Serious Mental Illness and Sexual Offending in Forensic Psychiatric Patients
Bibliographic record
Abstract
The role of serious mental illness among those who sexually offend is not well understood. We investigated clinical and risk-related areas of difference between male forensic psychiatric patients with ( n = 86) and without ( n = 245) a sexual offense history, including the age at which indications of mental disorder and criminal offending first emerged, from a registry of Ontario patients adjudicated Not Criminally Responsible on account of Mental Disorder (NCRMD) from 1999–2012. We further explored motivations for offending among a subset of patients deemed NCRMD for a sexual offense specifically ( n = 41). While no differences were found in the age onset of illness or offending across those with and without a sexual offending history, the former group was rated as having higher levels of historical/static risk for violence. Forensic patients with a sexual offense history were also more likely to offend against a stranger, and less likely to offend against a family member. Sexual index offenses were psychotically-motivated in the majority of cases, but with a meaningful proportion appearing to reflect criminogenic motivations, especially substance use and paraphilic interests. Results suggest greater similarity than difference among forensic patients with and without a sexual offense history, but also highlight an important divergence from the literature showing that victims of sexual offenses are frequently known to the individual committing them.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".