T123. RISK FACTORS FOR PSYCHOTIC DISORDERS WITHIN MIGRANT GROUPS IN CANADA
Bibliographic record
Abstract
Abstract Background Although there is a large evidence base suggesting elevated rates of psychotic disorder for some migrant groups, relative to the non-migrant host population, less is known about factors that modify the risk of psychosis within migrant groups. Our objective was to assess whether pre-migration, migration-related, and post-migration factors were associated with an elevated risk of psychotic disorder among first-generation migrants in Ontario, Canada. Methods Using linked population-based health administrative data, we constructed a retrospective cohort of first-generation migrants to Ontario over a 20-year period between 1992 and 2012. We identified first onset non-affective psychotic disorders using a validated algorithm. Pre-migration factors included sex, country of origin, education, occupation, marital status, and language. Migration-related factors included age at migration, year of migration, duration of residence, and migrant class. Post-migration factors included rural place of residence and neighbourhood-level indicators of marginalization. We used Poisson regression models to compute incidence rate ratios for each pre-migration, migration-related, and post-migration factor to assess its magnitude of effect on the risk of developing psychosis, relative to migrants who did not develop psychotic disorder. Results Our cohort included over two million first-generation migrants over the 20-year period. Preliminary findings suggest that males, refugees, and people who set up residence in urban centres have higher rates of psychotic disorder, and people who are older at the time of migration and those who set up residence in high-income areas have lower rates of psychotic disorder. Full results for pre-migration, migration-related, and post-migration risk factors will be presented. Discussion Migrant status is one of few well-established risk factors for psychotic disorder, yet we have limited understanding of the underlying etiology. The findings from this study help to identify high-risk groups to target for intervention. Improving our understanding of key risk factors for psychotic disorders within migrant groups is crucial for informing prevention and early intervention efforts.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".