The Purdue Pegboard test: normative data for older adults with low vision
Bibliographic record
Abstract
Purpose The usability of assistive technologies depends, in part, on the user's ability to manipulate the device. In the context of aging and visual impairment, the visibility of any device and its components becomes crucial, and often users rely on tactile information in order to overcome visibility barriers. The purpose of this study was to establish performance norms for older adults with low vision on a common measure of manual dexterity: the Purdue Pegboard Test. Method The Purdue Pegboard was completed visually with the dominant, non-dominant and both hands by 134 older adults (age 60-97) with various levels of low vision, ranging from 20/30 to 20/604 in the better eye. Results Scores decreased significantly as age increased. In addition, performance using the dominant hand was generally best. Compared to previously published values, scores were lower than the norms for healthy older adults as well as those for younger visually impaired individuals. Conclusions The present values for older adults with low vision add to the already existing standards and allow for comparison among future studies with this population. Systematic examination of manual dexterity in low vision clients will enable rehabilitation specialists to make more informed recommendations in terms of usable low-vision devices. Implications for rehabilitation Older adults with visual impairment often rely on tactile cues when using assistive devices. The Purdue Pegboard provides a systematic evaluation of manual dexterity, whereby age norms exist for older adults and for visually impaired younger adults. We present normative data on the Purdue Pegboard test for older adults with low vision in order to facilitate comparison of performance. Systematic evaluation of manual dexterity will inform whether some assistive devices are suitable for older adults with a visual impairment.
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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 | 0.000 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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".