Harmful microinjecting practices among a cohort of injection drug users in Vancouver Canada
Bibliographic record
Abstract
We sought to identify factors associated with harmful microinjecting practices in a longitudinal cohort of IDU. Methods: Using data from the Vancouver Injection Drug Users Study (VIDUS) between January 2004 and December 2005, generalized estimating equations (GEE) logistic regression was performed to examine sociodemographic and behavioral factors associated with four harmful microinjecting practices (frequent rushed injecting, frequent syringe borrowing, frequently injecting with a used water capsule, frequently injecting alone). Results: In total, 620 participants were included in the present analysis. Our study included 251 (40.5%) women and 203 (32.7%) self-identified Aboriginal participants. The median age was 31.9 (interquartile range: 23.4–39.3). GEE analyses found that each harmful microinjecting practice was associated with a unique profile of sociodemographic and behavioral factors. Discussion: We observed high rates of harmful microinjecting practices among IDU. The present study describes the epidemiology of harmful microinjecting practices and points to the need for strategies that target higher risk individuals including the use of peer-driven programs and drug-specific approaches in an effort to promote safer injecting 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".