Patient and Caregiver Experiences with Synchronous and Asynchronous Virtual Care in Primary Care across Ontario, Canada: 2022-2023
Bibliographic record
Abstract
Synchronous virtual care delivery in Ontario, employing telephone and video modalities is common. 1 Asynchronous modalities such as secure electronic messaging are available at a smaller scale 2 .Compared to the previous years, virtual care in Ontario now emphasizes recovery from COVID-19, and an expansion of virtual care to create models of integrated care 3 .In the fall of 2022, Ontario changed its virtual billing code structure, adopting a remuneration approach that restricts the eligibility of the patients for whom full virtual care fees can be applied 1 .This study is an extension of a previous study conducted in 2021 which principally assessed patient experience with the newly introduced synchronous modalities 4 .That study demonstrated overall, positive patient experiences and a high acceptability patients had with virtual care 4 .In this study, conducted in the fall of 2022, we expand on the previous findings further, and assess the patient experience with virtual care in comparison to the face-face-modality.In addition, as little is known about caregiver perspectives on virtual care with respect to the care that their care recipient receives, we sought to understand their experiences with virtual care in this study.➢ Please note: For brevity, the term "loved one" is used in this report to refer to the care recipient of individuals on whom the caregivers are reporting. OBJECTIVESThis study examines patient and caregiver experiences with virtual care through 6 dimensions: Communication, Self-Efficacy, Patient-Provider Relationship, Quality of Care, Whole-Person-Care, and Privacy and Confidentiality as well as the current use and expectation for asynchronous care in primary care in Ontario and make recommendations to inform its broader adoption.Dimensions Communication 535 12 (8, 15) 456 13 (10, 17) 164 11 (5, 16) 149 20 (13, 26) Quality of Care 536 8 (5, 10) 455 14 (11, 16) 164 9 (4, 15) 149 17 (10, 23) Self-Efficacy 532 14 (11, 17) 454 16 (13, 19) 164 12 (6, 17) 148 20 (14, 26) Whole Person Care 532 3 (-1, 6) 455 15 (12, 19) 164 11 (4, 18) 148 17 (10, 24) Patient/Provider Relationship 531 8 (5, 11) 453 15 (11, 19) 164 12 (6, 18) 148 18 (11, 24) Privacy and Confidentiality 531 8 (4, 11) 453 14 (11, 18) 164 10 (4, 15) 148 17 (11, 23) Overall 536 9 (6, 11) 456 14 (12, 17) 164 11 (6, 15) 149 18 (12, 23)Figure 1: Mean Scores per Dimension for Questions Comparing Virtual Appointments to In-Person Appointments.In the graphs above, scores that go towards +in person are in favor of in-person appointments over the appointment modality, and points that go towards +virtual, are in favor of the virtual modality over in-person appointments.A score of "0" would represent no difference, while positive score would reflect better experience with the virtual appointments compared to in person.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".