Immigration, Social Environment and Onset of Psychotic Disorders
Bibliographic record
Abstract
The recent decade has been characterized by a resurging interest for socio-environmental determinants of psychotic disorders, largely as a result of findings from studies of migration and psychotic disorders. This contribution reviews recent meta-analytic findings which confirm higher incidence rates of schizophrenia and related disorders among first- and second-generation immigrants than in non-immigrant populations, as well as substantial risk variation according to both ethnic minority groups and host society contexts. The relevance of social contexts in the onset of psychosis is also suggested by incidence variation according to the neighbourhood level ethnic density. While limited, an emerging literature suggests potential variations in psychotic-like experiences and at-risk mental states according to ethnic minority status. We then discuss the meaning of findings from migrant studies, as well as integrative models that attempt to account for ethnic variations in the incidence of psychosis and psychotic-like phenomena. In conclusion, there remain numerous gaps in our understanding of the relation between migration, ethnicity, social contexts and the onset of psychosis and we propose future research avenues to address these. In particular, there is a need for multilevel approaches integrating disciplines and methodologies across the psychosis continuum.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".