Outcome Measures for Wheelchair and Seating Provision: A Critical Appraisal
Bibliographic record
Abstract
Introduction: Every aspect of the wheelchair and seating provision process has an impact on overall outcomes for service users. This critical appraisal sought to identify outcome measures suitable for evaluation of wheelchair and seating provision, considering activity, participation, and impact of the service delivery on quality of life. Method: Outcome measures were identified using databases: Medline, CINHAL, PsychInfo, and Google Scholar. An evaluation was conducted to establish those that were particularly useful and a critical appraisal was completed. Findings: Five outcome measures identified as relevant for critical appraisal included: Wheelchair Outcome Measure; Functioning Every day in a Wheelchair; Goal Attainment Scale; Psychosocial Impact of Assistive Devices Scales; and the Quebec User Evaluation of Satisfaction with Assistive Technology. The strengths and limitations of each were identified. Conclusion: No single outcome measure captures all necessary information; trade-offs are inevitable. When choosing an outcome measure, the specific goals of the service evaluation and the resources available need to be considered within context. Critical appraisal of five outcome measures deemed appropriate for the evaluation highlighted some areas for consideration to inform decision making. A move towards sustainability indicators is suggested to monitor, measure, and respond to the provision processes and outcomes required to meet this primary need.
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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 | 0.254 | 0.495 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".