Psychosis as a risk factor for violence to others: A meta-analysis.
Bibliographic record
Abstract
The potential association between psychosis and violence to others has long been debated. Past research findings are mixed and appear to depend on numerous potential moderators. As such, the authors conducted a quantitative review (meta-analysis) of research on the association between psychosis and violence. A total of 885 effect sizes (odds ratios) were calculated or estimated from 204 studies on the basis of 166 independent data sets. The central tendency (median) of the effect sizes indicated that psychosis was significantly associated with a 49%-68% increase in the odds of violence. However, there was substantial dispersion among effect sizes. Moderation analyses indicated that the dispersion was attributable in part to methodological factors, such as study design (e.g., community vs. institutional samples), definition and measurement of psychosis (e.g., diagnostic vs. symptom-level measurement, type of symptom), and comparison group (e.g., psychosis compared with externalizing vs. internalizing vs. no mental disorder). The authors discuss these findings in light of potential causal models of the association between psychosis and violence, the role of psychosis in violence risk assessment and management, and recommendations for future research.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".