Communication during the COVID-19 pandemic: the hearing-impaired perspective
Bibliographic record
Abstract
To understand the communicational and psychosocial effects of COVID-19 protective measures in real-life everyday communication settings. An online survey consisting of close-set and open-ended questions aimed to describe the communication difficulties experienced in different communication activities (in-person and telecommunication) during the COVID-19 pandemic. 172 individuals with hearing loss and 130 who reported not having a hearing loss completed the study. They were recruited through social media, private audiology clinics, hospitals and monthly newsletters sent by the non-profit organisation “Audition Quebec.” Face masks were the most problematic protective measure for communication in 75–90% of participants. For all in-person communication activities, participants with hearing loss reported significantly more impact on communication than participants with normal hearing. They also exhibited more activity limitations and negative emotions associated with communication difficulties. These results suggest that, in times of pandemic, individuals with hearing loss are more likely to exhibit communication breakdowns in their everyday activities. This may lead to social isolation and have a deleterious effect on their mental health. When interacting with individuals with hearing loss, communication strategies to optimise speech understanding should be used.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".