A qualitative study of clinicians’ and individuals’ with lower limb loss perspectives on the development of a novel online self-management program
Bibliographic record
Abstract
PURPOSE: To explore the rehabilitation preferences and experiences of clinicians and patients for education after lower limb loss to facilitate the development of an online self-management program. METHODS: A qualitative descriptive approach was used. Thirty-one clinicians (physiotherapists, occupational therapists, and prosthetists), and 26 patients with lower limb loss (transtibial and transfemoral amputation; mean age (SD) of 63.3 (9.1), years) were recruited. We used semi-structured focus groups and one-on-one interviews, and audio recorded the interviews. Data were analyzed using conventional content analysis. RESULTS: Three themes were identified: (1) Needing education in rehabilitation described the education in current practice as one-on-one discussion and booklets and highlighted the limitations of education such as its length, static nature, and inaccessible for patients living in remote areas. (2) Getting back to activities prior to amputation emphasized how goal setting and social support could assist patients and facilitate self-management. (3) Augmenting learning highlighted the need for an accessible complementary source for education and potential solutions to overcome the barriers of online delivery. CONCLUSIONS: Our findings underscore the importance of education in the rehabilitation of patients to help them get back to their activities. An online accessible tool may improve education by providing information and peer support.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".