Implementation of virtual pulmonary rehabilitation during the COVID-19 pandemic: Experiences and perceptions of patients and healthcare providers
Bibliographic record
Abstract
BACKGROUND: Pulmonary rehabilitation (PR) plays an important role in the management of symptomatic patients with chronic respiratory diseases (CRD). While studies have investigated the feasibility and efficacy of virtual PR (VPR), it is important to understand the experiences of patients and healthcare providers (HCPs) during the rapid digital health transformation that occurred in the COVID-19 pandemic. OBJECTIVES: To explore the experiences and perspectives of patients and HCPs who participated in VPR during the pandemic. METHODS: Semi-structured interviews were conducted with CRD patients and HCPs. This study used a qualitative descriptive approach and a team-based inductive thematic analysis. RESULTS: Participants included 11 HCPs (7 female; 29-55 years) and 19 CRD patients (11 male; 62-83 years; 15 COPD, 4 COPD/ILD). Three major themes and 10 subthemes were identified: i) the pandemic response: a 'trial by fire' (navigating uncertainty, emotional impact of change, shifting practice amid complexity); ii) beyond the emergency: navigating a 'new normal' (eligibility and assessment for VPR, virtual exercise, virtual education and resources, clinical supervision and patient safety); and iii) care beyond boundaries: the implications of using technology for PR (benefits and limitations of technology, psychosocial implications, VPR in the future). CONCLUSION: The pivot to VPR was acknowledged as positive by both patients and HCPs although both groups were mindful of the implementation challenges. These findings provide insight into the experience of HCPs and patients in introducing VPR in response to the pandemic and will inform future implementation of VPR for individuals with CRD.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".