Evaluating the Use of a Psychiatric Intensive Care Unit: Is Ethnicity a Risk Factor for Admission?
Bibliographic record
Abstract
OBJECTIVE: This paper presents a descriptive study, undertaken in 1993, of a Psychiatric Intensive Care Unit (PICU) serving a deprived inner-city area, investigating the role of ethnicity as a risk factor for admission to the unit. METHODS: Clinical and demographic data were collected on consecutive admissions to a PICU. Global Assessment of Function Scale scores were rated on admission and at discharge from the unit. RESULTS: The majority of patients were male (63%) and the commonest DSM-IV diagnoses were schizophrenia (42%) and bipolar affective disorder (24%). Average length of stay was 13 days with patients making significant improvement in functioning during their stay. Fifty-five percent of PICU admissions came from ethnic minorities (compared with 25.6% of total hospital admissions and 20.9% of the local catchment area population aged between 16 and 65 years). There was no evidence that ethnic minority patients were being inappropriately admitted to the PICU. CONCLUSIONS: It is likely that a variety of factors contributed to the high rate of PICU admission amongst ethnic minority patients, including an increased prevalence of major mental illness and more frequent cannabis abuse.
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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 | 0.007 |
| 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) | 0.002 | 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".