Safeguarding Compassion in Virtual Family Physician Care
Bibliographic record
Abstract
INTRODUCTION: Following the COVID-19 pandemic, the role of virtual family medicine care is evolving. It can be tempting to consider only the technological aspects of virtual care; we argue we must attend to compassion's essential role in virtual family medicine care. This research aimed to understand the components contributing to compassionate family medicine virtual care and how these were demonstrated. METHODS: We conducted a qualitative Constructivist Grounded Theory study with 2 components; individual interviews with patients and family physicians (FP), and Collaborative Discussions, informed by the interviews, that brought patients and FPs together. Data collection and analysis were iterative using a constant comparative analysis. RESULTS: We recruited nineteen patient and fourteen FP participants for the first component and 6 patient and 4 FP participants for the second. We identified 4 themes: Conveying virtual compassion through actions; External factors affecting virtual compassion; Virtual visits extending compassionate care; and Role of the patient-FP relationship. These themes can be characterized as a stance that FPs assume in their practice of virtual care. DISCUSSION: We highlight 4 themes important to the delivery of compassionate virtual care. We provide specific actions FPs may consider in delivering virtual care. Offering virtual visits was viewed as a compassionate bridge between in-person visits. CONCLUSION: Our findings support that it is possible to convey compassion in virtual visits including telephone interactions. As virtual care evolves, our findings can support patients and family physicians to safeguard compassion so that it remains a hallmark of care for all modes of delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".