalence. African ethnicity and households below poverty predicted diabetes prevalence for Chicago. CONCLUSION: Although this report found no strong association between diabetes prevalence and access to PCPs, contextual factors significant in past individual-level diabetes studies were associated with diabetes prevalence at the neighbourhood/community level for Toronto and Chicago.
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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 | 0.002 |
| 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) | 0.002 | 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".