Enhancing accessibility and equity in utilization of virtual care: Virtual Care @ Your Library pilot project
Bibliographic record
Abstract
BACKGROUND: The post-pandemic expansion of virtual care in Nova Scotia aimed to improve access for patients without primary care providers. Virtual Care Nova Scotia, launched in 2021, and increased access, but equitable reach remained a concern. The Virtual Care @ Your Library (VC@YL) initiative addressed this gap by offering virtual healthcare access through public libraries in collaboration with government and health organizations. METHODS: This descriptive observational study applied the RE-AIM framework. Reach was assessed by the number of participants. Effectiveness was evaluated through service utilization and satisfaction. Adoption examined staff burden and role integration. Implementation fidelity and access barriers were documented. Maintenance was assessed via cost analysis and potential savings from avoiding emergency department (ED) and walk-in clinic visits under different utilization and cost scenarios. RESULTS: VC@YL engaged 518 unique users across 1,073 visits. Most users were aged 65+ (64.2%), citing technological barriers (75.4%) and support needs (77.6%) as primary reasons for use. All users successfully completed virtual care appointments, with 98% reporting positive experiences. Among library staff, 83% felt well-supported, and 65% of patron interactions required less than 15 min. Digital literacy assistance was the most common service (75.4%). The total project cost for VC@YL was $93,061, incorporating both one-time implementation and recurring staff costs. The cost per VC@YL utilization was $87. Avoided ED visits resulted in net savings of up to $63,614, though higher virtual care costs reduced savings in certain scenarios. Walk-in clinic diversions yielded negative cost savings due to the cost structure. Total savings ranged from $15,708 to $61,541, with per-person savings from $30 to $57, depending on virtual care consultation costs and utilization levels. CONCLUSIONS: The VC@YL initiative demonstrated how community-based programs can effectively enhance access to virtual care, particularly for individuals facing technological barriers. This pilot project showed strong potential for improving healthcare access through practical support and leveraging existing community infrastructure. Its scalability and cost-effectiveness make it a promising model for broader implementation in similar settings.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".