Motivational Influences and Trajectories to Violence in the Context of Major Mental Illness
Bibliographic record
Abstract
Developmental trajectories regarding the age onset of violence and offending have not routinely considered the role of major mental illness (MMI). In parallel, despite several studies investigating the relationship between MMI, violence and offending, fewer have identified motivational processes that may link illness to these outcomes in a more direct and proximal manner. This study investigates whether subtypes of forensic psychiatric patients deemed Not Criminally Responsible on account of Mental Disorder ( N = 91) can be identified based on the age onset of mental illness and offending behavior, and whether information on motivational influences for offending—elicited both from the patient directly and detailed collateral information—contributes to the clinical utility of this typology. Results indicated that most patients reported engaging in violence (51%) or antisocial behaviors (72%) prior to the onset of MMI, but that the index offense(s) resulting in forensic admission were predominantly psychotically motivated. In contrast to patients for whom the onset of MMI occurred prior to offending, patients exhibiting premorbid violence had higher levels of risk and criminogenic need; they were more likely to be diagnosed with personality and substance use disorders, and to have conventional (i.e., non-illness-related) motivations ascribed to their index offense. Findings are consistent with the existing literature regarding subgroups of mentally disordered offenders, but provide new information regarding proximal risk factors for violence through better identification of motivational processes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".