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.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".