Assessment of Risk Manageability of Intellectually Disabled Sex Offenders
Bibliographic record
Abstract
Background There are no validated risk assessment tools for intellectually disabled (ID) sex offenders, with the exception of the work of Lindsayet al.[Journal of Applied Research in Intellectual Disabilities(2004)17: 267] regarding the prediction of risk for aggressive behaviour of ID offenders in residential settings. ID sex offenders comprise a neglected subgroup, and one that poses unique challenges and rewards for clinicians. Methods and purpose Recent work by Tough [ Tough S. (2001) Validation of Two Standard Assessments (RRASOR, 1997; STATIC‐99, 1999) on a Sample of Adult Males who are Intellectually Disabled with Significant Cognitive Deficits. Master's Thesis, University of Toronto, Toronto, ON, Canada] examined the utility of the Rapid Risk Assessment for Sexual Offence Recidivism [RRASOR; Hanson R. (1997) The Development of a Brief Actuarial Risk Scale for Sexual Offence Recidivism, User Report 97‐04. Department of the Solicitor General of Canada, Ottawa, ON, Canada.] and the Static‐99 [Hanson R. K. & Thornton D. (1999)Static‐99: Improving Actuarial Risk Assessments for Sex Offenders, User Report 99‐02. Department of the Solicitor General of Canada, Ottawa, ON, Canada] for ID sex offenders. She determined that the Static‐99 may overestimate risk in ID sex offenders and that the RRASOR seemed to be a more accurate tool for these offenders. These actuarial tools provide a ‘risk baseline’, which helps in determining treatment intensity and level of supervision, but do not provide much help in designing treatment plans or management strategies based on the needs of the individual client. Hence, all three authors have developed risk management strategies in their work with ID sex offenders based largely on dynamic factors. This work has produced the present assessment. Outcome The present paper outlines a convergent approach which uses the information provided by static actuarial instruments and relevant dynamic factors as an introduction to the formation of a risk management strategies instrument for ID sex offenders. Thirty suggested items, split into four categories (chronic dynamic and acute dynamic for staff and environment; chronic dynamic and acute dynamic for offenders) are listed along with brief explanations of these items.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".