Improving Practices of Risk Assessment and Intervention Planning for Persons with Intellectual Disabilities Who Sexually Offend
Bibliographic record
Abstract
Abstract Proactive assessment and intervention planning should be undertaken in cases of sexual behavior by persons with intellectual disability (ID) that is outside of acceptable norms. Although best practices in risk assessment for the general offender population are somewhat established, sexually inappropriate behavior among individuals with ID is distinct. Research in effective specialized risk assessment and intervention planning for this client group is an emerging area of focus. This article reviews contemporary academic research on this subject and highlights opportunities for improvement. First, research on risk factors for this client group was reviewed, observing the differences between empirically validated common factors and factors that are specific to the individual. Second, existing actuarial and structured clinical judgment approaches were evaluated. Third, the person‐centered planning approach to service planning for persons with ID was examined, highlighting how the principles of this approach may be used to enhance current practices in risk assessment and intervention planning. The author concludes that effective collaboration between support teams and clinical professionals, with focus maximized on individual risk factors and strengths, should lead to improved outcomes of risk assessment and intervention planning for persons with ID whose sexual behavior is inappropriate. A call to action is presented for the development of an enhanced, fourth‐generation, approach that embodies a collaborative framework.
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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 | 0.034 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".