Reduction in Injection–Related HIV Risk After 6 Months in a Low-Threshold Methadone Treatment Program
Bibliographic record
Abstract
This study assessed injection-related HIV risk behavioral changes among opioid users 6 months after enrollment in low-threshold (harm reduction based) metha-done maintenance treatment (MMT) programs within needle exchange services in Kingston and Toronto, Ontario, Canada. Changes were assessed for all participants (whole cohort), participants who continued to use illicit drugs by any route (drug-using subcohort); and those who continued to inject drugs (injecting subcohort). In this prospective observational cohort study, an interviewer-administered questionnaire examining injection-related HIV risk behaviors was administered to 183 study participants at entry to treatment and 6 months later. Changes in risk behaviors were analyzed using conditional logistic regression which took into account the paired nature of the data. We found that the proportion of participants injecting drugs, sharing needles, sharing drug equipment, indirectly sharing and using shooting galleries declined with follow-up for the whole cohort. Within the drug-using subcohort, there was a decrease in the proportion of individuals who injected drugs, while within the injecting subcohort the sharing of injection equipment and the use of shooting galleries declined. Our findings suggest that low-threshold MMT programs can reduce the risk of HIV without the enforcement of abstinence-based policies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".