Accessibility and usability of self-serve kiosks for blind and partially sighted Canadians
Bibliographic record
Abstract
BACKGROUND: Approximately 7.4% of Canadians over the age of 15 report being blind or partially sighted; this impacts their daily functioning in public spaces [1]. Technological advances have included the proliferation of self-serve kiosk in many consumer settings. However, absent from discussions of community accessibility is the experiences of Canadians who are blind or partially sighted. OBJECTIVE: To better understand the experiences of this population with self-serve kiosks. METHODS: A descriptive cross-sectional study design was used to analyze survey data collected as part of a survey by Canadian National Institute for the Blind (CNIB) of people who are blind, Deafblind partially sighted about their use of self-serve kiosks. 731 participants were surveyed, representing a response rate of 3.5% across Canada. RESULTS: 64.14% of participants faced barriers in completing a task using self-serve technology. Human assistance was required to complete the tasks in most instances. 65.74% of participants reported they did not enjoy using self-serve kiosks and 60.90% of participants reported they would not continue to use self-serve kiosks in the future. CONCLUSIONS: The findings highlight a need to promote accessibility in the creation and implementation of self-serve kiosks in order to further their use and decrease exclusion of people who are blind and partially sighted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".