Prison health‐care wings: Psychiatry's forgotten frontier?
Bibliographic record
Abstract
BACKGROUND: There is worldwide evidence of high rates of mental disorder among prisoners, with significant co-morbidity. In England and Wales, mental health services have been introduced from the National Health Service to meet the need, but prison health-care wings have hardly been evaluated. AIMS/HYPOTHESES: To conduct a service evaluation of the health-care wing of a busy London remand (pre-trial) prison and examine the prevalence and range of mental health problems, including previously unrecognised psychosis. METHODS: Service-use data were collected from prison medical records over a 20-week period in 2006-2007, and basic descriptive statistics were generated. RESULTS: Eighty-eight prisoners were admitted (4.4 per week). Most suffered from psychosis, a third of whom were not previously known to services. Eleven men were so ill that they required emergency compulsory treatment in the prison under Common Law before hospital transfer could take place. Over a quarter of the men required hospital transfer. Problem behaviours while on the prison health-care wing were common. CONCLUSIONS AND IMPLICATIONS: Prison health-care wings operate front-line mental illness triaging and recognition functions and also provide care for complex individuals who display behavioural disturbance. Services are not equivalent to those in hospitals, nor the community, but instead reflect the needs of the prison in which they are situated. There is a recognised failure to divert at earlier points in the criminal justice pathway, which may be a consequence of national failure to fund services properly. Hospital treatment is often delayed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".