Incorporating Change Information Into Sexual Offender Risk Assessments Using the Violence Risk Scale–Sexual Offender Version
Bibliographic record
Abstract
We examined the use of risk-change information in sexual offender risk assessments featuring the Violence Risk Scale-Sexual Offender version (VRS-SO), a sex offender risk assessment and treatment planning tool. The study featured a combined international sample of 539 sex offenders followed up an average of 15.5 years post-release. Pre- and posttreatment VRS-SO ratings were amalgamated from two treated samples of sex offenders from Canada and New Zealand. Analyses focused on examinations and applications of change data and its relationship to sexual and violent recidivism. VRS-SO change scores were significantly associated with decreases in these outcome criteria with, and without, controlling for indicators of pretreatment risk (e.g., Static-99R score) and individual differences in follow-up time. Applications of logistic regression using fixed 5-year follow-ups generated estimated rates of sexual and violent recidivism at different VRS-SO score thresholds. The use of logistic regression demonstrated a clinically useful and systematic means of combining risk and change information into posttreatment risk appraisals. Implications for the use of change information in the assessment and management of sexual offender risk are discussed.
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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".