Symptom profiles and explanatory models of first‐episode psychosis in African‐, Caribbean‐ and European‐origin groups in Ontario
Bibliographic record
Abstract
AIM: To assess variability in symptom presentation and explanatory models of psychosis for people from different ethnic groups. METHODS: Clients with first-episode psychosis (n = 171) who identified as black African, black Caribbean or white European were recruited from early intervention programmes in Toronto and Hamilton. We compared results by ethnic group for symptom profiles and explanatory models of illness. RESULTS: Clients of black Caribbean origin had a lower odds of reporting that they were speaking incomprehensibly (OR = 0.36; 95% CI: 0.14-0.90) and black African clients had a greater odds of reporting persistent aches or pains (OR = 2.92; 95% CI: 1.32-6.50). Black African clients had a lower odds of attributing the cause of psychosis to hereditary factors (OR = 0.41; 95% CI: 0.19-0.89) or to substance abuse (OR = 0.29; 95% CI: 0.13-0.67) and had a lower odds of assigning responsibility for their illness to themselves (OR = 0.41; 95% CI: 0.19-0.89). CONCLUSIONS: Understanding the differences in illness models for ethnic minority groups may help improve the cultural competence of mental health services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".