Virtual and In-Person Delivery of Primary Care and the Effect on Compassion
Bibliographic record
Abstract
Context: During the pandemic, Family Physicians (FPs) moved rapidly to virtual visits, uncertain how compassionate care could be delivered. As we consider the future role of virtual care, we must ensure compassion remains at the centre of patient-FP relationships. Objective: To co-create with patients and FPs a framework for virtual and in-person interactions that inspires and safeguards compassion. Study Design and Analysis: Constructivist Grounded Theory (CGT) study using semi-structured interviews to explore how participants received or provided compassion during virtual interactions. Interviews were audio-recorded and transcribed verbatim. CGT principles of iteration and theoretical sampling were applied and interviews were conducted until data sufficiency was reached. Data collection and analysis was iterative using constant comparative analysis with three coding phases (line-by-line, focused and theoretical). Setting: Province of Ontario, Canada. Population Studied: We interviewed patients with multimorbidity (n=18) who had at least two virtual visits and FPs who had provided virtual care (n=14). Participants were selected for maximum variation concerning age, gender, urban/rural, and for FPs, practice models. Outcome Measures: Patient and FP experiences with virtual care. Results: Our findings highlighted four main themes. Participants identified the importance of actions to convey compassion including attentive listening and spending time; importantly patients talked about the need to be understood and FPs described the importance of being fully present and intentional. The second theme was how personal and external factors could influence compassionate care; especially for FPs this included distraction and fatigue. The patient-FP relationship, the third theme, was perceived by patients and FPs as the bedrock for compassionate care which was exemplified by trust and continuity. The final theme was the ability through virtual care to extend the provision of compassionate care, especially alleviating anxiety and suffering. Conclusions: These findings are informing upcoming collaborative discussions between patients and family physicians with the aim of developing a framework of virtual family physician care. These qualitative findings will inform future research and education interventions for FPs and residents in Family Medicine, aimed at creating dialogue to optimize benefits and mitigate threats of virtual modalities in compassionate FP care.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".