Severe mental illness diagnoses and their association with reoffending in a sample of men adjudicated for sexual offences
Bibliographic record
Abstract
The current study examined the relationship between mental illness and recidivism in a sample of 409 men adjudicated for sexual offences who scored higher than average on an established risk assessment tool (Static-99R). Participants were from all provincial correctional systems (except Prince Edward Island) and all regions of the Correctional Service of Canada. Severe mental illness diagnoses, with the exception of some personality disorders, were not associated with recidivism (after an average follow-up of 11 years). While some personality diagnoses were initially related to recidivism, this relationship often disappeared or was attenuated after controlling for substance misuse and risk score on the Static-99R. There were two exceptions: Histrionic and narcissistic personality disorders continued to predict sexual recidivism after controlling for Static-99R and substance misuse history. In sum, the current study suggests that severe mental illness diagnoses are not associated with higher rates of recidivism after accounting for risk score and substance misuse in men with sexual offences, with the exception of histrionic and narcissistic personality disorder diagnoses. For this reason, risk judgements that weigh both known risk factors and severe mental illness may overestimate an individual’s risk to reoffend.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".