Barriers to health information and health services in COVID-19 for older adults with combined vision and hearing loss
Bibliographic record
Abstract
Purpose : Older adults with combined vision and hearing loss (dual sensory loss/DSL) are often sidelined in vision and hearing research, and evidence suggests that they are at a high risk of cognitive impairment, functional decline, social isolation, falls, depression, and mortality. These consequences get exacerbated during the COVID-19 pandemic due to physical distancing restrictions on mobility and social interactions. Around one million older adults in Canada experience DSL;yet, there is very limited evidence in the Canadian context that could inform pandemic preparedness for this population. Hence, the present study identifies and describes the barriers to health information and health services access for older adults with DSL during the COVID-19 pandemic. Methods : We conducted semi-structured qualitative interviews with 11 communitydwelling older adults with DSL (age 62-85 years;7 female) in Montreal between September and December 2020. Diverse remote communication modes and accessible formats were used to obtain consent and interview participants. Interviews were audio-recorded and transcribed verbatim. Data were managed using NVivo software and analyzed using a thematic analysis approach. Results : Findings indicate that the central barriers to healthcare information and access are linked to communication breakdown between older adults with DSL and healthcare providers, in addition to the presentation of information through inaccessible formats. Furthermore, healthcare staff rarely have the additional time available that is necessary to interact with the DSL clientele or have the necessary training to accommodate their communication needs. In terms of barriers to accessibility, participants reported that they have difficulty following the 2-meter distance requirements and coloured lines painted on the floor to ensure physical distancing in the healthcare setting. Conclusions : Our results highlight that the pandemic heightened the risk for older adults with DSL because of the systemic and physical barriers to healthcare access for this population. There is a dire need for training of healthcare professionals to accommodate the communication and accessibility needs of older adults living with DSL. Healthcare administrators and policymakers should consider the distinct accessibility and communication needs of this vulnerable population in order to help them age well.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".