The Wheelchair Use Confidence Scale (WheelCon): Arabic translation, adaptation, and validation
Bibliographic record
Abstract
This study translated and culturally adapted the Wheelchair Use Confidence Scale for Manual Wheelchair Users (WheelCon-M) and the Wheelchair Use Confidence Scale for Power Wheelchair Users (WheelCon-P) into Arabic and examined their reliability and validity. Internal consistency and test–retest reliability were examined, and concurrent validity was evaluated using Pearson correlation coefficients with the Arabic versions of the Functioning Everyday with a Wheelchair (FEW) and the Functional Mobility Assessment (FMA). The Arabic translated versions of the WheelCon-M (WheelCon-M-A) and the WheelCon-P (WheelCon-P-A) were administered to 33 adult wheelchair users. Cronbach’s α was 0.94 (p < 0.01) for the WheelCon-M-A and 0.95 (p < 0.01) for the WheelCon-P-A. The WheelCon-M-A and WheelCon-P-A were reliable with respect to test–retest with an ICC of 0.974 (p < 0.01) and 0.965 (p < 0.01), respectively. The Pearson correlation coefficient of the WheelCon-M-A scores was 0.776 with the FEW scores and 0.685 with the FMA scores (p < 0.01). The Pearson correlation coefficient of the WheelCon-P-A scores was 0.782 with the FEW scores and 0.654 with the FMA scores (p < 0.01). This study has provided preliminary evidence of new valid, reliable, and useful tools for healthcare professionals to help measure confidence with wheelchair use among Arab wheelchair users.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".