Bibliographic record
Abstract
This article identifies research priorities for the field of forensic mental health. What is known about the association between mental disorders, retardation, brain damage and offending and violence is briefly reviewed. It is noted that while some of the correlates of offending are common to both non-disordered and disordered offenders, others characterize specific subgroups of mentally disordered offenders. The evidence is consistent in showing that most mentally disordered offenders have multiple problems that have been present, in many cases, since childhood. Knowledge about effective treatments for mentally disordered, mentally retarded, and brain damaged offenders is highlighted. It is concluded that there is a lack of information about the organization, legal powers, and content of treatment, management, and rehabilitation programs that have been shown to impact on recidivism, relapse, and autonomous functioning. Almost nothing is known about the impact of various social services. Future research should be designed to contribute to (1) improving the efficacy of models of service organization; (2) improving the efficacy of treatment, management, and rehabilitation programs; (3) improving the efficacy of the multiple components included in treatment, management, and rehabilitation programs; (4) integrating risk assessment of violent behavior into treatment, management, and rehabilitation programs and improving the accuracy of prediction; (5) identifying the etiologies of offending and violence among persons with mental disorders, mental retardation, and brain damage; and (6) preventing offending and violence among children at risk for mental disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".