Patient’s perspectives on a virtual physical prehabilitation program in lung transplant candidates: a qualitative study
Bibliographic record
Abstract
Background: A recent consensus statement recommends that a prehabilitation program be offered to solid organ transplant candidates. However, the optimal mode of delivery and the components are currently unknown. A recent study conducted at our center has shown that a virtual physical prehabilitation program can help lung transplant candidates improve or maintain their physical function while waiting for transplantation. Objective: To explore the views and perceptions of lung transplant candidates who participated in the prehabilitation program. Methods: We conducted 17 semi-structured individual interviews. They were recorded, and transcripts were generated using an online transcription tool and revised by a researcher who listened to all the interviews. The data were analyzed using the deductive thematic analysis method. Results: Five main themes emerged from the interviews: 1) Benefits: physical benefits of the program in preparing adequately for the transplant. 2) Program structure and design: The exercise program was complete and adapted, but participants would have liked to have live supervision for a longer period. 3) Safety and barriers: participants felt safe performing the exercises at home, even with their very advanced disease, and no major barriers were mentioned. 4) Support and external influences: support of their family and the therapist. 5) Development of skills and knowledge: they were able to develop skills and knowledge about exercises. Conclusion: Lung transplant candidates who participated in the virtual prehabilitation exercise program experienced benefits without encountering major barriers and felt supported throughout the program.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".