Family Physicians’ experiences with an innovative, community-based, hybrid model of in-person and virtual care: A mixed-methods study
Bibliographic record
Abstract
Abstract Introduction: Rural, remote, and underserved communities have often struggled to provide adequate access to family physicians. To bridge this gap in Renfrew County, a large, rural region in Ontario, Canada, a community-based, hybrid care model was implemented, combining virtual care from family physicians and in-person care from community paramedics. Studies have demonstrated the clinical and cost-effectiveness of this model but its acceptability to providers has not been examined. This study investigates the experiences of participating family physicians. Methods: A mixed-methods study, combining physician questionnaire response data and qualitative thematic analysis of focus group interview data. Results: Data was collected from n=17 survey respondents and n=9 participants in two semi-structured focus groups (n=4 and n=5 respectively). Physicians reported high satisfaction, driven by skills development and patient gratitude, and felt empowered to reduce ED visits, care for unattached patients, and address simple medical needs. However, physicians found it difficult to provide continuous care and were sometimes unfamiliar with local healthcare resources. Discussion: This study found that a hybrid model of in-person and virtual care from family physicians and community paramedics was associated with positive physician experiences in two main areas: clinical impacts, especially avoiding unnecessary ED visits, and physician satisfaction with the service. Potential improvements for this hybrid model were identified, and include better support for patients with complex needs, and more information about local health-system services. Our findings should be of interest to policymakers and administrators seeking to improve access to care through a hybrid model of in-person and 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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".