Developmental typologies of serious mental illness and violence: Evidence from a forensic psychiatric setting
Bibliographic record
Abstract
OBJECTIVE: To identify subgroups of forensic psychiatric patients based on the age onset of serious mental illness and offending and assess the external validity of the subgroups with theoretically based sociodemographic, clinical, legal and risk-related variables. METHOD: The age onset of serious mental illness and criminal contact was ascertained for a sample of 232 patients. A range of sociodemographic, clinical, legal and risk-related variables were coded to assess whether age onset subgroups differed in a manner consistent with the literature on typologies of mentally ill offenders. RESULTS: One-quarter of the sample was classified as early starters (patients whose first offense occurred before becoming mentally ill), while two-thirds were late starters (where first offense occurred following illness onset). A small percentage (8%) of patients were deemed late late starters, defined as late starters who had experienced 10+ years of illness and were >37 years upon first arrest. A larger proportion of early starters had a substance use disorder, antisocial personality disorder and a greater number of static/historical risk factors for violence. Early starters were younger upon first arrest and had more previous criminal contacts compared to late starters and late late starters. Mental illness was found to start later in life for late late starters; this group was also more likely to have been married and to have a spouse as victim in the index offense. CONCLUSION: We found support for distinct subgroups of mentally ill offenders based on the age onset of illness and criminal contact. Compared to late starters, offenses committed by early starters may be motivated more frequently by antisocial lifestyle and attitudes, as well as more instrumental behaviors related to substance abuse. In addition, late late starters may represent a distinct third subgroup within late starters, characterized by relatively higher levels of functioning and social stability; future work should replicate. Findings suggest different rehabilitation needs of the subgroups.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".