Barriers of Linkage to Hepatitis C Care and Treatment Among People Who Inject Drugs in Georgia
Bibliographic record
Abstract
Abstract Background: People who inject drugs (PWID) in Georgia have a high prevalence of hepatitis C virus antibody (anti-HCV). Access to care among PWID could be prioritized to meet the country’s hepatitis C elimination goals. This study assesses barriers of linkage to hepatitis C care among PWID in Georgia.Methods: Study participants were enrolled from 13 harm reduction centers throughout Georgia. Anti-HCV positive PWID who were tested for viremia (linked to care [LC]), were compared to those not tested for viremia within 90 days of screening anti-HCV positive (not linked to care [NLC]). Participants were interviewed about potential barriers to seeking care.Results: A total of 500 PWID were enrolled, 245 LC and 255 NLC. LC and NLC were similar with respect to gender, age, employment status, education, knowledge of anti-HCV status, and confidence/trust in the elimination program (p>0.05). More NLC (13.0%) than LC (7.4%) stated they were not sufficiently informed what to do after screening anti-HCV positive (p<0.05). In multivariate analysis, linkage to care was associated with perceived affordability of the elimination program (adjusted prevalence ratio=8.53; 95% confidence interval: 4.14-17.62). Conclusions: Post testing counselling and making hepatitis C services affordable could help increase linkage to care among PWID in Georgia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".