Involuntary discharge from drug or alcohol treatment programs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Retention in substance use treatment is essential to treatment success. While programmatic factors are known to influence retention, less is known about the role of involuntary discharges from drug or alcohol treatment programs. Therefore, we sought to identify the prevalence of and factors associated with involuntary discharge due to ongoing substance use. METHODS: Data were derived from two community-recruited prospective cohort studies of people who use drugs in Vancouver, Canada. Generalized estimating equation (GEE) analyses were used to identify variables associated with involuntary discharge from treatment programs due to ongoing substance use. RESULTS: Between June 2017 and March 2020, 1487 participants who accessed substance use treatment and completed at least one study interview were included in this study. Involuntary discharge from a treatment program due to ongoing substance use was reported by 41 (2.8%) participants throughout the study, with 23 instances reported at baseline and another 18 reported during study follow-up. In a multivariable GEE analysis, involuntary discharge was positively associated with homelessness (Adjusted Odds Ratio [AOR] = 3.22, 95% Confidence Interval [95% CI]: 1.59-6.52), daily injection drug use (AOR = 1.87, 95% CI 1.06-3.32) and recent overdose (AOR = 2.50, 95% CI 1.38-4.53), and negatively associated with age (AOR = 0.93, 95% CI 0.90-0.96). In sub-analyses, participants have most commonly been discharged from in-patient treatment centres (52.2%), recovery houses (28.3%) and detox programs (10.9%), and for using heroin (45.5%) and/or crystal methamphetamine (36.4%). CONCLUSIONS: While involuntary discharge was a relatively rare occurrence, those who were discharged due to active substance use possessed several markers of risk, including high-intensity injection drug use, homelessness, and recent non-fatal overdose. Our findings highlight the need for increased flexibility within treatment programs to account for those who re-initiate or continue to use substances during treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".