Getting to full disclosure: HCV testing and status disclosure behaviors among PWID and their injecting partners
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) face a substantial risk of hepatitis C virus (HCV) infection, often in the context of multiple injecting partnerships. The disclosure of HCV status to injecting partners holds significant implications for prevention and care among PWID. METHODS: We used cross-sectional dyadic survey data (collected from both members of injecting partnerships) to estimate the prevalence of HCV-status disclosure between PWID and their injecting partners, overall and by partnership HCV infection status. RESULTS: Across the two study sites (San Francisco and Montreal), 91% of participants self-reported receiving an HCV test, resulting in 162 individuals and 131 partnerships. A majority (57%) self-reported being HCV positive. HCV status disclosure was prevalent overall (79%) and was most common (41%) with partnerships where both partners' status was positive (+ / +) but less common (17%) when one partner was positive ( ±) and when neither partner was positive (-/-) (32%); no disclosure was more common when both partners were negative (-/-) (50%). CONCLUSIONS: Overall, our study demonstrated a high prevalence of HCV testing and subsequent disclosure of HCV status within injecting partnerships. This presents an opportunity to leverage these relationships for treatment linkage and prevention messaging.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".