Patient Experiences With Virtual Care During the COVID-19 Pandemic: Phenomenological Focus Group Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Virtual care has expanded during the COVID-19 pandemic and enabled greater access and continuity of care for many patients. From a patient-oriented research perspective, understanding the patient experience with virtual care appointments is an important first step in identifying ways to better support patient use and satisfaction. OBJECTIVE The purpose of this qualitative study was (1) to explore patients’ experiences and perspectives with the adoption and use of virtual care during COVID-19 in Newfoundland and Labrador, Canada, and (2) identify the education and informational needs of patients to inform future strategies for supporting patient use of virtual care. METHODS Using a phenomenological approach, we conducted a focus group interview with a purposive sample of patient representatives representing a cross-section of the population of the province of Newfoundland and Labrador. Five patient representatives were recruited from the Newfoundland and Labrador Support Patient Advisory Council and participated in the focus group. The focus group was conducted in February 2022 via videoconferencing technology. Using thematic analysis, we identified several recurrent themes that described respondents’ experiences with the use of virtual care during COVID-19, as well as their perceptions of education and informational needs to support more effective patient use of virtual care. RESULTS Respondents felt that virtual care is a beneficial addition to the health care system, enabling greater convenience and access to health care services. Key barriers and challenges in adopting and using virtual care appear to primarily arise from patients’ lack of knowledge, understanding, and familiarity with respect to virtual care. Cost, technological access, connectivity, and low digital literacy were challenges for some patients, particularly in rural communities and among older patient population. Patient education and support were critical and needed to be inclusive, easy to understand, and include information regarding privacy, security, consent, and the technology itself. The types of patient education experiences regarded as most helpful included peer support and knowledge sharing among patients themselves. CONCLUSIONS Beyond the COVID-19 pandemic, virtual care will have a continuing role in enhancing the continuity of care for patients through more convenient access. The education and informational needs of patients are important considerations in promoting the adoption and use of virtual care. Key education and informational needs and strategies were identified to enable and empower patients with the knowledge, digital literacy skills, and support to effectively use virtual care. CLINICALTRIAL
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".