Creating a Model for Choosing between Virtual and In-Person Family Physician Visits
Bibliographic record
Abstract
Context Following the COVID-19 pandemic, virtual care accelerated to the forefront of family physician (FP) care and continues to be used by FPs and their patients to a less or greater extent. The path forward for virtual care is evolving but must be informed by principles of equity and patient-centredness. Objective To explore how to make the choice between virtual and in-person FP care for persons with multimorbidity that is acceptable to both patients and family physicians. Study Design and Analysis We conducted a Constructivist Grounded Theory study to understand the processes patients and FPs employ when deciding on the mode of primary care delivery, including both virtual and in-person care. We used individual interviews to understand the perspectives and experiences of patients and FPs. Data analysis was iterative using constant comparative analysis. Setting or Dataset Conducted in Ontario, Canada. Population Studied We recruited patients who self-identified as having multimorbidity (at least two chronic conditions) and had received virtual care from their FP at least twice and FPs who had provided virtual care. Intervention/Instrument N/A Outcome Measures Patient and FP experiences with virtual care. Results There were two main themes revealed in the analysis of the data with corresponding subthemes: (1) Considerations in choosing mode of delivery and (2) Process for choosing mode of delivery. Theme 1 highlighted the considerations that need to be addressed to ensure the mode of delivery is appropriate for the visit including the reason for visit, impact on access, technological logistics and clinical practice organization. Moving from considerations to the process, Theme 2 focuses on endorsing the patient’s choice of modality and the process for scheduling visits. Conclusions The findings from our research highlight the overall process involved when choosing virtual and in-person primary care. Both patients and FPs emphasized several considerations that need to be addressed when deciding between virtual and in-person care. Participants emphasized the importance of patient choice in the process but that this is not always appropriate without considering the clinical context of the visit. The process for choosing the mode of delivery should be embedded within the patient-FP relationship.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".