Expanding the early and late starter model of criminal justice involvement for forensic mental health clients.
Bibliographic record
Abstract
The early and late starter model provides one of the most enduring frameworks for understanding the developmental course and severity of violence and criminality among individuals with severe mental illness. We expanded the model to account for differences in the age of onset of criminal behavior and added a group with no prior contact with the justice or mental health systems. We sampled 1,800 men and women found Not Criminally Responsible on account of Mental Disorder in 3 Canadian provinces. Using a retrospective file-based study, we explored differences in criminal, health, demographic, and social functioning characteristics, processing through the forensic psychiatric system and recidivism outcomes of 5 groups. We replicated prior research, finding more typical criminogenic needs among those with early onset crime. Those with crime onset after mental illness were more likely to show fewer criminogenic needs and to have better outcomes upon release than those who had crime onset during adulthood, before mental illness. Individuals with no prior contact with mental health or criminal justice had higher functioning prior to their crime and had a lower risk of reoffending. Given little information is needed to identify the groups, computing the distribution of these groups within forensic mental health services or across services can provide estimates of potential intensity or duration of services that might be needed. This study suggests that distinguishing subgroups of forensic clients based on the sequence of onset of mental illness and criminal behavior and on the age of onset of criminal behavior may be useful to identify criminogenic needs and predict outcomes upon release. This updated framework can be useful for planning organization of services, understanding case mix, as well as patient flow in forensic services and flow of mentally disordered offenders in correctional services. (PsycINFO Database Record
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".