Patient and provider experiences with virtual care during the COVID-19 pandemic: A mixed methods study
Bibliographic record
Abstract
The COVID-19 pandemic prompted the rapid uptake of Virtual Care (VC). Positive patient outcomes with VC are previously reported but little is known about the experiences of patients and providers using VC during the pandemic. We aimed to describe patient and primary care provider experiences, satisfaction, perceptions, and attitudes to VC during the COVID-19 pandemic that might explain adoption of VC across the continuum of care and inform sustained uptake. We conducted a sequential explanatory mixed methods study using online surveys and virtual interviews with a convenience sample of primary care providers and patients in a Canadian province (July – December 2020). Eligible participants included patients and primary care providers using VC during the COVID-19 pandemic. Survey responses and interviews were analyzed using descriptive statistics and thematic analysis, respectively. Overall satisfaction was compared using the Mann-Whitney U test. Eighty-five patients and 94 primary care providers responded to the surveys. Patients reported higher overall satisfaction with VC than primary care providers (median [interquartile range]: 4.4 [4.0-4.7] and 3.7 [3.4-3.9] p < 0.001). Ten patients and 11 primary care providers were interviewed. Both groups strongly appreciated VC’s increased access and convenience, identified the lack of compensation as a pre-pandemic barrier to providing VC, and reported willingness to continue VC post-COVID-19 pandemic. The COVID-19 pandemic provided an opportunity for patients and primary care providers to rapidly adopt VC with high satisfaction. Patients and primary care providers viewed VC positively due to its convenience and accessibility; both intend to continue using VC post-pandemic. Experience Framework This article is associated with the Staff & Provider Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".