Effect of Methadone Treatment on Incarceration Rates among Injection Drug Users
Bibliographic record
Abstract
BACKGROUND: Methadone maintenance treatment (MMT) has been shown to dramatically reduce illicit opioid use and criminal activity among injection drug users (IDU). However, questions remain concerning the effect of MMT in reducing rates of incarceration among IDU. We therefore sought to investigate the long-term effect of MMT on rates of incarceration. METHODS: We performed a generalized estimating equation longitudinal analysis of factors associated with incarceration among participants in the Vancouver Injection Drug Users Study (VIDUS). We also recorded whether participants reported having difficulty accessing drug treatment during the study period. RESULTS: Among 1,247 active IDU, 624 (50.0%) reported being incarcerated at least once during the 6-year study period. In multivariate analysis, there was a strong negative association between methadone treatment and incarceration (adjusted odds ratio = 0.64, 95% CI: 0.54-0.76, p < 0.001) despite intensive covariate adjustment. CONCLUSIONS: Given our findings concerning the strong negative association between MMT and incarceration, and the reported high-risk injection practices of incarcerated IDU, limiting the availability of MMT has the potential to further exacerbate the high levels of HIV transmission found among IDU who are in need of treatment both in their communities and in correctional facilities.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".