Violent and non-violent crime against adults with severe mental illness
Bibliographic record
Abstract
BACKGROUND: Little is known about the relative extent of crime against people with severe mental illness (SMI). AIMS: To assess the prevalence and impact of crime among people with SMI compared with the general population. METHOD: A total of 361 psychiatric patients were interviewed using the national crime survey questionnaire, and findings compared with those from 3138 general population controls participating in the contemporaneous national crime survey. RESULTS: Past-year crime was experienced by 40% of patients v. 14% of controls (adjusted odds ratio (OR) = 2.8, 95% CI 2.0-3.8); and violent assaults by 19% of patients v. 3% of controls (adjusted OR = 5.3, 95% CI 3.1-8.8). Women with SMI had four-, ten- and four-fold increases in the odds of experiencing domestic, community and sexual violence, respectively. Victims with SMI were more likely to report psychosocial morbidity following violence than victims from the general population. CONCLUSIONS: People with SMI are at greatly increased risk of crime and associated morbidity. Violence prevention policies should be particularly focused on people with SMI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".