Knowledge and barriers associated with assessment and treatment for hepatitis C virus infection among people who inject drugs
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Uptake of treatment for hepatitis C virus (HCV) infection among people who inject drugs is low. Further understanding is required of the relationship between HCV knowledge and treatment willingness, assessment and treatment in this population. DESIGN AND METHODS: A cross-sectional self-administered survey was conducted with clients of four opioid substitution therapy (OST) clinics and the Medically Supervised Injecting Centre in Sydney, Australia. RESULTS: Of 132 participants, 85 (64%) self-reported having HCV infection. HCV knowledge was mixed (mean 6.5, range 0-12) and was relatively lower on items measuring knowledge of factors impacting HCV-related disease progression. The likelihood of being in a higher knowledge category was associated with being female [adjusted odds ratio (AOR) = 3.78, 95% confidence interval (CI) (1.79, 7.98)], higher formal education [AOR = 3.28, 95% CI (1.57, 6.88)], being on a current OST program [AOR = 2.61, 95% CI (1.10, 6.19)] and being older [AOR = 1.04, 95% CI (1.01, 1.09)]. Participants receiving OST were more likely to report higher willingness to have HCV treatment [OR = 4.45, 95% CI (2.23, 8.17)]. Having been assessed for HCV treatment was associated with younger age [AOR = 0.93; CI 95% (0.88, 1.00)] and higher formal education [AOR = 7.81; 95% CI (1.62, 37.71)]. DISCUSSION AND CONCLUSIONS: Overall, knowledge scores were mid-range. Knowledge of modifiable factors influencing HCV-related liver disease progression was particularly low indicating the need for ongoing education. Education should also be targeted at older people and those not on OST, and be inclusive of those with lower literacy levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".