Patient and health care professional perspectives on using telehealth to deliver pulmonary rehabilitation
Bibliographic record
Abstract
The objective of this study was to identify the necessary features of pulmonary telerehabilitation (P-TR) from the perspectives of individuals living with chronic lung disease and health care professionals (HCPs) who deliver pulmonary rehabilitation (PR). Focus groups were carried out with patients ( n = 26) and HCPs ( n = 26) to elicit and explore their opinions about the critical elements of in-person PR and ideas for how these elements could be supported using technology. A questionnaire was used to assess technology use, PR experience, and general health status. Four key elements of PR were identified as critical to P-TR: the social aspect of PR; communicating with HCPs for education and support; using biosensors for monitoring and promoting self-knowledge; and the evolution of support with progress over time. A range of technology-enabled devices and programs were suggested as means to recreate aspects of these integral elements. Consultations with patients and HCPs suggest that users are interested in technology and want to ensure it recreates the important aspects of PR. Patients and HCPs identified similar key elements for P-TR. The opinions and suggestions of patients and HCPs should be the driving force of innovation if P-TR is to succeed in improving health outcomes.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".