Use of the Violence Risk Scale—Sexual Offender Version and the Stable 2007 to assess dynamic sexual violence risk in a sample of treated sexual offenders.
Bibliographic record
Abstract
The present study provides an examination of dynamic sexual violence risk featuring the Stable-2007 (Hanson, Harris, Scott, & Helmus, 2007) and the Violence Risk Scale-Sexual Offender version (VRS-SO; Wong, Olver, Nicholaichuk, & Gordon, 2003) in a Canadian sample of 180 federally incarcerated sexual offenders who attended a high-intensity sexual offender treatment program. Archival pretreatment and posttreatment ratings were completed on the VRS-SO and Stable-2007, and recidivism data were obtained from official criminal records, with the sample being followed up approximately 10 years postrelease. VRS-SO pre- and posttreatment dynamic scores demonstrated significant predictive accuracy for sexual, nonsexual violent, any violent (including sexual), and general recidivism, while Stable-2007 pre- and posttreatment scores were significantly associated with the latter 3 outcomes; these associations were maintained after controlling for the Static-99R (Helmus, Thornton, Hanson, & Babchishin, 2012). Finally, significant pre-post differences, amounting to approximately three quarters of a standard deviation, were found on Stable-2007 and VRS-SO scores. VRS-SO change scores were significantly associated with reductions in nonsexual violent, any violent, and general recidivism (but not sexual recidivism) after controlling for baseline risk or pretreatment score, while Stable-2007 change scores did not significantly predict reductions in any recidivism outcomes. Applications of these tools within the context of dynamic sexual violence risk assessment incorporating the use of change information are discussed. (PsycINFO Database Record
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".