Measuring wheelchair intervention outcomes: Development of the Wheelchair Outcome Measure
Bibliographic record
Abstract
PURPOSE: Provision of a wheelchair has immediate intuitive benefits; however, it can be difficult to evaluate which wheelchair and seating components best meet an individual's needs. As well, funding agencies now prefer evidence of outcomes; and therefore measurement upon prescription of a wheelchair or its components is essential to demonstrate the efficacy of intervention. As no existing tool can provide individualized goal-oriented measure of outcome after wheelchair prescription, a research project was undertaken to create the Wheelchair Outcome Measure (WhOM). METHOD: A mixed methods research design was employed to develop the instrument, which used in-depth interviews of prescribers, individuals who use wheelchairs and their associates, supplemented by additional questions in which participant preferences in key areas of the measure were quantified. RESULTS: The WhOM is a client-specific wheelchair intervention measurement tool that is based on the World Health Organization's International Classification of Function, Disability, and Health. It identifies desired outcomes at a participation level and also acknowledges concerns about body structure and function. CONCLUSION: The new outcome instrument will allow clients to identify and evaluate the outcomes they wish to achieve with their wheelchairs and seating and provide clinicians a way to quantify outcomes of their interventions in a way that is meaningful to the client and potential funding sources.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".