Compulsory admission at first presentation to services for psychosis: does ethnicity still matter? Findings from two population-based studies of first episode psychosis
Bibliographic record
Abstract
OBJECTIVES: Compared with the majority population, those from minority ethnic groups in the UK are more likely to be admitted compulsorily during a first episode of psychosis (FEP). We investigated whether these disparities in pathways in to care continue. METHODS: We analysed data from two first episode psychosis studies, conducted in the same geographical area in south London 15 years apart: the Aetiology and Ethnicity in Schizophrenia and Other Psychosis (AESOP) and the Clinical Record Interactive Search-First Episode Psychosis (CRIS-FEP) studies. The inclusion/exclusion criteria for case ascertainment for first episode psychosis were identical across the two studies. We performed multivariable logistic regression to estimate odds of compulsory admission by ethnic group, controlling for confounders. PARTICIPANTS: Two hundred sixty-six patients with first episode psychosis, aged 18-64 years, who presented to mental health services in south London in 1997-1999 and 446 with FEP who presented in 2010-2012. RESULTS: When the two samples were compared, ethnic differences in compulsory admission appear to have remained the same for black African patients, i.e. three times higher than white British in both samples: AESOP (adj. OR = 3.96; 95% CI = 1.80-8.71) vs. CRIS-FEP (adj. OR = 3.12; 95% CI = 1.52-6.35). Black Caribbean patients were three times more likely to be compulsorily admitted in AESOP (adj. OR = 3.20; 95% CI = 1.56-6.54). This was lower in the CRIS-FEP sample (adj. OR = 1.68; 95% CI = 0.71-3.98) and did not meet conventional levels for statistical significance. CONCLUSION: Ethnicity is strongly associated with compulsory admissions at first presentation for psychosis with evidence of heterogeneity across groups, which deserves further research.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".