Psychometric Examination of Treatment Change Among Mentally Disordered Offenders: A Risk–Needs Analysis
Bibliographic record
Abstract
The present study examined the association of psychiatric symptomatology, criminal attitudes, and treatment changes within these domains to violent and general recidivism in a sample of 614 mentally disordered offenders. Significant pre–post changes were found on multiple measures of criminal attitudes, symptomatology, and readiness for change. Antisocial Intentions and Attitudes Toward Associates (from the Measure of Criminal Attitudes and Associates [MCAA]) predicted general recidivism and covaried with the Big Four criminogenic need domains on the Level of Service Inventory–Ontario Revision; none of the remaining psychometric measures significantly predicted violent or general recidivism. Although pre–post changes were seldom linked to changes in recidivism, positive changes in Antisocial Intentions (MCAA) significantly predicted reductions in general recidivism via Cox regression survival analysis, controlling for baseline risk and pretreatment attitudes score. Risk and need implications of psychometric assessments of treatment change in mentally disordered offender populations are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".