A Follow‐up of Deinstitutionalized Men with Intellectual Disabilities and Histories of Antisocial Behaviour
Bibliographic record
Abstract
Background There is frequently great concern about the dangerousness of deinstitutionalized men with intellectual disabilities who have been institutionalized because they are considered to be at high risk for the commission of serious antisocial acts or sexual offending. Unfortunately, there is little information on whether changes in the behaviour of these men can be used to adjust supervision so as to manage risk. Methods An appraisal of men with intellectual disabilities and histories of serious antisocial behaviours who were residing in institutions about to be closed led to a 16 month follow‐up of 58 of these clients who had been transferred to community settings. Results A total of 67% exhibited antisocial behaviour of some kind and 47% exhibited ‘hands‐on’ violent or sexual misbehaviours directed toward other clients or staff. The Violent Risk Appraisal Guide was the best predictor of new violent or sexual incidents and a variety of other pre‐release predictors were related to the likelihood of antisocial incidents of any kind. Overall predictive accuracy was moderate. A field trial showed that monthly staff ratings of client characteristics were related to antisocial incidents. Conclusions These preliminary data indicate that measures of dynamic risk involving staff ratings are worth developing and evaluating.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".