Evaluation of a Virtual Care Program for Unattached Patients in Southeastern Ontario
Bibliographic record
Abstract
Access to primary care is critical for delivering preventive and coordinated healthcare services. Individuals lacking a primary care provider often have increased healthcare needs and associated costs, utilizing the emergency department for routine medical needs. The implementation of virtual and hybrid care addresses access issues and reduces high-cost healthcare utilization for unattached patients. The studies in this dissertation have demonstrated the efficacy of virtual care programs in supporting these populations. A quantitative study assessing the budget impact of the virtual/hybrid care program for 872 unattached patients in Ontario found that the delivery of virtual/hybrid care for unattached patients could reduce emergency department visits and inpatient hospitalizations by 21% and 27.7%, respectively. Furthermore, cost savings were associated with the implementation when modeled over 5 years. Qualitative analysis involving focus groups and interviews with 31 stakeholders, including patients, healthcare providers, decision-makers, and administrators, revealed that participants reported a high level of satisfaction, largely attributing this to the convenience and perceived quality of care provided. Patients expressed a preference for a hybrid model encompassing both virtual and in-person consultations. From the provider’s perspective, there were no discernible barriers related to the adoption of technology; however, a consensus emerged advocating for enhanced administrative support to foster operational efficiency. To further validate these findings, a mixed-methods analysis was employed. Utilizing a convergent mixed methods design, this investigation combined quantitative analysis of healthcare costs and utilization metrics, yielding confirmatory and expansionary results. Specifically, the value of virtual/hybrid care cannot be evaluated on cost alone, and the complexity of the unattached population cannot be overlooked. Overall, the findings advocate for the feasibility of implementing similar programs within existing healthcare frameworks, highlighting the cost and quality benefits of virtual and hybrid care modalities for unattached patients.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".