Implementation of virtual primary care: a comparative study of family medicine residents’ experiences
Bibliographic record
Abstract
Background: Virtual care (VC) was rapidly adopted during the COVID-19 pandemic to ensure continuity of primary care. In this study, we explored Family Medicine (FM) residents’ evolving experiences with VC across early (2020), late (2022), and post-pandemic (2024) phases in Saskatchewan, focusing on satisfaction, preparedness, supervision, and perceived impact on training and well-being. Methods: FM residents across eight distributed sites were surveyed at three time points using a standardized tool. Responses were analyzed using chi-square, Kruskal-Wallis, and post hoc Mann-Whitney U tests (p < 0.05). Results: Seventy-eight residents participated (2020: n = 26; 2022: n = 19; 2024: n = 33). Satisfaction with VC tended to decline over time (p = 0.074), while requests for additional VC training did not change (p = 0.269). Confidence to use VC post-residency dropped significantly from 100% (2020) to 60.6% (2024; p < 0.001), despite a consistent and moderate amount of supervision. The negative impact of COVID-19 on training declined by 2024 (p = 0.008), while trust in the provincial response to the COVID-19 pandemic also decreased (p < 0.0001). Conclusions: Although FM residents adapted to VC during the pandemic, long-term sustainability to use VC requires improved training, structured supervision, and curricular integration. Embedding VC competencies into postgraduate education is essential to support hybrid models of care in the evolving primary care landscape.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".