“There’s nothing like a good crisis for innovation”: a qualitative study of family physicians’ experiences with virtual care during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Prior to the pandemic, Canada lagged behind other Organisation for Economic Cooperation and Development countries in the uptake of virtual care. The onset of COVID-19, however, resulted in a near-universal shift to virtual primary care to minimise exposure risks. As jurisdictions enter a pandemic recovery phase, the balance between virtual and in-person visits is reverting, though it is unlikely to return to pre-pandemic levels. Our objective was to explore Canadian family physicians' perspectives on the rapid move to virtual care during the COVID-19 pandemic, to inform both future pandemic planning for primary care and the optimal integration of virtual care into the broader primary care context beyond the pandemic. METHODS: We conducted semi-structured interviews with 68 family physicians from four regions in Canada between October 2020 and June 2021. We used a purposeful, maximum variation sampling approach, continuing recruitment in each region until we reached saturation. Interviews with family physicians explored their roles and experiences during the pandemic, and the facilitators and barriers they encountered in continuing to support their patients through the pandemic. Interviews were audio-recorded, transcribed, and thematically analysed for recurrent themes. RESULTS: We identified three prominent themes throughout participants' reflections on implementing virtual care: implementation and evolution of virtual modalities during the pandemic; facilitators and barriers to implementing virtual care; and virtual care in the future. While some family physicians had prior experience conducting remote assessments, most had to implement and adapt to virtual care abruptly as provinces limited in-person visits to essential and urgent care. As the pandemic progressed, initial forays into video-based consultations were frequently replaced by phone-based visits, while physicians also rebalanced the ratio of virtual to in-person visits. Medical record systems with integrated capacity for virtual visits, billing codes, supportive clinic teams, and longitudinal relationships with patients were facilitators in this rapid transition for family physicians, while the absence of these factors often posed barriers. CONCLUSION: Despite varied experiences and preferences related to virtual primary care, physicians felt that virtual visits should continue to be available beyond the pandemic but require clearer regulation and guidelines for its appropriate future use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".