Development and content validation of the Wheelchair Use Confidence Scale: a mixed-methods study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it