EPA-1403 – Testing alternative hypotheses for the increased rate of psychotic disorder in immigrants
Bibliographic record
Abstract
Increased incidence rates of psychotic disorder have been consistently observed in immigrants with significant variability across ethnic groups. These findings have been attributed to cross-cultural biases, i.e. misinterpretation of culturally appropriate ideas leading to an overestimation of these disorders among ethnic minorities. Another alternative explanation constitutes the selection hypothesis positing that the increased rate is due to selective migration of predisposed people. We will present two studies that aimed at testing these hypotheses by examining (a) whether risk factors for psychosis are more prevalent among future emigrants, and (b) whether psychotic symptoms differ in severity and nature according to ethnic group. The first study was conducted among a cohort of 50 087 conscripts who were assessed at age 18 on cannabis use, IQ, psychiatric diagnosis, social adjustment, history of trauma and urbanicity of place of upbringing. Through data linkage we examined whether these exposures predicted emigration out of Sweden. The second study included 301 first episode psychosis (FEP) patients within a defined catchment area in Montreal, Canada. Patients were administered scales for the assessment of positive and negative symptoms, as well as general psychopathology. Symptom scores of the reference group were compared to those of patients with different ethnic backgrounds. The results of these studies will be discussed in light of candidate explanations for the increased risk of psychotic disorder among immigrants. Future avenues using data from the EUropean Gene-Environment Interaction (EU-GEI) project will be proposed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".