Is Group-Based Pelvic Floor Telerehabilitation an Acceptable Care Option for Older Women With Urinary Incontinence? A Qualitative Descriptive Study
Bibliographic record
Abstract
Purpose: Many older women experience urinary incontinence (UI). However, access to pelvic floor muscle (PFM) training, an effective first-line treatment, is limited. Group-based telerehabilitation appears cost-effective and could improve accessibility. Recent evidence suggests a group-based PFM telerehabilitation programme for treating UI in older women (teleGROUP) is feasible and clinically relevant. This qualitative study assessed teleGROUP’s acceptability to both patients and a clinician. Method: Thirty-three older women who completed the teleGROUP programme, and the physiotherapist who led it, discussed their experiences in focus groups or interviews. We transcribed qualitative data and analyzed them using thematic analysis. Results: Participating women enhanced self-efficacy in managing symptoms and contracting PFMs, reporting high self-efficacy in attending sessions and completing home exercises. The programme fostered positive affective attitudes in participants and the physiotherapist, aligned with their values, and improved self-efficacy with technology. Participants and the physiotherapist perceived clinical effectiveness as high. Women identified time commitment as a burden; the physiotherapist found time management challenging. However, the women found remote participation convenient. They recognized that PFM exercises led to symptom improvement. Conclusion: The programme proved acceptable for older women and the physiotherapist. Pragmatic trials in real-life clinical settings and implementation studies are necessary for further validation.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".