Exploring the Influence of a Community-Based Peer-Led Wheelchair Skills Training on Satisfaction with Participation in Children and Adolescents with Cerebral Palsy and Spina Bifida: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Peer-led approaches improve satisfaction with participation, wheelchair skills and wheelchair use self-efficacy in adults, but the evidence is limited in children. This pilot study aimed to explore the influence of community-based, peer-led, group wheelchair training program (i.e., Seating To Go) on satisfaction with participation (primary outcome), wheelchair skills, and wheelchair use self-efficacy in children and adolescents with cerebral palsy and spina bifida. METHODS: A single group pre-post design was used. Invitations were shared online and diffused by clinicians and advocacy and provider groups to recruit a convenience sample of eight pediatric wheelchair users. Participants completed the Seating To Go program in groups that were facilitated by adult wheelchair users. Satisfaction with participation (Wheelchair Outcome Measure-Young People), wheelchair skills (Wheelchair Skills Test), wheelchair use self-efficacy (Wheelchair Use Confidence Scale), and perceived wheelchair skills capacity (Wheelchair Skills Test Questionnaire; proxy rating: parents) were evaluated before and after the Seating To Go program. Descriptive statistics and nonparametric longitudinal data analysis were conducted to explore changes in all outcomes from baseline to post-intervention. RESULTS: Pediatric wheelchair users (ranging in age from 5 to 15 years) and their parents reported statistically significant improvements in satisfaction with participation. The improvements in wheelchair skills and wheelchair confidence were also statistically significant, but not the parents' perception of their children's wheelchair skills. CONCLUSIONS: A community-based peer-led approach to wheelchair skills training seems promising for improving wheelchair outcomes in pediatric wheelchair users. Further controlled studies with larger samples are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".