Evaluating Patient Experience with Integrated Virtual Care (IVC), a Hybrid Primary Care Model in Rural Ontario, Canada: A Cross-Sectional Survey
Bibliographic record
Abstract
INTRODUCTION: Canada faces a primary care crisis, especially in rural regions. In Ontario, the innovative, Integrated Virtual Care (IVC) program is a hybrid care model that enrolls patients with a family physician working predominantly remotely, while also embedded in a local Family Health Team, blending virtual and in-person care. OBJECTIVE: To evaluate the experience of patients enrolled in IVC. METHODS: We conducted a cross-sectional survey in a rural region in eastern Ontario, Canada. Participants included individuals enrolled in IVC for a minimum of 6 months. Primary outcome measures focused on patient experience with IVC, including satisfaction, access, self-reported health, and healthcare utilization. We also examined representativeness of survey respondents. RESULTS: 198 of 790 patients responded (response rate of 25.1%). Overall satisfaction was high, with 85% reporting being very satisfied or satisfied with IVC. Experiencing issues with virtual care was significantly associated with satisfaction. Survey respondents were generally older, Caucasian, and higher users of the healthcare system compared to a group of all those eligible to complete the survey. CONCLUSION: This study indicates high patient satisfaction with IVC among survey respondents. These insights can inform the expansion of innovative hybrid care models to meet the needs of underserved, rural populations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".