Gender differences in access to methadone maintenance therapy in a Canadian setting
Bibliographic record
Abstract
Introduction and Aims: Methadone maintenance therapy (MMT) is an evidence-based treatment for opioid addiction. While gender differences in MMT pharmacokinetics, drug use patterns and clinical profiles have been previously described, few studies have compared rates of MMT use among community-recruited samples of persons who inject drugs (IDU). Design and Methods: The present study used prospective cohorts of IDU followed between May 1996 and May 2013 in Vancouver, British Columbia, Canada. We investigated potential factors associated with time to methadone initiation using Cox proportional hazards modeling. Stratified analyses were used to examine for gender differences in rates of MMT enrolment. Results: Overall, 1848 baseline methadone naïve IDU were included in the study, among whom 595 (32%) were female. In an adjusted model, male gender was independently associated with increased time to MMT initiation and an overall lower rate of enrolment (adjusted relative hazard (ARH) = 0.74 [95% confidence interval [CI]: 0.65-0.85]). Among both female and male IDU, Caucasian ethnicity and daily injection heroin use were associated with decreased time to methadone initiation, while in females pregnancy was also associated with more rapid initiation. Discussion and Conclusions: These data highlight gender differences in methadone use among a population of community-recruited IDU. While factors associated with methadone use were similar between genders, rates of use were lower among male IDU, highlighting the need to consider gender when designing strategies to improve recruitment into MMT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.006 | 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".