A Phenomenological Inquiry of the Shift to Virtual Care Delivery: Insights from Front-Line Primary Care Providers
Bibliographic record
Abstract
The rapid deployment of virtual primary care visits served as a first-line response to COVID-19 and can now be examined for insights, particularly as virtual care is playing an ongoing role in patient care and consultations. Input from primary care providers directly responsible for virtual care delivery is needed to inform policies and strategies for quality care and interactions. The overarching goal of this research study was to examine the use of virtual care as a mechanism for primary healthcare delivery. A phenomenological approach investigated the shift in primary care service delivery as experienced by primary care providers and initiated during the COVID-19 pandemic. Focus groups were conducted with primary care providers (n = 21) recruited through email, advertisements, and professional organizations, exploring how virtual care was delivered, the benefits and challenges, workflow considerations, and recommendations for future use. Integrating virtual care was performed with a great deal of autonomy as well as responsibility, and overwhelmingly depended on the telephone. Technology, communication, and workflow flexibility are three key operational aspects of virtual care and its delivery. Providers highlighted cross-cutting themes related to the dynamics of virtual care including balancing risk for quality care, physician work/life balance, efficiency, and patient benefits. Primary care providers felt that virtual care options allowed increased flexibility to attend to the needs of patients and manage their practice workload, and a few scenarios were shared for when virtual care might be best suited. However, they also recognized the need to balance in-person and virtual visits, which may require guidelines that support navigating various levels of care. Overall, virtual care was considered a good addition to the whole 'care package' but continued development and refinement is an expectation for optimizing and sustaining 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".