Providing compassionate care in a virtual context: Qualitative exploration of Canadian primary care nurses’ experiences
Bibliographic record
Abstract
Objective: Virtual care presents a promising opportunity to create new communication channels and increase access to healthcare. However, concerns have been raised around the potential for unintended emotional distances created through virtual care environments that could strain patient-provider relationships. While compassionate care is an enabler of emotional connectivity and a core tenant of nursing, little is known about whether or how nurses have adapted their compassion skills into virtual interactions. These concerns are particularly relevant in primary care, where there is a focus on relational continuity (i.e. relationship-based, longitudinal care) and a broad uptake of virtual care. The aim of this study was to explore the meaning of compassionate virtual care and to uncover how nurses operationalized compassionate care through virtual interactions in primary care. Methods: We used a qualitative interpretive descriptive lens to conduct semistructured interviews with primary care nurses (Ontario, Canada) who had provided virtual care (i.e. video visits, remote patient monitoring, or asynchronous messaging). We used a thematic approach to analyze the data. Results: We interviewed 18 nurse practitioners and two registered nurses. Participants described how: (1) compassionate care was central to nursing practice, (2) compassionate care was evolving through virtual nurse-patient interaction, and (3) nurses balanced practice with patients' expectations while providing virtual compassionate care. Conclusions: There is an opportunity to better align nurses' understanding and operationalization of compassionate care in virtual primary care contexts. Exploring how compassionate care is operationalized in primary care settings is a necessary first step to building compassionate competencies across the nursing profession to support the continued virtual evolution of health service delivery.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".