Crystal Methamphetamine Use and Methadone Maintenance Treatment Dissatisfaction : A Prospective Cohort Study in Vancouver, Canada
Bibliographic record
Abstract
Background: Patient satisfaction is key to the success of methadone maintenance treatment (MMT), and yet how MMT satisfaction is affected by the increasingly common use of crystal methamphetamine among people receiving opioid treatment remains poorly understood. We aimed to assess the association between crystal methamphetamine use and MMT dissatisfaction. Methods: We employed generalized estimating equations to examine the relationship between crystal methamphetamine use and MMT dissatisfaction among patients receiving MMT within two prospective cohorts in Vancouver, Canada between December 2016 and March 2020. Results: Of the 836 participants receiving MMT, the median age was 47 years, and 55.3% self-identified as male at baseline. In a multivariable model, those reporting more than weekly crystal methamphetamine use were more likely to report MMT dissatisfaction (Odds ratio: 1.40, 95% confidence interval: 1.05 – 1.86) compared to those reporting less than monthly crystal methamphetamine use. Conclusions: Among our sample of people receiving MMT, we noted a positive association of frequent crystal methamphetamine use with MMT dissatisfaction. Our study suggests a need for novel strategies to better understand and address frequent methamphetamine use among those receiving MMT, particularly given recent shifts in substance use patterns involving the rising co-use of methamphetamines and opioids.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".