Perceived ethnic discrimination as a risk factor for psychotic symptoms: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown an elevated risk of psychotic symptoms (PS) and experiences (PEs) among ethnic minority groups, with significant variation between groups. This pattern may be partially attributable to the unfavorable socio-environmental conditions that surround ethnic minority groups. Perceived ethnic discrimination (PED) in particular has been a salient putative risk factor to explain the increased risk. METHODS: We conducted a systematic literature review and meta-analysis to assess the impact of PED on reporting PS/PEs in ethnic minorities. This review abides by the guidelines set forth by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The included studies were obtained from the databases: Medline, PsycINFO, and Web Of Science. Sub-group analyses were performed assessing the effect of PED in different subtypes of PS, the influence of ethnicity and moderating/mediating factors. RESULTS: Seventeen studies met the inclusion criteria, and nine were used to conduct the meta-analysis. We found a positive association between PED and the occurrence of PS/PEs among ethnic minorities. The combined odds ratio were 1.77 (95% CI 1.26-2.49) for PS and 1.94 (95% CI 1.42-2.67) for PEs. We found that the association was similar across ethnic groups and did not depend on the ethnic origin of individuals. Weak evidence supported the buffering effects of ethnic identity, collective self-esteem and social support; and no evidence supported the moderating effect of ethnic density. Sensitivity to race-based rejection significantly but only slightly mediated the association. CONCLUSION: These findings suggest that PED is involved in the increased risk of PS/PEs in ethnic minority populations.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".