Ethnic inequalities in the use of secondary and tertiary mental health services among patients with obsessive–compulsive disorder
Bibliographic record
Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) has similar prevalence rates across ethnic groups. However, ethnic minorities are underrepresented in clinical trials of OCD. It is unclear whether this is also the case in clinical services. AIMS: To explore whether ethnic minorities with OCD are underrepresented in secondary and tertiary mental health services in the South London and Maudsley (SLaM) NHS Foundation Trust. METHOD: The ethnic distribution of patients with OCD seen between 1999 and 2013 in SLaM (n = 1528) was compared with that of the general population in the catchment area using census data. A cohort of patients with depression (n = 22 716) was used for comparative purposes. RESULTS: Ethnic minorities with OCD were severely underrepresented across services (-57%, 95% CI -62% to -52%). The magnitude of the observed inequalities was significantly more pronounced than in depression (-29%, 95% CI -31% to -27%). CONCLUSIONS: There is a clear need to understand the reasons behind such ethnic inequalities and implement measures to reduce them.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".