A Scoping Review of Technology and Infrastructure Needs in the Delivery of Virtual Hearing Aid Services
Bibliographic record
Abstract
PURPOSE: The digital health revolution has brought forward integral technological advancements enabling virtual care as a readily accessible delivery model. Despite this forward momentum, the field of audiology still faces barriers that impede the uptake of virtual services into routine clinical practice. The aim of this study was to gather, synthesize, and summarize the literature around virtual hearing aid intervention studies and the related technology and infrastructure requirements. METHOD: A scoping review was conducted using MEDLINE, CINAHL, Scopus, Nursing and Allied Health, and Web of Science databases. Objectives, inclusion criteria, and scoping review methods were specified in advance and documented in a protocol. RESULTS: The 11 studies identified through this review related to virtual hearing aid services delivered by a licensed health care provider and/or facilitator(s) specific to hearing aid management, programming, verification, and validation services. Service delivery models varied according to patient population, technology experience, type(s) and time course of care, type of remote location, and technology/support requirements. Barriers and facilitators to implementation-related themes including technology access and function, client sociotechnical, convenience, education and training, interaction quality, service delivery, and technology innovation. CONCLUSIONS: This scoping review provides evidence around the technology and infrastructure required for full integration of virtual hearing aid services into practice and according to care type. Low-tech versus high-tech requirements may be used to guide virtual service delivery triaging efforts. Research and development efforts in the areas of pediatrics, clinical support tools, and hearing aid/app-based solutions will support further uptake of virtual service delivery in audiology.
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 | 0.036 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".