Face validity of standardized assessments for wheeled mobility & seating evaluations
Bibliographic record
Abstract
A problem in the Complex Rehabilitation Technology industry is the lack of standardization in the assessment for wheeled mobility and seating (WMS). The aim of this paper was to identify assessment tools commonly used by clinicians during WMS evaluations. After the tools were identified by a panel of 12 subject matter experts, a presentation at the 2018 International Seating Symposium in Vancouver, Canada and the 2018 European Seating Symposium in Dublin, Ireland polled attendees via the Sli.do polling application to determine professional opinions of each tool, resulting in face validity for use in wheelchair evaluations. The Lawshe Content Validity Ratio was used to convert this anecdotal data into numerical data, indicating which tools were most and least used by attendees. Finally, a literature search was conducted to determine the reliability, validity, and International Classification of Functioning, Disability, & Health domain for each measure. The findings indicate that while there are many standardized and reliable assessment tools available for wheeled mobility and seating evaluations, most clinicians use only a few standardized assessment tools during WMS evaluations.
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.
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 | 0.093 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".