Bibliographic record
Abstract
OBJECTIVE: Although a minority of persons with schizophrenia (SCZ) commits violent acts, SCZ remains a risk factor for violence. Here, we present a broad overview of evidence-based treatments for violence in SCZ, including biological and psychosocial interventions. METHOD: We conducted MEDLINE and PsychINFO literature searches to retrieve articles relating to treatments for violent, hostile, or aggressive behaviours in SCZ. RESULTS: Clozapine shows the strongest evidence for treating the acute violence of SCZ. Other atypical antipsychotics also possess antiaggressive effects, although the evidence is not as robust as that for clozapine. Psychosocial treatments can be useful adjuncts to pharmacotherapy once patients' positive symptoms have stabilized. Cognitive behavioural therapy for psychosis and cognitive remediation are 2 psychosocial interventions that have demonstrated positive outcomes for violence in SCZ. Most psychosocial studies that examined violence as an outcome were conducted in forensic psychiatric settings. CONCLUSIONS: Effective treatments exist for persons with SCZ who pose a risk for violent and aggressive behaviour, although the overall evidence base remains relatively weak. More randomized controlled trials of programs showing evidence for reduction of violence in SCZ are required. Further research should delineate which patients could benefit from multimodal treatment and where and when such treatments are optimally delivered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 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) | 0.007 | 0.001 |
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".