Understanding Preferences Toward Virtual Care: A Pre-COVID Mixed Methods Study Exploring the Perspectives of Patients with Chronic Liver Disease
Bibliographic record
Abstract
Background: Traditionally, outpatient visits for those with chronic liver disease (CLD) have been delivered in-person with the patient traveling to a centralized location to see the health care provider. The use of virtual care in health care delivery has been gaining popularity across a variety of patient populations, especially within the COVID-19 context. Performed before COVID-19, the aim of the present study was to explore the perspectives of patients with CLD toward the use of virtual care with their liver specialists. Methods: A cross-sectional, mixed methods study was used to conduct this work. Results: A total of 101 patients with CLD participated in this study. Participants had a mean age of 54.5 years (range 19–87 years). Quantitative analysis revealed that 86% were willing to attend a virtual visit with their liver specialist in the future. There was a significant relationship between both age and income level and acceptance of virtual care. The themes emerging from the qualitative analysis included: (1) past experiences attending in-person visits, (2) perspectives on the use of virtual visits, and (3) perceived challenges of virtual visits. Conclusions: Although there are many potential benefits of virtual care to both the patient and the health care system, there are instances (older age, low income level) when in-person care may be preferred by patients. A tailored approach that is mindful of the individual patient's health status, ease of access to technology, and preferences must be considered when offering virtual care. These findings are of particular relevance during COVID-19, an era that has forced us into the virtual space.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".