A prospective multisite examination of dynamic sexual violence risk: Extension and update to Olver, Nicholaichuk, Kingston, and Wong (2014).
Bibliographic record
Abstract
OBJECTIVE: The present study is an extension and update of Olver, Nicholaichuk, Kingston, and Wong's (2014) prospective multisite examination of sexual violence risk and treatment change on a large federal Canadian sample of 570 treated sexual offenders rated pretreatment and posttreatment on the Violence Risk Scale-Sexual Offense version (VRS-SO). METHOD: The present study featured the clinical application of a risk assessment and treatment planning tool, the VRS-SO, with recidivism outcome data updated by 4 years to a total of 10.2 years. VRS-SO change scores, representing reductions in sexual violence risk from treatment or other change agents (e.g., aging), were reanalyzed in terms of their associations with community sexual, violent, and general recidivism postrelease. RESULTS: = .878 to .938). CONCLUSIONS: The results affirm the dynamic nature of sexual violence risk and demonstrate that structured assessments of change, linked to participation in sexual offense specific treatment, to be associated with decreases in future sexual offending as well as other recidivism outcomes. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".