Probationers mandated to receive forensic mental health services in Canada: Risks/needs, service delivery, and intermediate outcomes.
Bibliographic record
Abstract
Probation orders mandating forensic mental health services are used by the courts to deal with the complex needs of probationers with mental disorders, but little is known about this particular legal leverage. The main goals of this study were to describe probationers who were mandated to receive forensic services, and to examine the relationships between measures of risks and needs, service delivery, and intermediate outcomes. To achieve this, we conducted a retrospective review of health records of 250 probationers with mental disorders using several standardized measures to assess criminogenic and psychosocial needs. The study also tracked outcomes and service utilization for an average of 9 months while probationers were receiving forensic services. A framework that integrates criminogenic and mental health needs to guide service delivery (Prins & Draper, 2009) was used as a theoretical backdrop. The probationer sample represented a heterogeneous group with a diverse range of criminogenic, clinical, and social needs. Their level of risk, service need, and clinical problems did not strongly determine the intensity of forensic services. Noncompliant probationers, representing almost half of the sample, had greater odds of psychiatric adverse events, criminal justice contacts, and violent behavior. This study represents one of the first to examine theoretical and pragmatic issues pertaining to the provision of forensic mental health services to probationers. Language: en
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 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".