Assessing risk of reoffending in adolescents who have committed a sexual offense: the accuracy of clinical judgments after completion of risk assessment instruments
Bibliographic record
Abstract
As courts often rely on clinicians when differentiating between sexually abusive youth at a low versus high risk of reoffense, understanding factors that contribute to accuracy in assessment of risk is imperative. The present study built on existing research by examining (1) the accuracy of clinical judgments of risk made after completing risk assessment instruments, (2) whether instrument-informed clinical judgments made with a high degree of confidence are associated with greater accuracy, and (3) the risk assessment instruments and subscales most predictive of clinical judgments. Raters assessed each youth's (n = 166) risk of reoffending after completing the SAVRY and J-SOAP-II. Raters were not able to predict detected cases of either sexual recidivism or nonsexual violent recidivism above chance, and a high degree of rater confidence was not associated with higher levels of accuracy. Total scores on the J-SOAP-II were predictive of instrument-informed clinical judgments of sexual risk, and total scores on the SAVRY of nonsexual risk.
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".