Improving Health Care Access: A Cross-Provincial Evaluation of a Virtual Consultation Platform in Prince Edward Island
Bibliographic record
Abstract
Introduction: Access to specialist care remains a major challenge in rural and remote areas of Canada. Virtual Hallway is a secure digital platform that enables timely, provider-to-provider telephone consultations between primary care providers (PCPs) and specialists. This study evaluates satisfaction and effectiveness of the platform in Prince Edward Island (PEI), a small, rural province, across two time points to assess its sustained impact and usage patterns. Methods: A cross-sectional survey was conducted during two periods in 2024. PCPs were invited to complete postconsultation surveys, which assessed user satisfaction, in-person referral avoidance, and motivations for specialist selection. Descriptive statistics and chi-square tests were used for analysis. Results: Survey data included 181 postconsultation responses. Referral avoidance remained high (85% and 75% at time points 1 and 2, respectively). User satisfaction was consistently high among both PCPs (98%–100%) and specialists (92%–98%). PCPs reported improved care quality even when referrals were not avoided. Over time, specialist selection shifted from local availability to unmet specialty access within PEI ( p = 0.03), suggesting increased use for complex cases. Conclusion: The Virtual Hallway platform demonstrated sustained effectiveness in improving access to specialist care and reducing in-person referrals in a rural province. These findings support broader implementation of virtual peer-to-peer consultation platforms to address access disparities across Canada.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".