Concurrent disorders treatment and rehospitalization in a forensic population
Bibliographic record
Abstract
The prevalence of concurrent mental illness and substance use is high in general psychiatric patient populations and is an acknowledged risk factor for re-hospitalization. Similarly, concurrent disorders are recognized as a risk factor for re-offending among correctional populations. However, there is currently little evidence regarding the relationship between concurrent disorders and rehospitalization in forensic psychiatric populations. This study therefore investigated the prevalence of concurrent disorders among forensic patients and the relationship of concurrent disorders to rehospitalization in a forensic psychiatric hospital following discharge, comparing whether individuals who received comprehensive risk/needs assessment and inpatient treatment for concurrent disorders had better outcomes than patients who did not. A retrospective chart review compared data from a sample of treatment and non-treatment patients (i.e. patients admitted for court ordered assessment) discharged from a Canadian Forensic Psychiatric Hospital (FPH) in the years 2015-2017. Using quantitative methods we contrasted rates of voluntary and involuntary (direct backs, breaches) rehospitalization and the reasons for returning to hospital as a function of whether the patient’s treatment team had completed a Short Term Assessment of Risk and Treatability (START) and whether they had received inpatient treatment for concurrent disorders in hospital.\n\t\tResults demonstrated that regardless of diagnosis or concurrent disorders treatment, patients who had completed START assessments and who received long term hospitalization had better outcomes than those who did not. This highlights the value of individualized risk/needs assessments for forensic populations and suggests the clinical importance of long-term, continuous care relationships in improving patient outcomes, specifically rehospitalization.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".