Risk Factors Associated with Unsafe Injection Practices at the First Injection Episode among Intravenous Drug Users in France: Results from PrimInject, an Internet Survey
Bibliographic record
Abstract
UNLABELLED: Background. New drug use patterns may increase the risk of human immunodeficiency virus and hepatitis infections. In France, new injection patterns among youths with diverse social backgrounds have emerged, which may explain the persistently high rates of hepatitis C virus infection. This study explores factors associated with injection risk behaviours at first injection among users who began injecting in the post-2000 era. Methods. A cross-sectional study was conducted on the Internet from October 2010 to March 2011, through an online questionnaire. Multivariate logistic regression identified the independent correlates of needle sharing and equipment (cooker/cotton filter) sharing. Results. Among the 262 respondents (mean age 25 years), 65% were male. Both risk behaviours were positively associated with initiation before 18 years of age (aOR 3.7 CI 95% 1.3-10.6 and aOR 3.0 CI 95% 1.3-7.0) and being injected by another person (aOR 3.1 CI 95% 1.0-9.9 and aOR 3.0 CI 95% 1.3-7.1). Initiation at a party was an independent correlate of equipment sharing (aOR 2.6 95% CI 1.0-6.8). CONCLUSIONS: Results suggest a need for innovative harm reduction programmes targeting a variety of settings and populations, including youths and diverse party scenes. Education of current injectors to protect both themselves and those they might initiate into injection is critically important.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".