Perspectives and Recommendations from Patients and Caregivers on the Role of Virtual Care in Ontario’s Primary Care System
Bibliographic record
Abstract
Context: Primary care quickly transition to virtual care due to the COVID-19 pandemic, resulting in practices shifting from in-person appointments to virtual appointments with limited preparation. There has been significant research regarding the experiences of delivering virtual care from the perspective providers, but there is less data regarding the experiences of patients and caregivers with the impact of virtual care. This study is the second phase of a multiyear study looking to learn more about the diverse perspectives from patients and caregivers across Ontario. Objective: To better understand patient and caregiver experiences with virtual care in the primary care context. Study Design: Mixed-methods study consisting of a provincial-wide survey and descriptive qualitative focus groups running in parallel. Eligible participants were patients and caregivers in Ontario who have had at least one virtual care appointment in the past 12 months. Results: We had 1,513 respondents complete the online survey and conducted 14 focus groups with 73 participants. Self-efficacy, as a dimension of virtual care had the highest experience score in contrast to whole person care which had the lowest experience score. Participants indicated that virtual care improves accessibility by saving time and money, accommodating for different populations, and making health care more approachable. Virtual care was seen as effective for specific types of appointments not requiring a physical presence like routine visits and prescription renewals. In-person care was still preferred for complex care situations and when the relationship with the provider was new or weak. Participants emphasized the value of continuing to offer virtual care as an option, as well as wanting robust patient portals to facilitate communication with providers and online appointment bookings. Recommendations for primary care clinics include creating best practice guidelines and developing standard processes, whereas broader systemic suggestions include building digital connectivity in remote areas, training providers to communicate effectively, and addressing shortage of providers. Conclusions: Virtual care remains an important option for patients and caregivers to increase access to primary health care in Ontario. Continued resourcing of virtual care is vital to address barriers to care for patients and caregivers from disadvantaged populations and rural communities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".