Factors associated with pretreatment and treatment dropouts: comparisons between Aboriginal and non-Aboriginal clients admitted to medical withdrawal management
Bibliographic record
Abstract
BACKGROUND: Addiction treatment faces high pretreatment and treatment dropout rates, especially among Aboriginals. In this study we examined characteristic differences between Aboriginal and non-Aboriginal clients accessing an inpatient medical withdrawal management program, and identified risk factors associated with the probabilities of pretreatment and treatment dropouts, respectively. METHODS: 2231 unique clients (Aboriginal = 451; 20%) referred to Vancouver Detox over a two-year period were assessed. For both Aboriginal and non-Aboriginal groups, multivariate logistic regression analyses were conducted with pretreatment dropout and treatment dropout as dependent variables, respectively. RESULTS: Aboriginal clients had higher pretreatment and treatment dropout rates compared to non-Aboriginal clients (41.0% vs. 32.7% and 25.9% vs. 20.0%, respectively). For Aboriginal people, no fixed address (NFA) was the only predictor of pretreatment dropout. For treatment dropout, significant predictors were: being female, having HCV infection, and being discharged on welfare check issue days or weekends. For non-Aboriginal clients, being male, NFA, alcohol as a preferred substance, and being on methadone maintenance treatment (MMT) at referral were associated with pretreatment dropout. Significant risk factors for treatment dropout were: being younger, having a preferred substance other than alcohol, having opiates as a preferred substance, and being discharged on weekends. CONCLUSIONS: Our results highlight the importance of social factors for the Aboriginal population compared to substance-specific factors for the non-Aboriginal population. These findings should help clinicians and decision-makers to recognize the importance of social supports especially housing and initiate appropriate services to improve treatment intake and subsequent retention, physical and mental health outcomes and the cost-effectiveness of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".