Effect of Assertive Community Treatment for Patients with Substance Use Disorder: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Substance use disorders (SUD) are an important health issue internationally. Traditional outpatient programmes often do not adequately address the substantial medical and social needs and in addition many patients have difficulties accessing the care needed. The assertive community treatment (ACT) model was originally developed for patients with a severe mental illness but has been adapted for patients with SUD by integrating specific SUD treatments into the traditional ACT model. This paper aims to assess the effectiveness of ACT for patients with SUD on a number of measures. METHODS: We performed a systematic review of ACT interventions for patients with SUD by analyzing randomized controlled studies published before June 2017 found on the electronic databases PsychINFO, MEDLINE, PsychARTICLES. Eleven publications using 5 datasets were included in the analysis. Quality of studies was analyzed using the JADAD scale or Oxford quality scoring system. Outcome measures used were substance use, treatment engagement, hospitalization rates, quality of life, housing status, medication compliance and legal problems. Patients included in the studies had a diagnosis of SUD. Two datasets included homeless patients and 2 datasets included patients with high service use. RESULTS AND CONCLUSIONS: The results of the very few existing randomized control studies are mixed. Treatment engagement was higher for ACT in 4 datasets. One dataset reported higher service contact rates for the ACT group than for controls. In 2 datasets a positive effect on hospitalization rates was found. Higher fidelity to the ACT model appears to improve outcomes. Substance use reduced only in half of the datasets, of which only one showed a significant reduction in the ACT group. Overall, ACT is a promising approach that may be useful for promoting treatment engagement for patients with SUD. According to earlier studies on patients with severe mental illness, patients with high inpatient service use benefit most from this assertive approach. We hypothesize that a similar high need user group among patients with SUD might benefit most from ACT. Further research is needed to examine which types of clinical interventions might help difficult-to-engage patients with addictions.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".