Development of the Wheelchair Interface Questionnaire and initial face and content validity
Bibliographic record
Abstract
BACKGROUND: Because resources are limited in low- and middle-income countries (LMIC), the development of outcome measures is of interest. Wheelchair outcome measures are useful to support evidence-based practice in wheelchair provision. OBJECTIVES: The Wheelchair Interface Questionnaire (WIQ) is being developed to provide a professional perspective on the quality of the interface between a wheelchair and its user. This article discusses the development of the WIQ and its face and content validity. METHOD: During field studies in Kenya, we sought to include professional report data on the wheelchair-user interface that could be analysed to inform design changes. None of the existing measures was focused on the interface between users and their wheelchairs. The WIQ was developed to meet this need. To investigate face and content validity, 24 experienced wheelchair professionals participated in a study that included two rounds of an online survey and a focus group in Kenya. RESULTS: Responses were categorised by topic and the WIQ was modified following each iteration. Participants affirmed the usefulness of a brief professional report measure to provide a snapshot of the user-wheelchair interface. Participants emphasised the importance of brevity, wide applicability and provision of specific feedback for wheelchair modification or design changes. The focus group agreed that the final version provided useful data and was applicable to virtually all wheelchair users in LMIC. CONCLUSION: These preliminary studies indicate initial face and content validity of the WIQ as a method for providing a professional perspective on the interface between a user and his or her wheelchair. KEYWORDS: Outcome measure; wheelchair assessment; user-wheelchair interface; wheelchair appropriateness; professional report.
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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 | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".