Test-Retest Reliability, Internal Item Consistency, and Concurrent Validity of the Wheelchair Seating Discomfort Assessment Tool
Bibliographic record
Abstract
Discomfort is a common problem for wheelchair users. Few researchers have investigated discomfort among wheelchair users or potential solutions for this problem. One of the impediments to quantitative research on wheelchair seating discomfort has been the lack of a reliable method for quantifying seat discomfort. The purpose of this study was to establish the test-retest reliability, internal item consistency, and concurrent validity of a newly developed Wheelchair Seating Discomfort Assessment Tool (WcS-DAT). Thirty full-time, active wheelchair users with intact sensation were asked to use this and other tools in order to rate their levels of discomfort in a test-retest reliability study format. Data from these measures were analyzed in SPSS using an intraclass correlation coefficient (ICC) model (2,k) to measure the test-retest reliability. Cronbach's alpha was used to examine the internal consistency of the items within the WcS-DAT. Concurrent validity with similar measures was analyzed using Pearson product-moment correlations. ICC scores for all analyses were above the established lower bound of .80, indicating a highly stable and reliable tool. In addition, alpha scores indicated good consistency of all items without redundancy. Finally, correlations with similar tools, such as the Chair Evaluation Checklist and the Short Form of the McGill Pain Questionnaire, were significant at the .05 level, and many were significant at the .001 level. These results support the use of the WcS-DAT as a reliable and stable tool for quantifying wheelchair seating discomfort. Its application will enhance the ability to assess and to research this important problem and will provide a means to validate the outcomes of specialized seating interventions for the study population of wheelchairs 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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".