An Exploration of Audiologists' Readiness to Adopt Connected Hearing Healthcare for Remote Hearing Aid Fitting
Bibliographic record
Abstract
Background: Globally, the increasing prevalence of hearing loss and need for improved access to hearing healthcare services, highlights the growing need for alternative service delivery models. A Connected Health model emerges as a solution for this need, focusing on the use of telecommunication technologies. This model, extended to audiology, can help to better ‘connect’ a patient to their own care process and to their provider during audiological diagnostics, treatment, and management services, at a distance and in an effective and timely manner. The strong capacity for and underutilization of Connected Audiology within current aural (re)habilitation service models have led to research around the “readiness” factors that are contributing to a low uptake of remote services within Canada.\nObjective: This survey-based study aimed to describe audiologists’ readiness to adopt Connected Audiology for remote hearing aid fitting using a modified framework for eHealth readiness.\nMethods: An analytic, cross-sectional quantitative survey called the Connected Audiology Readiness Evaluation (C.A.R.E.) was conducted using online data collection methods. Practicing audiologists, across Canada, were recruited via professional networks/associations to identify the main factors associated with clinician readiness to adopt remote hearing aid fitting services into clinical practice.\nResults: Reported readiness levels around the implementation of Connected Audiology displayed across the 8 CARE dimensions are as follows. High readiness levels are reported for the following dimensions: practice context, social capital, patient-provider relationship, organizational support and attitude; average readiness levels are reported for the access and aptitude dimensions; and low readiness for the standards dimension with a high need for the development and implementation of guidance documents to support implementation.\nConclusion: Findings from this survey will inform researchers, clinicians and policymakers of the main areas needing support for the uptake of Connected Audiology, guiding future planning, development, and implementation efforts. In addition, findings from this study can help guide Canadian audiologists in the integration of remote hearing aid fitting services into routine clinical practices.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".