Perceived barriers related to testing, management and treatment of HCV infection among physicians prescribing opioid agonist therapy: The C‐SCOPE Study
Bibliographic record
Abstract
The aim of this analysis was to evaluate perceived barriers related to HCV testing, management and treatment among physicians practicing in clinics offering opioid agonist treatment (OAT). C-SCOPE was a study consisting of a self-administered survey among physicians practicing at clinics providing OAT in Australia, Canada, Europe and the United States between April and May 2017. A 5-point Likert scale (1 = not a barrier, 3 = moderate barrier, 5 = extreme barrier) was used to measure responses to perceived barriers for HCV testing, evaluation and treatment across the domains of the health system, clinic and patient. Among the 203 physicians enrolled (40% USA, 45% Europe, 14% Australia/Canada), 21% were addiction medicine specialists, 29% psychiatrists and 69% were metro/urban. OAT physicians in this study reported poor access to on-site venepuncture (35%), point-of-care HCV testing (16%), and noninvasive liver disease assessment (25%). Only 30% of OAT physicians reported personally treating HCV infection. Major perceived health system barriers to HCV management included the lack of funding for noninvasive liver disease testing, long wait times to see an HCV specialist, lack of funding for new HCV therapies, and reimbursement restrictions based on drug/alcohol use. Major perceived clinic barriers included the lack of peer support programmes and/or HCV case managers to facilitate linkage to care, the need to refer people off-site for noninvasive liver disease staging, the lack of support for on-site phlebotomy and the lack of on-site delivery of HCV therapy. This study highlights several important modifiable barriers to enhance HCV testing, evaluation and treatment among PWID attending OAT clinics.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".