Substance Use Disorders as a Critical Element for Decision-Making in Forensic Assertive Community Treatment: A Systematic Review
Bibliographic record
Abstract
Introduction: The prevalence of substance use disorders in forensic populations is high. They are an important factor linked to negative outcomes in mentally ill offenders and are detrimental to forensic or non-forensic outcome measures. In contrast, substance use disorders are often underdiagnosed and undertreated, especially in forensic settings. Forensic Assertive Community Treatment is a forensic adaptation of regular assertive community treatment, combined with essential elements of forensic rehabilitation theories. Little is known however on the effectivity of forensic assertive community treatment when it comes to substance use disorders or what their exact role is on the outcome measures. In this paper, we explore how SUD is treated in Forensic assertive community treatment and how it relates to the forensic and non-forensic outcome measures. Methods: We performed a systematic review (PRISMA) of forensic Assertive community treatment teams that followed the main evidence-based principles of regular assertive community treatment and added basic elements of forensic rehabilitation. We analyzed articles the Psychinfo and Medline databases dating from 2005 to 2020. Fifteen studies fit the search criteria and were included in the analysis. The Quality of the studies was assessed using the Newcastle-Ottawa scale. Results: SUD was highly prevalent in all studies. Patients entered FACT through two pathways, either from a care continuum or directly from prison. The severity of SUD at intake emerges as a critical element when deciding which pathway to choose, as a high severity-score at the start of FACT follow-up was linked to recidivism. While differing in method all studies offered integrated SUD treatment. These included evidence-based techniques like CBT, therapeutic communities, and Substance Abuse Management Module. Though results on SUD outcomes were mixed 4 studies mentioned abstinence in 50–75%. The severity of SUD tended to increase initially and to stabilize afterwards. Conclusion: Severity of SUD at intake emerges as a decisive element in decision-making on entering FACT teams directly from prison or through a care-continuum. The ways to provide SUD treatment varied and outcomes for SUD were mixed. SUD was found to be detrimental to forensic and non-forensic outcome measures, such as recidivism or hospitalizations during FACT treatment.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".