Psychosis and offending in British Columbia: characteristics of a secure hospital population
Bibliographic record
Abstract
INTRODUCTION: There is an increased likelihood of violence in the mentally ill although the risk is small. AIMS: The study aimed to ascertain the features in a secure hospital population that linked offending and mental illness. METHOD: A survey of patients in the high security hospital serving the province of British Columbia in Canada was carried out. Information on 175 mentally disordered offenders was extracted and included demographic data and specific characteristics of their offences, diagnoses and psychotic symptoms. RESULTS: The most prevalent offences were crimes of violence, but 39% of patients were not primarily violent offenders. Almost two-thirds (61%) had two or more diagnoses. A large majority of the patients were psychotic, schizophrenia being the most common diagnosis. There was a highly significant association between psychosis and violence, but the strength of the association was not increased by the presence of imperative hallucinations or delusions. The sample comprised various ethnic groups, one of which, Native Americans, was over-represented. However, no association was found between violent offending and ethnicity, or age or years of illness. DISCUSSION: The study replicates previous findings of the link between violent offending and psychosis, but not a specific link between violent offending and psychotic drive. A surprising finding was a lack of association between violent offences and substance misuse.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".