Virtual care Nova Scotia: An evaluation of a hybrid model of virtual primary care to inform scaling and sustainability considerations
Bibliographic record
Abstract
Objective The COVID-19 pandemic accelerated the use of virtual health care in Canada. Nova Scotia launched VirtualCareNS—a hybrid model integrating virtual and in-person primary care—to address access gaps. This rapid evaluation assesses its feasibility, preliminary economic outcomes, and stakeholder experiences. Methods A mixed-methods rapid evaluation design was employed, incorporating utilization and economic analyses. Surveys were completed by key informants, including users (N = 74,159), non-users (N = 3130), and implementation team members (N = 31). Interviews were conducted with providers (N = 8), implementation team members (N = 11), and platform users (N = 28). Results Over 101,000 virtual primary care visits were completed, with 76,054 unique profiles created. The cost per consultation was $123 (95% CI: $99–$149), and the net cost-savings per consultation was $85 (95% CI: $62–$111), primarily driven by reduced travel time and avoidance of emergency department and walk-in clinic visits. Patient satisfaction was high (91%), and providers reported improved access, especially for patients without a regular primary care provider. Conclusion Our rapid evaluation suggests that a hybrid model of virtual and in-person care can effectively address non-urgent care needs, generate cost savings, and improve access for both unattached and attached patients. VirtualCareNS benefits from dedicated leadership structures and integration with Nova Scotia's broader primary care services, positioning it as a scalable and sustainable approach to primary care delivery. Ongoing refinement—guided by user, provider, and implementation feedback—will be critical to realizing its full potential within the publicly funded health system.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".