Feasibility of telerehabilitation to address the orientation and mobility needs of individuals with visual impairment: perspectives of current guide dog users
Bibliographic record
Abstract
Orientation and Mobility (O&M) training, including guide dog services, is crucial for individuals with low vision and blindness to attain independent travel. While teleassistance has proven effective for navigation and communication, telerehabilitation in O&M remains unexplored.Objective To assess guide dog users’ perspectives on the feasibility of telerehabilitation for their O&M needs.Method An online survey gathered insights from 56 guide dog (GD) users (Mean age = 59, Mean GD used = 4, Mean duration of use = 22 years). Thirteen GD users further participated in interviews or focus groups to explore survey responses. Data were analyzed using content analysis.Findings Most (40) were blind, and 16 had low vision, with intermediate (25) and advanced (25) communication technology proficiency. Most GD users (46) underwent residential training, and 10 received one-on-one visits. Qualitative analysis revealed acceptance of telerehabilitation services, citing accessibility as an advantage. However, GD users expressed concerns about safety, potential loss of behavioral observation, and social contact loss. Success depended on the type of technology, service type, and personal attributes.Conclusion While feasible, telerehabilitation services may not be universally suitable for all training stages. Flexibility and applicability in service design are necessary to accommodate individual preferences and experience levels.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".