Risk reduction treatment of psychopathy and applications to mentally disordered offenders
Bibliographic record
Abstract
Therapeutic nihilism on treating psychopathy is widespread and is largely based on many outdated and poorly designed studies. Important recent advances have been made in assessing psychopathy and recidivism risks, as well as in offender rehabilitation to reduce reoffending, all of which are now well supported by a considerable literature based on credible empirical research. A 2-component model to guide risk reduction treatment of psychopathy has been proposed based on the integration of key points from the 3 bodies of literature. Treatment programs in line with the model have been in operation, and the results of early outcome evaluations are encouraging. Important advances also have been made in understanding the possible etiology of mentally disordered offenders with schizophrenia and history of criminality and violence, some with significant features of psychopathy. This article presents a review of recent research on risk reduction treatment of psychopathy with the additional aim to extend the research to the treatment of mentally disordered offenders with schizophrenia, violence, and psychopathy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".