What are COVID-19 Patient Preferences for and Experiences with Virtual Care? Findings From a Scoping Review
Bibliographic record
Abstract
Virtual care became a routine method for healthcare delivery during the coronavirus disease 2019 (COVID-19) pandemic. Patient preferences are central to delivering patient-centered and high-quality care. The pandemic challenged healthcare organizations and providers to quickly deliver safe healthcare to COVID-19 patients. This resulted in varied implementation of virtual healthcare services. With an increased focus on remote COVID-19 monitoring, little research has examined patient experiences with virtual care. This scoping review examined patient experiences and preferences with virtual care among community-based self-isolating COVID-19 patients. We identified a paucity of literature related to patient experiences and preferences regarding virtual care. Few articles focused on patient experiences and preferences as a primary outcome. Our research suggests that (1) patients view virtual care positively and to be feasible to use; (2) patient access to technology impacts patient satisfaction and experiences; and (3) to enhance the patient experience, healthcare organizations and providers need to support patient use of technology and resolve technology-related issues. When planning virtual care modalities, purposeful consideration of patient experiences and preferences is needed to deliver quality patient-centered care.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".