Does Reassessment Improve Prediction? A Prospective Study of the Sexual Offender Treatment Intervention and Progress Scale (SOTIPS)
Bibliographic record
Abstract
This prospective study examined the predictive validity of the Sex Offender Treatment Intervention and Progress Scale (SOTIPS; McGrath et al., 2012), a sexual recidivism risk/need tool designed to identify dynamic (changeable) risk factors relevant to supervision and treatment. The SOTIPS risk tool was scored by probation officers at two sites ( n = 565) for three time points: near the start of community supervision, at 6 months, and then at 12 months. Given that conventions for analyzing dynamic prediction studies have yet to be established, one of the goals of the current paper was to demonstrate promising statistical approaches for the analysis of longitudinal studies in corrections. In most analyses, static SOTIPS scores predicted all types of recidivism (sexual, violent, and general [any]). Dynamic SOTIPS scores, however, only improved the prediction of general recidivism, and only when the analyses with the greatest statistical power were used (Cox regression with time dependent covariates).
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".