Assessment and treatment of violence-prone forensic clients: an integrated approach
Bibliographic record
Abstract
BACKGROUND: A risk-reduction treatment programme complemented by a focused assessment, both guided by the risk-need-responsivity principles, is suggested as the preferred treatment for violence-prone individuals with personality disorder. AIMS: Violence Reduction Programme (VRP) and Violence Risk Scale (VRS) were used to illustrate the design and implementation of such an approach. Participants from a similarly designed Aggressive Behaviour Control Programme were used to illustrate the principles discussed and to test programme efficacy. METHOD: The VRS was used to assess risk/need and treatment readiness, and DSM-III/IV psychiatric diagnoses of 203 federal offenders. RESULTS: Participants had a high probability of violent recidivism and many violence-linked criminogenic needs, similar to offenders with high PCL-R scores. Most had antisocial personality disorder and substance use disorders; in terms of treatment-readiness, most were in the contemplation stage of change. Outcome evaluation results support the objectives of the VRP. CONCLUSIONS: Integrating risk-need-responsivity principles in assessment and treatment can provide useful guidelines for intervention with violence-prone forensic clients with personality disorder.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".