Reliability and consistency of risk formulations in assessments of sexual violence risk
Bibliographic record
Abstract
Sexual violence represents an intrusion of personal boundaries that can be physically and psychologically traumatic for the victim.Assessing risk for sexual violence is an important process that can have serious consequences for the public (e.g., risk to public safety) and the individual assessed (e.g., indefinite commitment).Given the serious potential consequences, it is vital that assessments are conducted using empirically supported risk assessment measures.The Risk for Sexual Violence Protocol (RSVP; Hart, et al., 2003) is a measure to guide sexual violence risk assessments.The RSVP provides a framework for case formulation, a process that gathers diverse case-specific information to guide decision-making.Formulation is an essential element of risk assessment, but has been neglected in research.The current study added to the literature base supporting the RSVP and addressed the gap in the literature concerning formulation.First, reliability of presence, relevance and summary risk judgments was examined.Second, the similarity of formulations made by different raters for the same cases was compared to that of formulations made by different raters for different cases.Seventeen professionals completed an online risk assessment course on the administration of the RSVP and completed file-based RSVP assessments for six of ten cases.Rater agreement for presence and relevance ratings and summary judgments was poor to fair, whereas agreement for domain and total scores was fair to good.Similarity ratings (made by independent judges) for randomly selected pairs of formulations made by different raters for the same cases were significantly higher than those made by different raters for different cases.This was true both for global ratings of formulation similarity (i.e., causes of past sexual violence, scenarios of future sexual violence, recommended management strategies), as well as specific facets of formulations similarity (e.g., identification of motivating, disinhibiting, and destabilizing risk factors in past sexual violence; nature, severity of future sexual violence; monitoring, supervision, treatment, and victim safety planning tactics).The findings provide evidence that formulations of violence risk are consistent or similar across raters.Findings are discussed in the context of risk assessment practice, directions for risk assessment training, 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 | 0.041 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".