Condom use among injection drug users accessing a supervised injecting facility
Bibliographic record
Abstract
OBJECTIVES: Although supervised injecting facility (SIF) use has been associated with reductions in injection-related risk behaviours, the impact of SIFs on the sexual behaviour of injection drug users (IDUs) has not been thoroughly investigated. Therefore, we examined the patterns and predictors of condom use among SIF users in Vancouver, Canada. METHODS: We performed a longitudinal analysis of the factors associated with consistent condom use among IDUs recruited from within a SIF. RESULTS: Among 1090 individuals, 650 (59.6%) reported a sexual partner in the past 6 months at baseline. Consistent condom use was reported by 108 (25.3%) and 205 (61.6%) individuals reporting regular or casual partners, respectively. After 2 years of observation, these proportions increased to 32.9% and 69.8%, respectively. In multivariate analysis, predictors of consistent condom use with regular partners included HIV positivity (adjusted odds ratio (AOR) 2.23; 95% CI 1.51 to 3.31), injecting with a sex partner (AOR 0.50; 95% CI 0.37 to 0.68), enrollment in addiction treatment (AOR 0.68, 95% CI 0.52 to 0.89) and time since recruitment (AOR 1.29; 95% CI 1.06 to 1.55 per year). Predictors of consistent condom use with casual partners included HIV positivity (AOR 1.70; 95% CI 1.03 to 2.81), syringe borrowing (AOR 0.54; 95% CI 0.32 to 0.91) and syringe lending (AOR 0.52; 95% CI 0.32 to 0.84). CONCLUSIONS: Our results demonstrate that among SIF users, consistent condom use was more frequent among casual sex partners and among HIV positive individuals. Importantly, while the prevalence of consistent condom use was low at baseline, it increased over time. Our findings suggest a possible beneficial effect of the SIF on safer sexual practices.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".