Virtual Healthcare in Rural and Remote Settings: A Qualitative Study of Canadian Rural Family Physicians’ Experiences during the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: This paper aims to explore the experiences of rural family physicians using virtual healthcare in their clinical practice during the COVID-19 pandemic in Canada. DESIGN: A community-based participatory approach. SETTING: Rural and remote communities in Canada. PARTICIPANTS: Thirteen rural family physicians with at least one year of clinical experience. RESULTS: The data illustrate significant issues associated with virtual healthcare in rural healthcare. The adoption of virtual healthcare has been expressed to pose a harsh polarity; the benefit conferred to rural family physicians with the opportunity to have flexible working hours and work at home while interacting with family members is starkly contrasted with the struggles of insufficient financial support to facilitate setting up virtual healthcare for rural physicians, unreliable technological infrastructure, and inadequate technological resources, which are all exacerbated by the lack of adequate health literacy in rural communities. Results were compiled into five major categories underpinning the lived experiences of rural family physicians: 1-potential trade-off between convenience and quality of care; 2-work-family boundaries; 3-patient-doctor communication; 4-technology as barrier or enabler; 5-increased call duration. CONCLUSION: The differing trends assessed in the findings illustrate the complications faced in providing virtual healthcare, which resonates with the experiences and views of rural physicians. The findings of this study may guide the development of tailored technologies that adjust for the complexity of administering virtual healthcare in rural communities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".