Power mobility skill progression for children and adolescents: a systematic review of measures and their clinical application
Bibliographic record
Abstract
AIM: To identify and critically appraise standardized measures of power mobility skill used with children (18y or younger) with mobility limitations and explore the measures' application for 'exploratory', 'operational', and 'functional' learners. METHOD: Five electronic databases were searched along with hand-searching for peer-reviewed articles published in English to July 2017 (updated 31st August 2017). Key terms included power(ed) mobility, power(ed) wheelchair, and database-specific terms. Studies included at least one child with a disability, and a detailed description of the measure of power mobility skill. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was followed with inclusion criteria set a priori. Two reviewers independently screened titles, abstracts, and full-text articles. RESULTS: Of 96 titles, 24 articles met inclusion criteria, describing nine measures of power mobility skill. The Wheelchair Skills Checklist, the Powered Mobility Program (PMP), and the Power Mobility Training Tool were augmented by three adaptations of the PMP. Two additional measures were further developed to create a third, the Assessment of Learning Powered mobility use. Validity evidence related primarily to content development while reliability evidence was reported on only two measures. INTERPRETATION: All measures are in the initial stages of development and testing. Research investigating the measures' appropriateness for different types of learners and environments is warranted. WHAT THIS PAPER ADDS: There are four distinct measures of paediatric power mobility skill: three task-based, one process-based. Power mobility learners may be divided into three groups: exploratory, operational, and functional. Application of measures of power mobility skill differs for these three groups.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".