How Face Masks Affect the Use of Echolocation by Individuals With Visual Impairments During COVID-19: International Cross-sectional Online Survey
Bibliographic record
Abstract
BACKGROUND: Although a critical safety measure, preliminary studies have suggested that the use of a face mask may pose a problem for some users with disabilities. To date, little is known about how the wearing of a traditional face mask may pose a barrier to individuals with visual impairments who draw on auditory cues and echolocation techniques during independent travel. OBJECTIVE: The goal of this study was to document the difficulties, if any, encountered during orientation and mobility due to the use of a face mask during the COVID-19 pandemic and the strategies used to address these barriers. METHODS: In total, 135 individuals aged 18 years and older who self-identified as being blind, being deafblind, or having low vision and who could communicate in either English or French completed an anonymous cross-sectional online survey between March 29 and August 23, 2021. RESULTS: In total, 135 respondents (n=52, 38.5%, men; n=83, 61.5%, women) between the ages of 18 and 79 (mean 48.22, SD 14.48) years participated. Overall, 78 (57.7%) self-identified as blind and 57 (42.3%) as having low vision. In addition, 13 (9.6%) identified as having a combined vision and hearing loss and 3 (2.2%) as deafblind. The most common face coverings used were cloth (n=119, 88.1%) and surgical masks (n=74, 54.8%). Among the barriers raised, participants highlighted that face masks made it more difficult to locate people (n=86, 63.7%), communicate with others (n=101, 74.8%), and locate landmarks (n=82, 60.7%). Although the percentage of those who used a white cane before the pandemic did not substantially change, 6 (14.6%) of the 41 participants who were guide dog users prior to the pandemic reported no longer working with a guide dog at the time of the survey. Moreover, although guide dog users reported the highest level of confidence with independent travel before the pandemic, they indicated the lowest level of confidence a year after the pandemic began. CONCLUSIONS: These results suggest that participants were less able to draw on nonvisual cues during independent travel and social interactions due to the use of a facemask, contributing to a reduction in perceived self-confidence and independence. Findings inform the development of evidence-based recommendations to address identified barriers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".