Rate of methadone use among Aboriginal opioid injection drug users
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown elevated rates of health-related harms among Aboriginal people who use injection drugs such as heroin. Methadone maintenance therapy is one of the most effective interventions to address the harms of heroin injection. We assessed the rate of methadone use in a cohort of opioid injection drug users in Vancouver and investigated whether methadone use was associated with Aboriginal ethnic background. METHODS: Using data collected as part of the Vancouver Injection Drug Users Study (May 1996-November 2005), we evaluated whether Aboriginal ethnic background was associated with methadone use using generalized estimating equations and Cox regression analysis. We compared methadone use among Aboriginal and non-Aboriginal injection drug users at the time of enrollment and during the follow-up period, and we evaluated the time to first methadone use among people not using methadone at enrollment. RESULTS: During the study period, 1603 injection drug users (435 Aboriginal, 1168 non-Aboriginal) were recruited. At enrollment, 54 (12.4%) Aboriginal participants used methadone compared with 247 (21.2%) non-Aboriginal participants (odds ratio [OR] 0.53, 95% confidence interval [CI] 0.38-0.73, p < 0.001). Among the 1351 (84.3%) participants who used heroin, Aboriginal people were less likely to use methadone throughout the follow-up period (adjusted OR 0.60, 95% CI 0.45-0.81, p < 0.001). Among people using heroin but who were not taking methadone at enrollment, Aboriginal ethnic background was associated with increased time to first methadone use (adjusted relative hazard 0.60, 95% CI 0.49-0.74, p < 0.001). INTERPRETATION: Methadone use was lower among Aboriginal than among non-Aboriginal injection drug users. Culturally appropriate interventions with full participation of the affected community are required to address this disparity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".