New Clinically Relevant Findings about Violence by People with Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To review findings with clinical relevance that add to knowledge about antisocial and aggressive behaviour among persons with schizophrenia. METHOD: Nonsystematic literature review. RESULTS: Recent evidence shows that individuals who develop schizophrenia present cognitive deficits, psychotic-like experiences, and internalizing and externalizing problems from childhood onwards. Many of their relatives present not only schizophrenia-related disorders but also antisocial behaviour. While the increased risk of aggressive behaviour among persons with schizophrenia has been robustly established, recent findings show that by first contact with clinical services for psychosis, most people with schizophrenia who will engage in aggressive behaviour may be identified. At first episode, 2 distinct types are distinguishable: those who present a history of antisocial and aggressive behaviour since childhood and those who began engaging in aggressive behaviour as illness onsets. Antipsychotic medications and other treatments shown to be effective for schizophrenia are needed by both types of patients. Additionally, those with a history of antisocial and aggressive behaviour since childhood require cognitive-behavioural programs aimed at reducing these behaviours and promoting prosocial behaviour. Reducing physical victimisation and cannabis use will likely reduce aggressive behaviour. Evidence suggests that threats to hurt others often precede assaults. CONCLUSIONS: At first contact with services, patients with schizophrenia who have engaged in aggressive behaviour should be identified and treated for schizophrenia and for aggression. Research is needed to identify interactions between genotypes and environmental factors, from conception onwards, that promote and that protect against the development of aggressive behaviour among persons with schizophrenia.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".