Course and outcome of psychosis in black Caribbean populations and other ethnic groups living in the UK: A systematic review
Bibliographic record
Abstract
BACKGROUND: A higher incidence of psychosis has repeatedly been reported in black Caribbean populations in the UK. This has been attributed to a number of biological, psychological and sociocultural causes, including black Caribbean populations having a different illness course and outcome compared to other ethnic populations living in the UK. AIMS: A systematic review of UK-based quantitative studies, which compared at least two aspects of outcome in black Caribbean populations and other ethnic populations living in the UK, was conducted to assess whether the current body of research suggests that there are differences in the course and outcome of psychoses for these populations. METHOD: A wide variety of databases were searched using MeSH terms and keywords. Studies were evaluated according to specified inclusion criteria and analysed using predefined scoring criteria. RESULTS: Searches yielded a heterogeneous collection of studies. Large variances in methodological approaches and the quality of studies were reported. Many studies reported little or no difference between black Caribbean and other ethnic populations living in the UK. CONCLUSIONS: Emphasis is placed on the unreliability of these findings given the methodological limitations of the studies, and the need for higher-quality research in this area is highlighted.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".