Correlates of self-reported Wheelchair Skills Test Questionnaire scores of new users of mobility scooters: a cross-sectional study
Bibliographic record
Abstract
Objectives To describe the subjective reported scooter-skill scores of new mobility scooter users and to identify significant correlations with other characteristics and measures.Materials and methods This was a single-centre study using a cross-sectional design. Participants (N = 22) completed the Wheelchair Skills Test-Questionnaire (WST-Q) Version 4.3 for scooter users. It measures the users’ perceived capacity (what the user can do), performance (what the user actually does), and confidence (or self-efficacy). Their scooter skills were also rated objectively with the Wheelchair Skills Test (WST). They completed standardised measures of cognition, hearing, vision, life space mobility, visual attention and task switching, and confidence negotiating the social environment using their scooters.Results Mean total WST-Q capacity scores were 83% and performance scores were 25%. WST-Q capacity scores had significant positive correlations with WST-Q performance (r = 0.321) and confidence scores (r = 0.787), WST capacity scores (r = 0.488), and confidence negotiating the social environment (WheelCon) (r = 0.463). WST-Q capacity scores were significantly negatively correlated with Trail Making B scores (r = −0.591) and age (r = −0.531).Conclusions The correlations between WST-Q scores and other variables are similar to those found in other studies among users of scooters and other mobility devices. The gap between capacity and performance scores highlights the needs for additional skills training in this population of novice scooter users.IMPLICATIONS FOR REHABILITATIONIn implementing scooter training for new scooter users, attention should be paid to building community-based skills for navigating both the physical and the social environment.Scooter users’ age and their driving capabilities need to be taken into account when developing and delivering the training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".