Adoption and usability of a braille communication assistive device (CAD) for face-to-face and remote communication in two users with deafblindness
Bibliographic record
Abstract
Purpose There are currently no best practice training methods with communication assistive devices (CAD) for individuals with deafblindness using a braille display notetaker connected to an iPhone. Therefore, to capture adoption and usability of braille CAD in clinic, the Technology development and evaluation model of Schulz et al. (2015) was applied. Objectives were 1-to measure the level of difficulty in life habits involving communication before, during and after training with a braille CAD, 2-to document the feasibility of training for face-to-face and remote communication, 3-to simulate and assess communication exchanges with an unknown hearing person face-to-face, and 4-to document the long-term usability and adoption of CAD.Methods A case study involved a 68-year-old woman and a 55-year-old man with Usher syndrome who have recently learned braille. Therapies were 90 min./week and 120 min. every two weeks. Data were collected with the Life habit 4.0 adapted (-3, 0, 6, 18 months), clinician workbook (monthly), filming scenario testing of communication (at 8–10 months) and observation grids (experience of usability, communication interaction).Results Results at 18 months revealed that communication with hearing persons, travelling outside the home alone and conducting non accessible leisure were still very difficult to impossible. Clinicians accomplished 12 modalities in therapy sessions. They encountered 14 challenges for face-to-face communication due to the instability of VoiceOver® with Notes® and French language skills. Scenarios involving commercial exchanges were not efficient (buying a pen, renewing health insurance card).Conclusion Adoption of a braille CAD for remote communication has proven satisfactory, effective but not efficient. Face-to-face communication has been non-adopted.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".