Offenders with mental illness have criminogenic needs, too: Toward recidivism reduction.
Bibliographic record
Abstract
Many programs for offenders with mental illness (OMIs) seem to assume that serious mental illness directly causes criminal justice involvement. To help evaluate this assumption, we assessed a matched sample of 221 parolees with and without mental illness and then followed them for over 1 year to track recidivism. First, compared with their relatively healthy counterparts, OMIs were equally likely to be rearrested, but were more likely to return to prison custody. Second, beyond risk factors unique to mental illness (e.g., acute symptoms; operationalized with part of the Historical-Clinical-Risk Management-20; Webster, Douglas, Eaves, & Hart, 1997), OMIs also had significantly more general risk factors for recidivism (e.g., antisocial pattern; operationalized with the Level of Service/Case Management Inventory; Andrews, Bonta, & Wormith, 2004) than offenders without mental illness. Third, these general risk factors significantly predicted recidivism, with no incremental utility added by risk factors unique to mental illness. Implications for broadening the policy model to explicitly target general risk factors for recidivism such as antisocial traits are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".