Choosing between virtual and in-person family physician care: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Virtual care accelerated to the forefront of family physician (FP) care following the COVID-19 pandemic and continues to play a significant role in patient care. The choice between virtual and in-person primary care must be sensitive to patients' contexts particularly for those with multi-morbidity. OBJECTIVES: This study explored how to make the choice between virtual and in-person FP care for persons living with multi-morbidity that is acceptable to patients and FPs. METHODS: We conducted a constructivist grounded theory study to understand the processes patients and FPs employ when deciding on the mode of primary care delivery. We used individual interviews to understand the perspectives and expectations of patients with multi-morbidity (2+ chronic conditions) and FPs. RESULTS: There were two main themes revealed in data analysis: Considerations in choosing mode of delivery (including reason for visit, impact on access, technological logistics, and reimbursement for virtual care) and Process for choosing mode of delivery (including endorsing the patient choice when possible and scheduling visits). CONCLUSION: This paper integrated the experience of both patients and FPs to understand how to make the choice between virtual and in-person care. This understanding can support the future of FP care where diverse modes of delivery are employed, but currently technological barriers remain. Clinical scheduling systems that depend on telephone interactions between clinic staff and patients do not always support the process patients and FPs indicated they prefer; that is, one that respects patient preference and FP clinical expertise.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".