Consumer Perspectives for a Future Mobile App to Document Real-World Listening Difficulties: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: By enabling individuals with hearing loss to collect their own hearing data in their personal real-world settings, there is scope to improve clinical care, empower consumers, and support shared clinical decision-making and problem-solving. Clinician support for this approach has been established in a separate study. OBJECTIVE: This study aims to explore, for consumers with hearing loss, their (1) experiences of listening difficulties, to identify the data an app could usefully collect; (2) preferences regarding the features of mobile apps in general; and (3) opinions on the potential value and desirable features of a yet-to-be designed app for documenting listening difficulties in real-world settings. METHODS: A total of 3 focus groups involved 27 adults who self-reported hearing loss. Most were fitted with hearing devices. A facilitator used a topic guide to generate discussion, which was video- and audio-recorded. Verbatim transcriptions were analyzed using inductive content analysis. RESULTS: Consumers supported the concept of a mobile app that would facilitate the documenting of listening difficulties in real-world settings important to the individual. Consumers shared valuable insights about their listening difficulties, which will help determine the data that should be collected through an app designed to document these challenges. This information included early indicators of hearing loss (eg, mishearing, difficulty communicating in groups and on the phone, and speaking overly loudly) and prompts to seek hearing devices (eg, spousal pressure and the advice or example provided by others, and needing to rely on lipreading or to constantly request others to repeat themselves). It also included the well-known factors that influence listening difficulties (eg, reverberation, background noise, group conversations) and the impacts and consequences of their difficulties (eg, negative impacts on relationships and employment, social isolation and withdrawal, and negative emotions). Consumers desired a visual-based app that provided options for how data could be collected and how the user could enter data into an app, and which enabled data sharing with a clinician. CONCLUSIONS: These findings provide directions for the future co-design and piloting of a prototype mobile app to provide data that are useful for increasing self-awareness of listening difficulties and can be shared with a clinician.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".