Patient Experiences of a Telehealth Multidisciplinary Cancer Rehabilitation Programme: Qualitative Findings from the ReStOre@Home Feasibility Study
Bibliographic record
Abstract
Purpose . Multidisciplinary rehabilitation programmes providing exercise, nutrition support, education, and peer support can effectively meet the rehabilitation needs of upper gastrointestinal (UGI) cancer survivors. This study aimed to explore the experiences of participants who engaged in a telehealth, multidisciplinary rehabilitation programme for UGI cancer survivors. Methods . This single‐arm feasibility study recruited participants who completed curative treatment for UGI cancer. Participants ( n = 10, male = 9) aged 58–76 years were 5–17 months postsurgery. A 12‐week telehealth rehabilitation programme was delivered via video call, consisting of group resistance training, remotely monitored aerobic training, 1 : 1 dietary counselling, 1 : 1 physiotherapy support, and group education sessions. Independent researchers conducted semistructured interviews at postintervention assessments. Transcripts were analysed using reflexive thematic analysis (RTA). Results . RTA of participant transcripts generated three overarching themes: (1) ReStOre@Home impacted psychosocial and physical needs by addressing a broad and meaningful gap in services, (2) paving a pathway towards prosperity, and (3) contrasting experiences with using technology. Participants’ preferences and recommendations for future telehealth programmes were discussed. Conclusions . A telehealth multidisciplinary rehabilitation programme supported participants in physical and psychosocial recovery. Qualitative analysis identified an important ongoing need for some in‐person care and provided detailed insights into participant experiences during telehealth‐delivered rehabilitation.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".