Exploring Patient Advisors’ Perceptions of Virtual Care Across Canada: Qualitative Phenomenological Study
Bibliographic record
Abstract
BACKGROUND: While virtual care services existed prior to the emergence of COVID-19, the pandemic catalyzed a rapid transition from in-person to virtual care service delivery across the Canadian health care system. Virtual care includes synchronous or asynchronous delivery of health care services through video visits, telephone visits, or secure messaging. Patient advisors are people with patient and caregiving experiences who collaborate within the health care system to share insights and experiences in order to improve health care. OBJECTIVE: This study aimed to understand patient advisors' perceptions related to virtual care and potential impacts on health care quality. METHODS: We adopted a phenomenological approach, whereby we interviewed 20 participants who were patient advisors across Canada using a semistructured interview protocol. The protocol was developed by content experts and medical education researchers. The interviews were audio-recorded, transcribed verbatim, and analyzed thematically. Data collection stopped once thematic saturation was reached. The study was conducted at Queen's University, Kingston, Ontario. We recruited 20 participants from 5 Canadian provinces (17 female participants and 3 male participants). RESULTS: Six themes were identified: (1) characteristics of effective health care, (2) experiences with virtual care, (3) modality preferences, (4) involvement of others, (5) risks associated with virtual care encounters, and (6) vulnerable populations. Participants reported that high-quality health care included building relationships and treating patients holistically. In general, participants described positive experiences with virtual care during the pandemic, including greater efficiency, increased accessibility, and that virtual care was less stressful and more patient centered. Participants comparing virtual care with in-person care reported that time, scheduling, and content of interactions were similar across modalities. However, participants also shared the perception that certain modalities were more appropriate for specific clinical encounters (eg, prescription renewals and follow-up appointments). Perspectives related to the involvement of family members and medical trainees were positive. Potential risks included miscommunication, privacy concerns, and inaccurate patient assessments. All participants agreed that stakeholders should be proactive in applying strategies to support vulnerable patients. Participants also recommended education for patients and providers to improve virtual care delivery. CONCLUSIONS: Participant-reported experiences of virtual care encounters were relatively positive. Future work could focus on delivering training and resources for providers and patients. While initial experiences are positive, there is a need for ongoing stakeholder engagement and evaluation to improve patient and caregiver experiences with virtual 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 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".