The Predictive Properties of Psychiatric Diagnoses, Dynamic Risk and Dynamic Risk Change Assessed by the VRS-SO in Forensically Admitted and Released Sexual Offenders
Bibliographic record
Abstract
Psychiatric diagnoses, static risk factors, and criminogenic needs at time of admission and release were examined in a mentally ill sample of psychiatrically detained sexual offenders. Although clinically found to be at low or even very low risk at discharge, 12% reoffended sexually over an average follow-up of 7 years. Psychotic disorders were present in only 5% of offenders, whereas 93% had a personality disorder diagnosis and 76% a paraphilic disorder diagnosis. Only exhibitionism and alcohol misuse were associated with relapse. Static risk factors captured by the Static-99 also did not significantly predict recidivism; however, the VRS-SO-a structured risk assessment tool that assesses criminogenic needs and changes in risk from treatment or other change agents, rated retrospectively on the present sample-predicted sexual recidivism as well as any new imprisonment or psychiatric placement. In particular, the sexual deviance factor of the VRS-SO had large in magnitude predictive associations with sexual reoffending, while treatment related changes assessed on this factor were significantly related to non-reoffending. Findings corroborate the advantages of structured risk assessment and structured change monitoring, particularly for complex clientele such as mentally ill sexual offenders.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".