Development of a co-created online self-management program for people with lower limb loss: self-management for amputee rehabilitation using technology (SMART)
Bibliographic record
Abstract
PURPOSE: To develop an online self-management program for individuals with recent lower limb loss, called Self-Management for Amputee Rehabilitation using Technology (SMART). MATERIALS AND METHODS: We used the Intervention Mapping Framework as a blueprint and involved stakeholders throughout the process. A six-step study was conducted including (1) needs assessment using interviews, (2) translating needs to content, (3) applying the content into a prototype using theory-based methods, (4) a usability assessment using think-aloud cognitive testing, (5) planning for future adoption and implementation, and (6) assessing feasibility using mixed-methods to generate a plan to assess the effectiveness on health-outcomes in a randomized controlled trial. RESULTS: = 12) by recruiting individuals with lower limb loss from different pools. We modified SMART to be assessed in a randomized controlled trial. SMART is a six-week online program with weekly contact of a peer mentor with lower limb loss who supported patients with goal-setting and action-planning. CONCLUSIONS: Intervention mapping facilitated the systematic development of SMART. SMART may improve health outcomes, but this would need to be confirmed in future studies.Implications for rehabilitationLearning new coping strategies and habits are essential after lower limb loss.Given the limitations and inaccessibility of educational and rehabilitation programs, online self-management education can assist patients in their recovery.Self-Management for Amputee Rehabilitation using Technology (SMART) has the potential to augment the self-management behaviors in individuals with lower limb loss through an improvement in access to educational content, skill-based videos, and support of a peer.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".