HIV and Hepatitis C Prevalence and Risk Behavior among People Who Inject or Inhale Drugs in Whitehorse, Yukon, Canada.
Bibliographic record
Abstract
INTRODUCTION: People who use drugs are at increased risk for acquiring blood-borne infections such as HIV and hepatitis C virus (HCV) through the use of contaminated drug equipment. Little is known about people who use drugs in Yukon, a territory in northern Canada, and the prevalence of HIV and HCV in this population. METHODS: An interviewer-administered questionnaire (part of I-Track Phase 3 enhanced surveillance in Canada) collected information on demographics, drug use, injection and non-injection risk behavior, sexual risk behavior and use of services among people who use drugs in Whitehorse, Yukon. A biological sample was collected and tested for HIV and HCV antibodies. Chi-square/Fisher's exact tests assessed differences in proportions with significance level set at P <0.05; logistic regression models assessed correlates for HCV seropositivity and sharing of used needles, syringes or equipment. RESULTS: 103 drug users (55 injectors, 48 non-injectors) were interviewed in Whitehorse. Average age was 39 years and 62% were male. HIV seropositivity was 6% (95% CI = 2.2–12.5%). Awareness of HIV seropositivity was 100%. Lifetime exposure to HCV was 45% (95% CI = 35.0–55.3%). Most frequently injected drugs were cocaine (74%) and non-prescribed morphine (56%). 20% and 19% of injectors borrowed and lent used needles, respectively. 42% and 44% of injectors borrowed and lent used equipment, respectively. Condom use at last sex was 29% and 38% reported two or more sex partners. 90% of respondents ever used a needle exchange program. HCV seropositivity was associated with currently injecting ( P = 0.007) and time since first injection ( P = 0.007). Sharing of used needles, syringes or equipment was associated with time since first injection ( P = 0.004) and most common drug partner ( P = 0.004).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".