Has Virtual Care Arrived? A Survey of Rural Canadian Providers During the Early Stages of the COVID-19 Pandemic
Bibliographic record
Abstract
We investigated the uptake and perceptions of virtual care solutions by rural Canadian primary and specialist providers during the early phase (May-June 2020) of the COVID-19 pandemic. A web-based, cross-sectional survey of rural primary and specialty care providers examined types of virtual care platforms used (eg, phone, video), appointment length, experience and satisfaction with the solution used, plans for future use of virtual care, and patients' use of virtual care services. Targeted participants were actively-practicing providers in rural Western Canada who were emailed an invitation for the study and its survey link. Fifty-nine providers (26% response rate) completed the survey. During the pandemic, 78% of providers reported using virtual care for more than 60% of their appointments, while only 3% did so frequently pre-pandemic. Most providers used phone consultations, despite believing that video provided a better virtual visit. Key barriers included workflow interruptions, unique concerns about quality of care, remuneration and sustainability, or poor internet access and bandwidth for both providers and patients. The key opportunity noted was improved access to care. While most virtual care visits were not conducted using video technologies, overall virtual care resulted in high provider satisfaction, while not increasing workload. Virtual care will continue to play an important role in future rural care practice; however, sustainability will require both provider-level and system-level changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".