Retrospective population-based study of 132 000 Canadians on the relationship between community belonging and diabetes incidence
Bibliographic record
Abstract
Introduction: Community belonging is a dimension of subjective well-being that is of growing public health interest for mitigating chronic disease. However, there is limited longitudinal evidence that such a relationship exists. We assessed the effect of community belonging on the subsequent 5-year risk of diabetes. Research design and methods: This population-based cohort study consisted of 132 295 Ontario respondents of the Canadian Community Health Survey (2000-2014) aged 30-75 years and diabetes-free. Individuals were linked to a validated diabetes registry and followed for 5 years or until 31 March 2020. Survey respondents rated their sense of belonging to community on a 4-point scale (very strong, somewhat strong, somewhat weak or very weak belonging). We estimated the effect of community belonging on 5-year diabetes incidence using Cox proportional hazards models. Inverse probability of treatment weighting (IPTW) was used to account for sociodemographic and behavioural confounders. Analyses were also stratified, weighted and modelled by key individual-level characteristics. Results: At survey response, 9.5% of the sample reported having very weak sense of belonging to their communities. By the end of study follow-up, 5.3% of the sample developed diabetes. IPT weighting achieved balance between community belonging groups across the sample and subgroups. Respondents with a very weak sense of belonging exhibited higher risk of diabetes (HR 1.26, 95% CI 1.09 to 1.46, compared with those with somewhat weak, somewhat strong and very strong sense of belonging), even after accounting for individual-level factors via IPTW (HR 1.16, 95% CI 1.01, 1.34). Those with very weak belonging exhibited elevated risk of diabetes across most subgroups, and this association remained conclusive after IPT weighting for Canadian-born and white participants. Conclusions: The association between weak sense of belonging to community and diabetes risk demonstrated by this study highlights the critical role that social connections play in chronic disease epidemiology. Our findings signal a need to include social and community factors in population health strategies for chronic disease prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".