Factors Associated with Pretreatment and Treatment Dropouts Among Clients Admitted to Medical Withdrawal Management
Bibliographic record
Abstract
The aims of this study were to identify factors associated with pretreatment and treatment dropouts among individuals accessing an inpatient medical withdrawal management program (Vancouver Detox). Two thousand five hundred sixty-six unique clients, who were referred to Vancouver Detox over two-year period, were assessed. Demographic and drug related variables were analyzed as possible risk factors, and two multivariate logistic regression analyses were conducted. We found that being male, being aboriginal, having no children, no fixed address, alcohol as a preferred substance, and being on methadone maintenance treatment at referral were significantly associated with high pretreatment dropout. Significant risk factors for treatment dropout were: being younger, having HCV infection, having a preferred substance other than alcohol, having opiates as a preferred substance, and being discharged on welfare check issue periods or weekends. These findings may help clinicians and decision-makers to initiate corresponding preventive measures to decrease unnecessary attritions and improve utilization of treatment resources.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".