Homicide in the context of psychosis: analysis of prior service utilisation and age at onset of illness and violence
Bibliographic record
Abstract
BACKGROUND: Public stigma and fear are heightened in cases of extreme violence perpetrated by persons with serious mental illness (SMI). Prevention efforts require understanding of illness patterns and treatment needs prior to these events unfolding. AIMS: To examine mental health service utilisation by persons who committed homicide and entered into forensic care, to investigate the adequacy of mental healthcare preceding these offences. METHOD: = 112). Sociodemographic, clinical and offence-related variables were coded from the health record and reports prepared for the forensic tribunal. RESULTS: Most patients (75.7%) had mental health contacts preceding the homicide, with 28.4% having a psychiatric in-patient admission in the year prior. For those with service contacts in the year preceding, 50.9% had had only sporadic contact and 70.7% were non-adherent with prescribed medications. Victims were commonly known to the individual (35.7%) and were often family members in care-providing roles (55.4%). Examination of age at onset of illness and offending patterns suggested that most persons admitted to forensic care for homicide act in the context of illness and exhibit a low frequency of pre-homicide offending. CONCLUSIONS: Many individuals admitted to forensic care for homicide have had inadequate mental healthcare leading up to this point. Effective responses to reduce and manage risk should encompass services that proactively address illness-related (e.g. earlier access and better maintenance in care) and criminogenic (e.g. substance use treatment, employment and psychosocial supports) domains.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".