Development and content validation of the Wheelchair Use Confidence Scale: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Confidence in one's ability to perform a given task can be a stronger predictor of performance than skill itself. There are currently no measures to assess confidence with manual wheelchair use. The objective of this study was to develop and assess the content validity of the Wheelchair Use Confidence Scale (WheelCon-M). METHOD: A two-phase mixed-methods design was used. Semi-structured interviews were conducted to generate items, followed by a Delphi survey for item selection. Persons who use a wheelchair, health care professionals, and researchers participated in both phases of the study. RESULTS: An 84-item WheelCon-M was developed based on the qualitative data. After the Delphi survey, a final 62-item WheelCon-M was composed of the following six areas (number of items per area): Negotiating the Physical Environment (33 items), Activities Performed using a Manual Wheelchair (11 items), Knowledge and Problem Solving (6 items), Advocacy (4 items), Managing Social Situations (5 items) and Managing Emotions (3 items). CONCLUSION: This article reports the development and content validation of the WheelCon-M. As a scale to measure confidence with wheelchair use was not available prior to this work, clinicians now have a method of identifying individuals who have low confidence with wheelchair use.
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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 | 0.062 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".