Homicide of Strangers by People with a Psychotic Illness
Bibliographic record
Abstract
BACKGROUND: The homicide of strangers by people with psychosis, referred to here as "stranger homicides," are rare and tragic events that generate adverse publicity for mental health services and have resulted in significant changes in mental health policy and law. AIM: To estimate the incidence of stranger homicides, using data from previously published studies, and to compare the characteristics of psychotic offenders who killed strangers with the characteristics of those who killed a close relative. METHOD: Meta-analysis of the population-based studies of homicide by persons suffering from a psychosis in which the number of subjects who killed strangers was also reported. Characteristics of stranger homicide and family homicide offenders were examined in a multicenter case-control study of homicide during psychotic illness in four high-income countries. RESULTS: A pooled estimate of 1 stranger homicide per 14.3 million people per year (95% confidence interval, 1 in 18.9 million to 1 in 11.5 million people per year) was calculated by meta-analysis of 7 studies. The characteristics of the 42 stranger homicide offenders from New South Wales [NSW], Quebec and Eastern Ontario, Finland, and the Netherlands were identified. Twenty seven (64%) of these had never previously received treatment with antipsychotic medication. The stranger homicide offenders were more likely to be homeless, have exhibited antisocial conduct, and had fewer negative symptoms than those who killed family members. The victims of stranger homicide were mostly adult males and the homicides rarely occurred in the victim's home or workplace. CONCLUSIONS: Stranger homicide in psychosis is extremely rare and is even rarer for a patient who has received treatment with antipsychotic medication. A lack of distinguishing characteristics of stranger homicide offenders and an extremely low base rate of stranger-homicide suggests that risk assessment of patients known to have a psychotic illness will be of little assistance in the prevention of stranger homicides.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".