Exploring Correlates of Braille Reading Performance in Working-age and Older Adults with Visual Impairments
Bibliographic record
Abstract
Tactile, motor and cognitive capacities decline with age, but little is known about how this relates to braille reading outcomes. This study investigated correlates of braille reading speed among working-age and older adults. Texts were read in two modes (oral/silent) and two media (paper/electronic braille display) by 46 blind adults (age range 23–88) who learned braille between the ages of 4 and 63. Participants completed demographic questionnaires and tests of tactile acuity, fine-motor dexterity and working-memory. A relationship between decreased performance in tactile sensitivity and increased age was observed, but no relationship between increased age and braille reading speed was found. Active tactile acuity, reading frequency and braille learning age were significantly correlated with braille reading speed. No significant difference based on medium was observed, though silent reading was significantly faster than reading aloud. Findings underscore the importance of providing opportunities for older braille learners to secure training and to have frequent opportunities to practice braille between sessions. Findings also challenge the suggestion that increased age alone will impede braille learning. The most significant barrier faced by older braille learners is not their age, but challenges that could instead be addressed through policy and practice changes.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".