A meta-analysis of the risk for psychotic disorders among first- and second-generation immigrants
Bibliographic record
Abstract
BACKGROUND: There is increasing acceptance of migration as a risk factor for schizophrenia and related disorders; however, the magnitude of the risk among second-generation immigrants (SGIs) remains unclear. Generational differences in the incidence of psychotic disorders among migrants might improve our understanding of the relationship between migration, ethnicity and psychotic disorders. This meta-analysis aimed at determining the risk of psychotic disorders among SGIs in comparison with non-migrants and first-generation immigrants (FGIs). METHOD: Medline, EMBASE and PsycINFO databases were searched systematically for population-based studies on migration and psychotic disorders published between 1977 and 2008. We also contacted experts, tracked citations and screened bibliographies. All potential publications were screened by two independent reviewers in a threefold process. Studies were included in the meta-analysis if they reported incidence data, differentiated FGIs from SGIs and provided age-adjusted data. Data extraction and quality assessment were conducted for each study. RESULTS: Twenty-one studies met all inclusion criteria. A meta-analysis of 61 effect sizes for FGIs and 28 for SGIs yielded mean-weighted incidence rate ratios (IRRs) of 2.3 [95% confidence interval (CI) 2.0-2.7] for FGIs and 2.1 (95% CI 1.8-2.5) for SGIs. There was no significant risk difference between generations, but there were significant differences according to ethno-racial status and host country. CONCLUSIONS: The increased risk of schizophrenia and related disorders among immigrants clearly persists into the second generation, suggesting that post-migration factors play a more important role than pre-migration factors or migration per se. The observed variability suggests that the risk is mediated by the social context.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.066 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".