Measuring wheelchair confidence among power wheelchair users: an adaptation of the WheelCon-M using focus groups and a think aloud process
Bibliographic record
Abstract
PURPOSE: The aim of this study was to adapt the WheelCon-M, an outcome measure used to assess confidence with manual wheelchair use, into a version that is applicable for power wheelchair users (WheelCon-P). METHODS: Adaptation of the WheelCon-M into the WheelCon-P occurred in two phases: (1) item modification was conducted using focus groups and (2) item refinement was done using a think aloud process. RESULTS: The healthcare professionals (n = 12) were mostly female, mostly occupational therapists and practiced an average of 14 years. The power wheelchair users (n = 8) were 50% men with an average of 8 years of power wheelchair experience and a range of diagnoses. In phase 1, of the 63 WheelCon-M items, 6 remained the same, 18 were removed, 38 modified and 15 new items added to develop the WheelCon-P. In phase 2, 15 WheelCon-P items were refined. CONCLUSIONS: This study resulted in the modification of the WheelCon-M into the WheelCon-P, a 59-item, self-report outcome measure designed to assess confidence with power wheelchair use. The next step in the evolution of this promising new measure is the assessment of its measurement properties. Implications for Rehabilitation The WheelCon-P is a new outcome measure designed to assess confidence with power wheelchair use. This study suggests that the items in this measure will be interpreted as intended.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".