Sexual Violence Risk Assessment: An Investigation of the Interrater Reliability of Professional Judgments Made Using the Risk for Sexual Violence Protocol
Bibliographic record
Abstract
The RSVP is a set of structured professional judgment guidelines for assessing risk of sexual violence. We investigated the interrater reliability (IRR) of judgments made using the RSVP in a multidisciplinary forensic-clinical context. Raters were 28 forensic mental health and intellectual disability professionals with diverse training and experience. They used the RSVP to evaluate six case vignettes that varied with respect to offense characteristics, clinical complexity, and level of risk. The IRR of ratings for individual risk factors was generally fair. There was a good level of interrater reliability on Summary Judgments and Supervision Recommendations. Interrater reliability was highest when used by professionals who were highly trained in forensic risk assessment. On average, professionals with lower levels of specialist training agreed less with their colleagues and experts, and provided higher estimations of sexual violence risk. Lower levels of agreement were found in cases with moderate levels of complexity and risk. The RSVP can be used to make judgments of risk with adequate levels of interrater reliability. However, this is dependent on the training and expertise of professionals who use the tool. Methodological strengths and limitations are considered, followed by a discussion of implications for training, practice, and future research.
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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 | 0.295 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".