Exploring Compassionate Care in Virtual Rehabilitation: Qualitative Study
Bibliographic record
Abstract
Background: Virtually delivered health care services can offer numerous benefits, and the demand for virtual care continues to grow among subgroups facing mobility challenges. The experience of compassion in health care is linked to patient satisfaction and clinical outcomes; however, this link in virtual rehabilitation settings is underexplored. Objective: The objectives of this study were to explore what compassionate care means to rehabilitation patients in a virtual rehabilitation context and explore patients' experiences of how the technology associated with virtual rehabilitation impacted their experience of care. Methods: We conducted one-on-one semistructured qualitative interviews with patients with limb loss and chronic obstructive pulmonary disease. A reflexive thematic analysis approach was used to generate domain summaries and initial themes across the sample. Themes were generated following analytic work over a series of discussions within the research team. Results: Sixteen interviews were conducted. Four themes illustrating participants' perceptions of compassionate care were generated: (1) features of compassionate care include feeling valued, connected, and cared for by the health care provider; (2) threats to compassionate care in virtual rehabilitation; (3) facilitating compassion in virtual rehabilitation through preparation; and (4) benefits of virtual care. Conclusions: Patient perceptions of compassionate care in a virtual rehabilitation setting may be impacted by the behaviors and communication of providers. Provider training and preparation and the personal connections formed with their patients may impact compassionate care experiences.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| 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".