The influence of immigrant status and concentration on psychiatric disorder in Canada: a multi-level analysis
Bibliographic record
Abstract
BACKGROUND: Many studies have reported an increased incidence of psychiatric disorder (particularly psychotic disorders) among first generation adult immigrants, along with an increasing risk for ethnic minorities living in low-minority concentration neighborhoods. These studies have depended mostly on European case-based databases. In contrast, North American studies have suggested a lower risk for psychiatric disorder in immigrants, although the effect of neighborhood immigrant concentration has not been studied extensively. METHOD: Using multi-level modeling to disaggregate individual from area-level influences, this study examines the influence of first generation immigrant status at the individual level, immigrant concentration at the neighborhood-level and their combined effect on 12-month prevalence of mood, anxiety and substance-dependence disorders and lifetime prevalence of psychotic disorder, among Canadians. RESULTS: Individual-level data came from the Canadian Community Health Survey (CCHS) 1.2, a cross-sectional study of psychiatric disorder among Canadians over the age of 15 years; the sample for analysis was n=35,708. The CCHS data were linked with neighborhood-level data from the Canadian Census 2001 for multi-level logistic regression. Immigrant status was associated with a lower prevalence of psychiatric disorder, with an added protective effect for immigrants living in neighborhoods with higher immigrant concentrations. Immigrant concentration was not associated with elevated prevalence of psychiatric disorder among non-immigrants. CONCLUSIONS: The finding of lower 12-month prevalence of psychiatric disorder in Canadian immigrants, with further lessening as the neighborhood immigrant concentration increases, reflects a model of person-environment fit, highlighting the importance of studying individual risk factors within environmental contexts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".