Real-world assessment of listener preference for hearing aid technology levels in socially involved situations
Bibliographic record
Abstract
Introduction Current hearing aids have an abundance of feature options and technologies. It is important to understand the clinical impact of hearing aid technology selection and how to individualize fittings to optimize hearing aid performance according to listening environment. To probe the naturalistic listening experiences researchers can use in-situ outcome measures. Survey-based real-world assessments can increase knowledge of hearing aid users' everyday scenarios, beyond the limits of lab-based scenarios. This study aimed to assess the relationship between subjective preference ratings of adult listeners and hearing aid technology level using Ecological Momentary Assessment (EMA). A secondary research question explored survey completion as a function of real-world participation in socially involved situations. Methods This study aimed to capture and assess in-the-moment listening situations and participant preference for hearing aid technology levels, using EMA through an app-based survey. Surveying was completed indoors (at home), indoors (away from home); and outdoors, and while in a listening situation with at least one communication partner. Fourteen older adults, aged 61–82 years, who were experienced bilateral hearing aids users were included in this study. Participants completed a 2-week acclimatization period wearing study-provided hearing aids, and a 2-week data collection period. In-situ surveying was used to evaluate technology-level preference in real-world listening situations with at least one communication partner. Survey data captured in-the-moment details surrounding environment, activity, and listening preference. Mixed methods were used to analyze the data, including Bayesian analyses for preference data and content analysis for text-based survey responses, including the use of the International Classification of Functioning, Disability and Health to guide activity categorization. Results Across a wide variety of categorized activities, participants demonstrated a preference for mid- to high-level hearing aid technologies when compared to the lowest level. Technology preference also varied according to reported activity location. Discussion The use of in-situ surveying provided a broader understanding of hearing aid users' listening environments when conversing with one or more communication partners and related technology preferences. EMA was found to be a feasible method of data collection for this cohort and may help guide clinical, person-centered selection of technology level.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".