Outcomes of Offenders With Co-Occurring Substance Use Disorders and Mental Disorders
Bibliographic record
Abstract
OBJECTIVE: Whether a diagnosis of a mental disorder contributes to the risk of poorer correctional outcomes is controversial. This study aimed to clarify the extent to which mental and substance use disorders individually and in combination contribute to correctional outcomes in order to determine optimal treatment and promote public safety. METHODS: Differences were examined between four groups of federal offenders in Canada (N=715): those with a mental disorder only, those with a substance use disorder only, those with co-occurring mental and substance use disorders, and those with no disorder. Groups were compared on profiles, criminal histories, charges while incarcerated (institutional charges), and reconvictions after release from incarceration by using chi-square tests and Cox regression analyses that controlled for risk factors. RESULTS: Of the four groups, those with co-occurring disorders had the most substantial criminal histories and the highest rates of institutional charges, transfers to segregation while incarcerated, and reconvictions. The group with only mental disorders had outcomes intermediate between the groups with only substance use disorders and the group with neither type of disorder. CONCLUSIONS: Having a substance use disorder appeared to be the key factor contributing to poorer correctional outcomes for offenders with mental disorders. Psychiatric services in correctional facilities must screen for substance use disorders and, if they are present, ensure provision of treatment to improve quality of life for this population and promote public safety.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".