S131. NEIGHBOURHOOD-LEVEL SOCIAL CAPITAL, MARGINALIZATION, AND THE INCIDENCE OF PSYCHOTIC DISORDERS IN TORONTO, CANADA: A RETROSPECTIVE POPULATION-BASED COHORT STUDY
Bibliographic record
Abstract
Abstract Background Previous studies have shown mixed results regarding the relationship between social capital and the risk of developing a psychotic disorder, and this has yet to be studied in North America. This study aims to examine the relationship between neighbourhood-level social capital, marginalization, and the incidence of psychotic disorders in Toronto, Canada. Methods A retrospective cohort of people aged 14 to 40 years residing in Toronto, Canada in 1999 (followed to 2008) was constructed from population-based health administrative data. Incident cases of schizophrenia spectrum psychotic disorders were identified using a validated algorithm. Voter participation rates in a municipal election were used as a proxy neighbourhood-level indicator of social capital. Exposure to neighbourhood-level marginalization was obtained from the Ontario Marginalization Index. Poisson regression models adjusting for age and sex were used to calculate incidence rate ratios (IRR) for each social capital quintiles and marginalization quintile. Results In the study cohort (n = 640,000) over the 10-year follow-up period, we identified 4,841 incident cases of schizophrenia spectrum psychotic disorders. We observed elevated rates of psychotic disorders in areas with the highest levels (IRR = 1.13, 95% CI 1.00–1.27) and moderate levels (IRR = 1.23, 95% CI 1.12–1.36) of social capital, when compared to areas with the lowest levels of social capital, after adjusting for neighbourhood-level indicators of marginalization. The risk associated with social capital was not present when analyzed in only the females in the cohort. All neighbourhood marginalization indicators, other than ethnic concentration, were significantly associated with risk. Discussion The risk of developing a psychotic disorder in Toronto, Canada is associated with socioenvironmental exposures. Social capital is associated with risk, however, the impact of social capital on risk differs by sex and social capital quintile. Across the entire cohort, exposure to all neighbourhood-level marginalization indicators, except ethnic concentration, impacts risk. Future research should examine how known individual-level risk factors, including immigration, ethnicity, and family history of a mental disorder may interact with these findings.
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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 | 0.001 |
| 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 | 0.000 |
| 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".