Concerns regarding the accessibility of self-service interactive devices for people with disabilities
Bibliographic record
Abstract
PURPOSE: Self-service interactive devices allow users to access information or services without directly interacting with service personnel. As the prevalence of disability increases, it is important to consider the barriers individuals face in using these devices and explore opportunities to increase accessibility through assistive and adaptive technologies. This study aimed to establish recommendations to enhance the accessibility of self-service interactive devices, with the objective of understanding users' experiences with these devices. MATERIALS AND METHODS: Nineteen semi-structured interviews were held with stakeholders focusing on accessible design for people with disabilities, categorized as (a) persons with lived experiences with disability, (b) disability advocates, or (c) assistive technology industry experts. The study used content analysis to identify recurring concepts and opportunities to improve accessibility. Participants discussed the potential benefits of updating or incorporating additional accessibility technologies into self-service devices and proposed solutions to existing deficiencies. RESULTS: Common concerns expressed among participants included the privacy and security of self-service devices, protection of personal information, and the consistency and usability of devices. Participants also suggested how this inconsistency could be mitigated and how to improve existing accessibility functionalities. Accessible functionalities in self-service devices have the potential to help address the unmet needs of Canadians with disabilities. CONCLUSIONS: With the breadth of available accessible and adaptive technologies, the study concludes that it is imperative to understand (1) what technologies are useful to people with disabilities, (2) whether the inclusion of these technologies is feasible in self-service devices, and (3) how user experience can be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".