Effect of a Community-Based Peer-Led eHealth Wheelchair Skills Training Program: A Randomized Control Trial
Bibliographic record
Abstract
OBJECTIVE: To measure the effect of a community-based peer-led eHealth manual wheelchair (MWC) skills training program on community participation, wheelchair skills capacity and performance, wheelchair-specific self-efficacy, and health-related quality of life. DESIGN: Randomized control trial with wait-list control group. SETTING: Community. PARTICIPANTS: Community-dwelling MWC users aged 18 years or older who propel using both arms (N=50). INTERVENTIONS: The 4-week MWC skills training intervention was comprised of 3 virtual sessions with a peer trainer and a self-directed eHealth home training application delivered via a computer tablet. Peer trainers were experienced MWC users who had received structured training for intervention delivery. Participants were provided with required equipment and encouraged to involve a care provider during home training. Peer trainers tailored the program to life activities participants identified as relevant. The control group were placed on a 4-week no intervention wait-list (reflecting typical clinical practice) and after postintervention data collection were offered the training program. MAIN OUTCOME MEASURES: The primary outcome was community participation measured by the Wheelchair Outcome Measure. Secondary outcomes included skill capacity and performance on the Wheelchair Skills Test-Questionnaire, self-efficacy on the Wheelchair Use Confidence Scale, and health-related quality of life on the Short-Form 36 Health Survey Enabled. RESULTS: =0.09), increasing by 24%. Per protocol (n=42) secondary analyses indicated significant improvements of 16.1% in the skill capacity (P=.004), 11.4% in self-efficacy (P=.017), and 7% relative improvement in quality of life (P=.012). CONCLUSIONS: The findings indicate that an eHealth MWC training program incorporating peer and tablet application training components was effective in improving community participation, skill capacity, self-efficacy, and quality of life for a wide range of MWC users. An eHealth delivery format offers considerable potential from both an access and resource perspective.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".