Rethinking Approaches to Risk Reduction for Injection Drug Users
Bibliographic record
Abstract
OBJECTIVE: To identify and compare the drug-injecting network characteristics of cocaine and heroin injectors associated with a risk of HIV and hepatitis C virus (HCV). METHODS: Active injectors were recruited from syringe exchange and methadone programs. Characteristics of all participants and their social networks were elicited. Regression analysis using generalized estimating equations examined the network characteristics of injection drug users (IDUs) relative to cocaine or heroin use in the past 6 months. RESULTS: Of 282 IDUs, 228 (81%) used cocaine and 54 (19%) used heroin as their primary injected drug. In analyses adjusted for age and gender, cocaine injectors compared with heroin injectors were more likely to live in unstable housing (odds ratio [OR] = 3.55, 95% confidence interval [CI]: 1.49 to 8.40), self-report HCV infection (OR = 4.69, 95% CI: 2.14 to 10.31), and have a greater number of IDUs in their social network (OR = 1.61, 95% CI: 1.14 to 2.28) and were less likely to be polydrug users (OR = 0.06, 95% CI: 0.02 to 0.16) and to have social support (OR = 0.97, 95% CI: 0.95 to 0.99). The injecting networks of cocaine users were more likely to have members who were older (OR = 1.08, 95% CI: 1.04 to 1.12), had a history of shooting gallery use (OR = 2.27, 95% CI: 1.08 to 4.76), and had shorter relationships with the subject (OR = 0.91, 95% CI: 0.85 to 0.97). CONCLUSIONS: Beyond personal behaviors, HIV and HCV infection risk seems to be linked to social network traits that are determined by drug type. Prevention efforts to control the spread of bloodborne viruses among IDUs could benefit from tailoring interventions according to the type of drug used.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".