Measure for the assessment of confidence with manual wheelchair use (WheelCon-M) version 2.1: Reliability and validity
Bibliographic record
Abstract
OBJECTIVE: To evaluate the measurement properties of the Wheelchair Use Confidence Scale for manual wheelchair users version 2.1 (WheelCon-M 2.1). DESIGN: Cohort study. PARTICIPANTS: Volunteer sample of 83 community-dwelling, experienced manual wheelchair users. METHODS: Participants completed the WheelCon-M 2.1 twice to assess retest reliability. Validity was assessed by evaluating hypothesized relationships between the WheelCon-M 2.1 and relevant variables. Responsiveness was assessed using the standard error of measurement (SEM) and smallest real difference (SRD). RESULTS: The median (interquartile range) WheelCon-M 2.1 score was 84.6 (71.3-92.0) of a possible 0-100. The one-week retest intraclass correlation coefficient was 0.84 with 95% bias-corrected and accelerated confidence intervals of 0.77-0.90. Cronbach's alpha was 0.92. Correlations ranging from rs = -0.19 (p = 0.780) to rs = 0.58 (p < 0.001) were found between the WheelCon-M 2.1 and other relevant outcome measures with all correlations being statistically significant except for age (p = 0.780) and social support (p = 0.057). A statistically significant difference was not found between the sexes (p = 0.140). The SEM and SRM were 5.9 and 16.4, respectively. CONCLUSIONS: WheelCon-M 2.1 has high internal consistency, strong retest reliability, and support for concurrent validity, construct validity and responsiveness. This new test holds promise as a clinical and research tool.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".