HEARING AND MEMORY DEFICITS IN OLDER ADULTS USING TELEHEALTH
Bibliographic record
Abstract
It is well known that hearing abilities decline with age. Older adults living in rural communities often have to rely on Telehealth to access specialized healthcare. Telehealth systems rely on audio compression algorithms to facilitate transmission of acoustic information through the internet/telephone lines, resulting in degraded speech signals. When older adults hear acoustically degraded speech they have more difficulty remembering the content of these sentences after a delay compared to younger adults. The proposed reason for this deficit is that older adults use more cognitive resources to understand degraded speech compared to younger adults, which draws cognitive resources away from the memory system. Forgetting what was said during a telehealth session can have significant health consequences for older adults. Thus, the goal of the present study was to assess the impact of hearing abilities on the ability to retain information provided during telehealth sessions in older adult users of Telehealth in Newfoundland and Labrador. Participants completed a 29-item self-report questionnaire, including the Hearing Screening Inventory (HSI), which has a high correlation with pure-tone audiometry. Two groups were created: (1) Normal-Hearing, and (2) Hearing-Loss; based on the 25 dB HL cutoff scores from the HSI. Groups did not differ in age, gender or education. The Hearing-Loss group reported significantly more difficulty remembering health information provided during Telehealth sessions compared to the normal-hearing group. More importantly, this effect remained significant when controlling for self-reported memory ability over the past year and for self-reported ability to hear the health-care provider during Telehealth sessions. Overall, this pattern of results suggests that participants in the hearing-loss group had a specific difficulty remembering information provided over a Telehealth system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".