Age at migration and the risk of psychotic disorders: a systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review and meta-analysis of the existing evidence on the association between age at migration and the risk of psychotic disorders. METHODS: Observational studies were eligible for inclusion if they presented data on the association between age at migration and the risk of psychotic disorders among first-generation migrant groups. We used two random effects meta-analyses to pool effect estimates for each stratum of age at migration relative to (i) a native-born reference category and (ii) the youngest age stratum (0 to 2 years). RESULTS: Ten studies met inclusion criteria, and five were included in the meta-analysis. The risk of psychotic disorder among people who migrate prior to age 18 is nearly twice as high as the native-born population, with no evidence of effect modification by age strata. People who migrate during early adulthood (19 to 29 years) have a similar risk of psychotic disorder as the native-born population (IRR = 0.93, 95% CI = 0.60, 1.44) and a lower risk relative to those who migrate during infancy (0 to 2 years) (IRR = 0.58, 95% CI = 0.33, 1.04). CONCLUSIONS: Migrant status is one of few well-established risk factors for psychotic disorder, yet we have limited understanding of the underlying etiology. The findings of this review advance our understanding of this association and identify high-risk groups to target for intervention.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".