Family perspectives on virtual care during the COVID19 pandemic and beyond
Bibliographic record
Abstract
Context: Early in the COVID-19 pandemic, Canadian primary care practices rapidly adapted to provide care virtually to minimize exposure and transmission risks for themselves and their patients. Most family physicians (FP’s) lacked prior training or expertise with virtual care. In the absence of formalized guidance, FP’s made individual decisions about in-person vs. remote care based on clinical judgement, their longitudinal relationships with their patients, and personal risk assessments. Objective: To explore Canadian FP’s perspectives on the strengths and limitations of virtual care approaches for their patient populations during and beyond the COVID-19 pandemic, in order to inform the integration of virtual visits into broader primary care practice. Study design and analysis: A qualitative, multiple case study including jurisdiction-specific COVID chronologies and qualitative interviews with FPs. We analysed interview data using a structured applied thematic approach. Setting: Four Canadian jurisdictions (Vancouver Coastal Health region, British Columbia; Southwestern Ontario; the province of Nova Scotia; and Eastern Health region of Newfoundland and Labrador). Population studied: FPs practicing in Canada during the COVID-19 pandemic. Instrument: Semi-structured exploratory qualitative interviews. Outcome measures: Perspectives from FPs on the possibilities, challenges, necessary supports, and future directions for virtual care. Results: We interviewed 68 FPs across the four study regions. We identified five virtual-care related themes during our analysis: 1) changes in access to care; 2) changes in care quality and patient experience; 3) patient perceptions of virtual visits; 4) striking a balance between in-person care and modes of virtual care (e.g. telephone versus video visits); and 5) sustaining and improving virtual care moving forward. Conclusion: The move to virtual visits enhanced access to care for some patients and was helpful for FPs to better manage their panels. However, virtual care also created access challenges for some patients (e.g. people who are underhoused or who live in areas without good phone or internet access) and for some types of care (e.g. care that requires access to medical devices). FPs were positive about the ongoing integration of virtual care into broader primary care delivery, but guidance and regulation are needed to ensure equitable access and maximize quality of care.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".