The link between psychosis, negative affect, and violence: A systematic review
Bibliographic record
Abstract
A moderate positive association has consistently been observed between psychosis and violence in existing literature. However, substantial variation is present across individual studies, and there is preliminary support for negative affect (i.e., anger, hostility, anxiety) as a causal link between these two constructs to explain the heterogeneity of results. Due to the limited scope of previous reviews and relevant studies published since, a systematic review of 35 empirical studies ( N = 15,597) was conducted to examine if, across existing literature that includes all three constructs, negative affect may correlate or mediate the preestablished relationship between psychosis diagnosis and/or symptoms and violence. Anger or hostility was positively associated with violence or physical aggression for individuals with a diagnosis and/or symptoms of psychosis in 31 (89 %) of the studies, of which seven were cross-sectional, nine were retrospectively predictive, and 19 were prospective. Anger which followed positive psychosis symptoms played a positive statistically mediating role in participants' subsequent violence in all six studies that investigated this pathway. Within studies that examined other forms of negative affect (anxiety, depression, fear), nine discovered positive association with violence, six found negative association, and seven demonstrated no association. These results align with theoretical models of violence in individuals with psychosis, suggesting that psychosis is a sometimes necessary but often insufficient risk factor for violence. When assessing violence risk for individuals with psychosis, negative affect may be critical to consider alongside symptoms in case formulation and to target in subsequent intervention efforts, as opposed to symptoms in isolation.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".