Understanding user-perceived benefits of an online self-management program with peer mentor support for lower limb loss: A mixed-methods study
Bibliographic record
Abstract
BACKGROUND: People with lower limb loss face physical and psychological challenges that impact daily life. While self-management programs can help, access is often limited due to in-person delivery. eHealth offers a more accessible alternative, yet few comprehensive program exists. OBJECTIVES: This study investigated the perceived benefits and usefulness of an eHealth self-management program, delivered by peer mentor support, for individuals with lower limb loss. STUDY DESIGN: A mixed-methods approach was used. METHODS: Adults with a unilateral transtibial or transfemoral amputation, who had been casted for their initial prosthesis within the past year, used the eHealth program for 6 weeks, and met with a peer mentor weekly throughout the program. The eHealth program offered information on goal setting, emotional and physical wellness, home modifications, use of a prosthesis, residual limb care, daily activities, and exercise. User experiences were explored through semistructured interviews and the Perceived Usefulness questionnaire. RESULTS: Twelve participants, with a median age of 56 (range 26-79) years, took part in the study. The median Perceived Usefulness score was 69 out of 85 (range 61-75). The study revealed 2 themes. The first theme, "Pursuing Goals," highlighted the important role of the eHealth program and peer support in establishing, actively working toward, and ultimately achieving realistic goals. The second theme, "Augmenting Education," characterized the eHealth program as an invaluable educational resource that supported participants in gaining new knowledge and reviewing previously learned content. CONCLUSIONS: This study underscores the potential for further refinement and evaluation of the self-management program. An eHealth program with support of a peer mentor that enables goal setting has the potential to augment postoperative self-management in individuals with lower limb loss regardless of their geographical location.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".