Remote Hearing Aid Support: The Next Frontier
Bibliographic record
Abstract
BACKGROUND: In an effort to reduce health-care disparities, there has been a recent surge of interest in the remote provision of care. Audiologists have begun to provide screening, diagnostic, and rehabilitative services via telehealth technologies. PURPOSE: To evaluate the feasibility and perceived benefits of providing remote hearing aid follow-up appointments in a controlled clinical environment and in participants' homes. RESEARCH DESIGN: A descriptive quasi-experimental study was completed. STUDY SAMPLE: The study consisted of two phases. The in-clinic phase included 50 adults with hearing loss who participated in remote hearing aid follow-up appointments at Vanderbilt University Medical Center. A subgroup of 21 adults from the original in-clinic phase plus one additional participant completed the in-home appointments. DATA COLLECTION AND ANALYSIS: All participants completed the Montreal Cognitive Assessment and study-designed questionnaires. All participants were asked to install proprietary distance support (DS) client software on a laptop or desktop computer and participate in hearing aid follow-up appointments. RESULTS: The majority of participants in both groups installed the DS client software with no assistance other than written instructions, and indicated a preference for DS appointments over face-to-face appointments. CONCLUSION: On average, participants and the study audiologist were satisfied with remote hearing aid follow-up visits. Additional support might be needed for older patients with little confidence in their ability to interact with technology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".