Incarceration and drug use patterns among a cohort of injection drug users
Bibliographic record
Abstract
AIMS: Drug law enforcement remains the dominant response to drug-related harm. However, the impact of incarceration on deterring drug use remains under-evaluated. We sought to explore the relationship between incarceration and patterns of drug use among people who inject drugs (IDU). DESIGN: Using generalized estimating equations (GEE), we examined the prevalence and correlates of injection cessation among participants in the Vancouver Injection Drug User Study followed over 9 years. In subanalyses, we used McNemar's tests and linear growth curve analyses to assess changes in drug use patterns before and after a period of incarceration among participants reporting incarceration and those not incarcerated. FINDINGS: Among 1603 IDU, 842 (53%) reported injection cessation for at least 6 months at some point during follow-up. In multivariate GEE analyses, recent incarceration was associated negatively with injection cessation [adjusted odds ratio (AOR) = 0.43, 95% confidence interval (CI) 0.37-0.50], whereas the use of methadone was associated positively with cessation (AOR = 1.38, 95% CI 1.22-1.56). In subanalyses assessing longitudinal patterns of drug use among incarcerated individuals and those not incarcerated over the study period, linear growth curve analyses indicated that there were no statistically significant differences in patterns of drug use between the two groups (all P > 0.05). CONCLUSIONS: These observational data suggest that incarceration does not reduce drug use among IDU. Incarceration may inhibit access to mechanisms that promote injection cessation among IDU. In contrast, results indicate that methadone use is associated positively with injection cessation, independent of previous frequency of drug use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".