Assessing illness- and non–illness-based motivations for violence in persons with major mental illness.
Bibliographic record
Abstract
Research on violence perpetrated by individuals with major mental illness (MMI) typically focuses on the presence of specific psychotic symptoms near the time of the violent act. This approach does not distinguish whether symptoms actually motivate the violence or were merely present at the material time. It also does not consider the possibility that non-illness-related factors (e.g., anger, substance use), or multiple motivations, may have been operative in driving violence. The failure to make these distinctions clouds our ability to understand the origins of violence in people with MMI, to accurately assess risk and criminal responsibility, and to appropriately target interventions to reduce and manage risk. This study describes the development of a new coding instrument designed to assess motivations for violence and offending among individuals with MMI, and reports on the scheme's interrater reliability. Using 72 psychiatric reports which had been submitted to the court to assist in determining criminal responsibility, we found that independent raters were able to assess different motivational influences for violence with a satisfactory degree of consistency. More than three-quarters (79.2%) of the sample were judged to have committed an act of violence as a primary result of illness, whereas 20.8% were deemed to have offended as a result of illness in conjunction with other non-illness-based motivating influences. Current findings have relevance for clarifying the rate of illness-driven violence among psychiatric patients, as well as legal and clinical issues related to violence risk and criminal responsibility more broadly.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".