Communication with face masks during the COVID-19 pandemic for adults with hearing loss
Bibliographic record
Abstract
Face masks have become common protective measures in community and workplace environments to help reduce the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Face masks can make it difficult to hear and understand speech, particularly for people with hearing loss. An aim of our cross-sectional survey was to investigate the extent that face masks as a health and safety protective measure against SARS-CoV-2 have affected understanding speech in the day-to-day lives of adults with deafness or hearing loss, and identify possible strategies to improve communication accessibility. We analyzed closed- and open-ended survey responses of 656 adults who self-identified as D/deaf or hard of hearing. Over 80% of respondents reported difficulty with understanding others who wore face masks. The proportion of those experiencing difficulty increased with increasing hearing loss severity. Recommended practical supports to facilitate communication and social interaction included more widespread use of clear face masks to aid lip-reading; improved clarity in policy guidance on face masks; and greater public awareness and understanding about ways to more clearly communicate with adults with hearing loss while wearing face masks.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".