Ethnic minority position and migrant status as risk factors for psychotic symptoms in the general population: a meta-analysis
Bibliographic record
Abstract
Psychotic symptoms (PS) are experienced by a substantial proportion of the general population. When not reaching a threshold of clinical relevance, these symptoms are defined as psychotic experiences (PEs) and may exist on a continuum with psychotic disorders. Unfavorable socio-environmental conditions, such as ethnic minority position (EMP) and migrant status (MS), may increase the risk of developing PS and PEs. We conducted an electronic systematic review and a meta-analysis assessing the role of EMP and MS for the development and persistence of PS in the general population. Sub-group analyses were performed investigating the influence of ethnic groups, host countries, age, types of PS, and scales. Twenty-four studies met our inclusion criteria. EMP was a relevant risk factor for reporting PS [odds ratio (OR) 1.44, 95% confidence interval (CI) 1.22-1.70) and PEs (OR 1.36, 95% CI 1.16-1.60). The greatest risk was observed in people from the Maghreb and the Middle East ethnic groups in Europe (OR 3.30, 95% CI 2.09-5.21), in Hispanic in the USA (OR 1.98, 95% CI 1.43-2.73), and in the Black populations (OR 1.85, 95% CI 1.39-2.47). We found a significant association between MS and delusional symptoms (OR 1.47, 95% CI 1.33-1.62). We found no association between EMP and persistence of PEs.EMP was associated with increased risk of reporting PS and PEs, and the risk was higher in ethnic groups facing deprivation and discrimination. We found an association between MS and delusional symptoms. These results raise questions about the precise role of socio-environmental factors along 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.039 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".