Risk of Diagnosed Adolescent-Onset Non-Affective Psychotic Disorder by Migration Background in British Columbia: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: We recently found that the risk of diagnosed non-affective psychotic disorder between the ages of 13 and 19 was lower for immigrant adolescents compared to those without a personal or parental migration history in British Columbia (BC), Canada. In the current study, we further examined the risk for migrants compared to non-migrants by region of origin and immigrant generation (first vs. second), adjusting for several demographic factors and migration class. METHODS: = 193,400). Cases were identified by either one hospitalization or two outpatient physician visits with a primary diagnosis of a non-affective psychotic disorder. Poisson regression was used to estimate incidence rate ratios (IRR) of a diagnosed non-affective psychotic disorder by region of origin among first- and second-generation migrants compared to non-migrants, adjusting for sex, birth year, neighbourhood income and low family income. RESULTS: Risk of diagnosed non-affective psychotic disorder was lower among first-generation migrants from East Asia (IRR = 0.34[95% CI: 0.25-0.46]), South-Asia (IRR = 0.47[95% CI: 0.25-0.89]) and South-East Asia (IRR = 0.55[95% CI: 0.32-0.93]) and second-generation migrants from East Asia (IRR = 0.49[95% CI: 0.35-0.69]) and South Asia (IRR = 0.52[95% CI: 0.37-0.73]), compared to non-migrants. Adjusting for migration class attenuated but did not fully explain variation in risk by region among first-generation migrants. No groups exhibited a significantly elevated risk of the diagnosed non-affective psychotic disorder compared to non-migrants. CONCLUSION: Findings from this study underline the complexity of the association between migration and psychotic disorders. Future research should investigate why certain groups of migrants are less likely to be diagnosed and whether there are specific sub-groups that face an elevated risk.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".