Wheelchair Mobility Confidence Scale for Dutch youth using a manual wheelchair
Bibliographic record
Abstract
Purpose: The objective of this study was to develop a questionnaire to assess confidence in wheelchair mobility in Dutch youth (WheelCon-Mobility Dutch Youth).Methods: (1) A forward–backward translation process was used to translate the original WheelCon-M from English to Dutch. (2) Items related to wheelchair mobility in Dutch youth were selected and adapted based on focus groups with youth, parents and health care professionals to create the WheelCon-Mobility Dutch Youth. (3) The WheelCon-Mobility Dutch Youth and the Utrecht Pediatric Wheelchair Mobility Skills Test 2.0 (UP-WMST 2.0) were administered to 62 participants to evaluate internal consistency and construct validity.Results: Translation and cultural adaptation led to general adaptations in instructions, sentence structure and response scale. At the item level, 24 items were included with (n = 17) and without (n = 7) adaptation, 10 items were deleted and 7 new items were included. The WheelCon-Mobility Dutch Youth had an excellent Cronbach’s alpha of 0.924 and a significant correlation (r = 0.44, p < .001) with the UP-WMST 2.0.Conclusions: This study resulted in the adaptation of the WheelCon-M into the WheelCon-Mobility for Dutch youth using a manual wheelchair. Our study suggests there is evidence supporting the internal consistency and construct validity of the WheelCon-Mobility Dutch Youth.Implications for RehabilitationThe WheelCon-Mobility Dutch Youth is a newly developed tool for assessing confidence in wheelchair mobility in Dutch youth using a manual wheelchair.It is important to assess performance and confidence in wheelchair mobility in paediatric rehabilitation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".