Psychopharmaco-psychosocial treatment for substance misuse among individuals in a Canadian forensic psychiatric program
Bibliographic record
Abstract
BACKGROUND: Substance misuse is disproportionately prevalent among individuals in the criminal justice system and can complicate their recovery and community reintegration. While psychosocial therapy is integral for managing substance use, its efficacy may be limited if not well-designed to effectively integrate with psychopharmacological treatment to target patients risk factors and resources. OBJECTIVE: This study presents findings from a semi-structured manualized treatment program (SSMTP) for substance misuse based on motivational enhancement and cognitive behavioural models among individuals in a Canadian forensic psychiatry program. METHODS: This retrospective study was completed by reviewing pre- and post-treatment data of patients with concurrent disorders who underwent the SSMTP in addition to psychopharmacological treatment. The review explored data collected on readiness for change using the Stage of Change Readiness and Treatment Eagerness Scale (SOCRATES) across domains of recognition, ambivalence, and taking steps. RESULTS: The patients (n = 96) were predominantly males (90.6 %). Mean age was 40.25 (SD = 10.04) years. For patients using alcohol, ambivalence increased significantly between pre- and post-SSMTP treatment (p = .025). For patients using cannabis, cocaine, and tobacco, there were significant increases in recognition and taking steps between pre- and post-SSMTP treatment, without any significant change in ambivalence. CONCLUSION: This study suggests SSMTP may increase the level of recognition and degree of steps taken toward substance use cessation and abstinence in forensic populations with concurrent disorders, albeit its effects may amplify within a psychopharmaco-psychosocial treatment model. Future trials with robust methodology are needed to establish the long-term efficacy of SSMTP and generalization to a broader population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".