A Mapping Review of Existing Tools to Assess Physical Qualities of Manual Wheelchair Users
Bibliographic record
Abstract
Background: Assessment of physical competencies is one way to enhance uptake and maintain participation in a leisure-time physical activity (LTPA) for manual wheelchair (MWC) users. Weineck’s model explains physical competencies through eight physical qualities. The use of this model may influence MWC users’ motivation for participation in LTPA. The aim of this study was to identify and categorize existing assessment tools designed for MWC users of physical qualities (strength, speed, power, muscular endurance, cardiovascular endurance, balance, and flexibility). Methods: A mapping review was conducted following the “Preferred reporting items for systematic reviews and meta-analyses (PRISMA)” guidelines. Two reviewers selected articles that documented assessment tools for the physical qualities of MWC users. Tools were extracted from each article to categorize them in a list. Results: A total of 149 articles that contained assessments of physical qualities were included in the review. A total of 97 assessment tools were extracted and categorized according to the eight physical qualities. Conclusions: These assessments are categorized into physical qualities that would facilitate the creation of test batteries aimed at assessing physical qualities in MWC users. This study is the first step in the construction of a test battery to assess the physical qualities of MWC users.
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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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.037 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".