The validity and reliability of the Violence Risk Scale-Sexual Offender version: Assessing sex offender risk and evaluating therapeutic change.
Bibliographic record
Abstract
The Violence Risk Scale-Sexual Offender version (VRS-SO) is a rating scale designed to assess risk and predict sexual recidivism, to measure and link treatment changes to sexual recidivism, and to inform the delivery of sexual offender treatment. The VRS-SO comprises 7 static and 17 dynamic items empirically or conceptually linked to sexual recidivism. Dynamic items with higher ratings identify treatment targets linked to sexual offending. A modified stages of change model assesses the offender's treatment readiness and change. File-based VRS-SO ratings were completed on 321 sex offenders followed up an average of 10 years post-release. VRS-SO scores predicted sexual and nonsexual violent recidivism post-release and demonstrated acceptable interrater reliability and concurrent validity. A factor analysis of the dynamic items generated 3 factors labeled Sexual Deviance, Criminality, and Treatment Responsivity, all of which predicted sexual recidivism and were differentially associated with different sex offender types. The dynamic items together made incremental contributions to sexual recidivism prediction after static risk was controlled for. Positive changes in the dynamic items, measured at pre- and posttreatment, were significantly related to reductions in sexual recidivism after risk and follow-up time were controlled for, suggesting that dynamic items are indeed dynamic or changeable in nature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".