Measuring mobility performance: experience gained in designing a mobility course
Bibliographic record
Abstract
BACKGROUND: This paper reviews the most common methods of measuring and scoring orientation and mobility (O and M) and the effects of visual impairment on O and M. We discuss the difficulties inherent in designing a 'real-world' course to measure O and M and we describe the course that we finally used. METHODS: Thirty-five participants in two age groups, with low vision due to a variety of disorders, took part in mobility trials on the final version of the course. Aspects of visual function were measured. RESULTS: Factor analysis indicated that mobility errors, visual detection distance and visual identification distance were grouped with measures of visual acuity, contrast sensitivity and Humphrey visual field mean deviation, while preferred walking speed and walking speed were separately grouped. Humphrey pattern standard deviation did not group with any other measure and neither did percentage preferred walking speed. This study is in agreement with other studies that visual field and contrast sensitivity, sometimes with low contrast visual acuity, were the best clinical visual predictors of mobility performance. Based on our experiences we present a number of recommendations for designing courses for assessing mobility. CONCLUSIONS: For future studies, it would behove researchers to include a range of mobility measures, until further understanding is gained about how they are interrelated and contribute information on the relationship among mobility, vision and other individual factors.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".