Using the Violence Risk Scale-Sexual Offense version in sexual violence risk assessments: Updated risk categories and recidivism estimates from a multisite sample of treated sexual offenders.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study sought to develop updated risk categories and recidivism estimates for the Violence Risk Scale-Sexual Offense version (VRS-SO; Wong, Olver, Nicholaichuk, & Gordon, 2003-2017), a sexual offender risk assessment and treatment planning tool. The overarching purpose was to increase the clarity and accuracy of communicating risk assessment information that includes a systematic incorporation of new information (i.e., change) to modify risk estimates. Four treated samples of sexual offenders with VRS-SO pretreatment, posttreatment, and Static-99R ratings were combined with a minimum follow-up period of 10-years postrelease (N = 913). Logistic regression was used to model 5- and 10-year sexual and violent (including sexual) recidivism estimates across 6 different regression models employing specific risk and change score information from the VRS-SO and/or Static-99R. A rationale is presented for clinical applications of select models and the necessity of controlling for baseline risk when utilizing change information across repeated assessments. Information concerning relative risk (percentiles) and absolute risk (recidivism estimates) is integrated with common risk assessment language guidelines to generate new risk categories for the VRS-SO. Guidelines for model selection and forensic clinical application of the risk estimates are discussed. (PsycINFO Database Record
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it