Development and Evaluation of a New Taxonomy of Mobility-Related Assistive Technology Devices
Bibliographic record
Abstract
OBJECTIVE: This article reports on the development of a new taxonomy for mobility-related assistive technology devices. DESIGN: A prototype taxonomy was created based on the extant literature. Five mobility device experts were engaged in a modified Delphi process to evaluate and refine the taxonomy. RESULTS: Multiple iterations of expert feedback and revision yielded consensual agreement on the structure and terminology of a new mobility device taxonomy. The taxonomy uses a hierarchical framework to classify ambulation aids and wheeled mobility devices, including their key features that impact mobility. Five attributes of the new taxonomy differentiate it from previous mobility-related device classifications: (1) hierarchical structure, (2) primary device categories are grouped based on their intended mobility impact, (3) comprehensive inclusion of technical features, (4) a capacity to assimilate reimbursement codes, and (5) availability of a detailed glossary. CONCLUSIONS: The taxonomy is intended to support assistive technology outcomes research. The taxonomy will enable researchers to capture mobility-related assistive technology device interventions with precision and provide a common terminology that will allow comparisons among studies. The prominence of technical features within the new taxonomy will hopefully promote research that helps clinicians predict how devices will perform, thus aiding clinical decision making and supporting funding recommendations.
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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 | 0.063 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".