Factors associated with illicit methadone injecting in a Canadian setting
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: While methadone is well established as an evidence-based treatment for opioid use disorder, safety concerns persist regarding its diversion. The authors examine the prevalence of and risk factors associated with injection of methadone in an urban population. METHODS: Between December 2005 and November 2013, data were derived from two open prospective studies of persons who inject drugs (PWID) in Vancouver, Canada. Generalized estimating equations (GEE) logistic regression was used to determine factors independently associated with illicit methadone injecting. RESULTS: During the study, 1911 individuals (34% women) were recruited; 134 (7%) participants reported methadone injecting at least once. In multivariable analysis, Caucasian ethnicity [adjusted odds ratio (AOR) = 1.90, 95% confidence interval (CI) = 1.20-3.00]; homelessness (AOR = 1.46, 95% CI = 1.09-1.95); drug dealing (AOR = 2.10, 95% CI = 1.50-2.93); ≥daily heroin injection (AOR = 1.57, 95% CI = 1.08-2.26); ≥daily crack smoking (AOR = 2.06, 95% CI = 1.44-2.95); being a victim of violence (AOR = 1.48, 95% CI = 1.04-2.12); and non-fatal overdose (AOR = 1.67, 95% CI = 1.67 (1.00-2.79) were independently and positively associated with methadone injection; female gender (AOR = 0.47, 95% CI = 0.30-0.75) was negatively associated. DISCUSSION AND CONCLUSIONS: The diversion of methadone for illicit injection in this urban setting was associated with several markers of addiction severity and other health and social vulnerabilities. SCIENTIFIC SIGNIFICANCE: These findings underscore the need to ensure methadone accessibility while limiting diversion-related risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".