Exploring the use of smartphones and tablets among people with visual impairments: Are mainstream devices replacing the use of traditional visual aids?
Bibliographic record
Abstract
Smartphones and tablets incorporate built-in accessibility features, but little is known about their impact within the visually impaired population. This study explored the use of smartphones and tablets, the degree to which they replace traditional visual aids, and factors influencing these decisions. Data were collected through an anonymous online survey targeted toward visually impaired participants above the age of 18, whom had been using a smartphone or tablet for at least three months. Among participants (n = 466), 87.4% felt that mainstream devices are replacing traditional solutions. This is especially true for object identification, navigation, requesting sighted help, listening to audiobooks, reading eBooks and optical character recognition. In these cases, at least two-thirds of respondents indicated that mainstream devices were replacing traditional tools most or all of the time. Users across all ages with higher self-reported proficiency were more likely to select a mainstream device over a traditional solution. Our results suggest that mainstream devices are frequently used amongst visually impaired adults in place of or in combination with traditional assistive aids for specific tasks; however, traditional devices are still preferable for certain tasks, including those requiring extensive typing or editing. This provides important context to designers and rehabilitation personnel in understanding the factors influencing device usage.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".