International prevalence of tactile map usage and its impact on navigational independence and well-being of people with visual impairments
Bibliographic record
Abstract
Abstract For people with visual impairments (PVI), understanding space is crucial for independence and tactile maps are useful tools to gain spatial information and improve orientation. However, their popularity and impacts in the PVI population are not yet fully understood. Thus, this study aims to determine the prevalence of tactile map usage and their effects on independent travel and well-being internationally. To do so, two online surveys were completed by PVI (n 1 = 752, n 2 = 510) in 40 countries from which information was collected related to travel habits, spatial abilities, experience with tactile maps, mobility services, and perceived well-being. The surveys revealed that only 17.15% of respondents have had experience with tactile maps and that this tactile map experience was related to better cognitive mapping skills, a higher level of education, a higher perceived well-being and a higher sense of independence (i.e., perceiving the area of living as more accessible, and requiring assistance less often). This study confirms that tactile maps have a positive impact on the independent travel and well-being of PVI around the world, including both individuals with low-vision and blindness, and demonstrates the importance of early tactile map exposure, as tactile maps support the development of generalizable spatial concepts and abilities.
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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 | 0.002 |
| 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) | 0.003 | 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".