Patterns of practice and barriers to care for hepatitis C in the direct-acting antiviral (DAA) era: A national survey of Canadian infectious diseases physicians
Bibliographic record
Abstract
Background: Infectious diseases (ID) physicians are important for hepatitis C virus (HCV) care delivery in Canada. Our study describes their current and intended patterns of practice, attitudes, and barriers to care. Methods: The study population includes 372 practicing ID physicians who are members of the Association of Medical Microbiology and Infectious Disease (AMMI) Canada. A random sample from each province was invited to participate in a web-based survey. Our outcome of interest was level of HCV care provided, and related intentions for the next 12 months. Additional survey domains included attitudes toward treatment and perceived barriers to care. Results: Of 205 invitations to complete the survey, 64 (31%) physicians responded to the full survey and 81 to an abbreviated survey on the main outcomes of interest (overall response rate 71%). After adjusting for non-response, we estimate that 38% (95% CI 29% to 46%) are prescribing direct-acting antiviral (DAA) therapy, and 17% (95% CI 9% to 24%) are interested in starting to prescribe. Of full survey respondents, 100% of prescribers and 79% of non-prescribers agreed that people who inject drugs should be offered DAA therapy. Common barriers to care include patients' competing priorities, mental health comorbidities, poor access to harm reduction services, and insufficient physician training. Conclusions: A large proportion of Canadian ID physicians are not currently prescribing DAA therapy for HCV. While some of these physicians are interested in starting to prescribe, we need strategies to improve physician training and address other barriers to care as provincial restrictions on DAA eligibility are being eliminated.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".