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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".