Improving Risk Communication: Developing Risk Ratios for the VRAG-R
Bibliographic record
Abstract
We developed a set of risk ratios for the Violence Risk Appraisal Guide—Revised (VRAG-R) to broaden the range of risk communication options available when using this tool and to provide information needed for future efforts to apply The Council of State Governments Justice Center’s standardized five-level risk framework to the scale. A slightly reduced version of the VRAG-R normative data set was used for the analyses ( N = 1,238). Contrary to previous research developing risk ratios, logistic regression provided a more accurate estimate of observed violent recidivism rates than Cox regression for both total VRAG-R scores and VRAG-R decile bins. Further analyses indicated the relationship between the VRAG-R and violent recidivism was consistent over a 15-year follow-up period. Due to the difficulties with interpreting odds ratios, the final risk ratios were computed using rate ratios derived from a logistic regression model using a 5-year fixed follow-up period. These risk ratios, and templates for how the ratios might be used in an assessment report, are presented in the appendices.
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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 | 0.122 | 0.398 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".