Medical Detoxification for Nonopioid Substances Is Associated With Lower Likelihood of Subsequent Linkage to Substance Use Disorder Treatment
Bibliographic record
Abstract
BACKGROUND: Although factors associated with completion of medical detoxification treatment for substance use disorders (SUD) are well described, there is limited information on barriers and facilitators to subsequent linkage to SUD treatment in the community. This study aimed to evaluate correlates of successful linkage to community SUD treatment on discharge. METHODS: Data were drawn from 2 prospective cohorts of people who use unregulated drugs in Vancouver, Canada between December 2012 and May 2018. Multivariable generalized estimating equations were used to investigate factors associated with linkage to community SUD treatment in the 6-month period after attending detoxification treatment. RESULTS: Of the 264 detoxification treatment encounters contributed by 178 people who use unregulated drugs, these were most often (n = 104, 39%) related to polysubstance use, and the majority (n = 174, 66%) resulted in subsequent linkage to community treatment. In the multivariable analysis, compared to attending detoxification treatment for opioid use, attending detoxification treatment for stimulants (adjusted odds ratio [AOR] = 0.23, 95% confidence interval [CI] : 0.10-0.51) and alcohol (AOR = 0.17, 95% CI: 0.06-0.54) were associated with lower odds of subsequent linkage to community treatment. Conversely, later calendar year of detoxification treatment remained associated with higher odds (AOR = 1.23, 95% CI: 1.06-1.42). CONCLUSIONS: Only two-thirds of detoxification treatment encounters in Vancouver were subsequently linked to community SUD treatment, with those related to nonopioid substances being less likely. Findings suggest the need for tailored interventions for specific substances to improve linkage to SUD treatment in the community on discharge.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".