38. PSYCHOSIS AMONG MIGRANTS AND ETHNIC MINORITY GROUPS IN AUSTRALIA, EUROPE, AND NORTH AMERICA: DISPARITIES ACROSS THE SPECTRUM FROM ONSET TO OUTCOMES
Bibliographic record
Abstract
Disparities exist across the spectrum from onset to outcomes for some migrant and ethnic minority groups with first-episode psychosis. African and Caribbean groups, in particular, tend to have higher rates of psychotic disorder, experience more negative and coercive pathways to care, and may have poorer outcomes from treatment, including early intervention services. However, the majority of this research has been done in a European context, with less information on these disparities from other countries. This symposium will present data from Australia, Europe, and North America looking at the risk, access to care, and outcomes of first-episode psychosis among migrant and ethnic minority groups. Dr. Brian O’Donoghue will present data from Australia showing an increased risk of psychotic disorder among particular migrant groups, and also whether services are successfully detecting migrants at ultra-high risk for psychosis. Dr. Els van der Ven will then present data showing significant variation in rates of psychosis among migrants across five European countries, and the important role of contextual factors. Dr. Kelly Anderson will present data from Canada showing higher rates of hospitalization and involuntary admission among migrant and ethnic minority groups with first-episode psychosis, and that differences in clinical presentation and how it is perceived at the first hospitalization may be driving these trends. Finally, Dr. Oladunni Oluwoye will present data from the RAISE study in the United States demonstrating racial and ethnic disparities in family involvement in specialty care programs, and how that impacts on disparities in outcomes. Finally, Sir Robin Murray will discuss these findings and their implications for population health and service delivery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".