When “virtual” works and when it doesn’t: A survey of physician and patient experiences with virtual care during the COVID-19 pandemic
Bibliographic record
Abstract
Objective: To assess the experience of virtual care among both patients and physicians across a range of clinical scenarios during the COVID-19 pandemic. Methods: A web-based survey was disseminated to patients and physicians through a variety of media and healthcare communications from May 2020 to July 2021. Demographic details and attitudes across a range of virtual care domains were collected. Quantitative responses were analyzed descriptively. Open-text responses were gathered to contrast when a virtual visit was superior or inferior to an in-person one, and a thematic content analysis was used. Results: There were 197 patients and 93 physician respondents, representing a range of demographic and practice characteristics. Patients noted several benefits of virtual care and felt it should continue to be available. Physicians felt they could do a lot of their care virtually. Common themes related to the superiority of virtual care were for "quick" visits, reviewing test results, chronic disease monitoring, and medication needs. Virtual care was less ideal when a physical exam was needed, and was not perceived as a good fit for an individual's cultural, language, or emotional needs. Certain conditions were identified as both ideal and non-ideal for the virtual format (e.g. mental healthcare). Discussion: Certain situations are more amenable to virtual care with personal preferences among both patients and physicians. Future priorities should ensure that virtual care is effective across the range of clinical situations in which it may be used and that both virtual and in-person options are equally available to those who want them.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".