Hepatitis C patient communication source and modality preferences in the direct-acting antiviral era
Bibliographic record
Abstract
Background: Although hepatitis C virus (HCV) treatment has improved dramatically, decision making related to treatment continues to be complex and challenging. Little data exists regarding patient information needs and preferences in the direct-acting antiviral (DAA) era. Methods: We evaluated patient-perceived information needs and preferences when making HCV treatment decisions. A cross-sectional survey was conducted at two Ottawa-based sites: a hospital-located outpatient viral hepatitis clinic, and a community-based HCV patient support program. Results: One hundred and seventeen patients completed the survey: the mean age was 52.1 years (range 23 to 78), and 64% were male, 81.5% were White, 48.6% were on disability support or leave, and 60.3% had a high school education or less. Although traditional sources of health information (e.g., direct communication with health care providers) remain preferred by most, a range of preferences were reported including utilization of newer technologies (e.g., emails, text messages). The telephone was rated as the preferred method of contact for medication reminders, with daily or weekly communication reported as most helpful. White participants, those more highly educated, and those with a higher income all indicated a greater acceptability for e-mail and/or text message communication for medication and appointment reminders. Conclusions: There is no single preferred source or method of communicating with patients. These findings indicate that a tailored multi-pronged approach, including newer technologies, is more likely to effectively educate and communicate with the heterogeneous population of individuals living with HCV.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".