Evaluating a Theoretically Informed and Cocreated Mobile Health Educational Intervention for First-Time Hearing Aid Users: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Adults living with hearing loss have highly variable knowledge of hearing aids, resulting in suboptimal use or nonuse. This issue can be addressed by the provision of high-quality educational resources. OBJECTIVE: This study aims to assess the everyday experiences of first-time hearing aid users when using a newly developed, theoretically informed cocreated mobile health (mHealth) educational intervention called m2Hear. This intervention aims to deliver greater opportunities for individualization and interactivity compared with our previously developed multimedia intervention, C2Hear. METHODS: A total of 16 first-time hearing aid users trialed m2Hear for a period of 10-weeks in their everyday lives, after which individual semistructured interviews were completed. The data were analyzed using an established deductive thematic analysis procedure underpinned by the Capability, Opportunity, Motivation-Behavior model. The model stipulates that to engage in a target behavior, an individual must have physical and psychological capability, physical and social opportunity, and automatic and reflective motivation. RESULTS: Capability-m2Hear was viewed as a concise and comprehensive resource, suitable for a range of digital literacy skills. It was stated that m2Hear could be conveniently reused to provide useful reminders that facilitate knowledge of hearing aids and communication. Opportunity-m2Hear was simple and straightforward to use, enabling greater individualization and independence. The availability of m2Hear via mobile technologies also improved accessibility. Motivation-m2Hear provided greater support and reassurance, improving confidence and empowering users to self-manage their hearing loss. CONCLUSIONS: Overall, this qualitative study suggests that m2Hear supports first-time hearing aid users to successfully self-manage their hearing loss postfitting. Furthermore, this study demonstrates the utility of employing a combined theoretical and ecologically valid approach in the development of mHealth educational resources to meet the individual self-management needs of adults living with hearing loss. TRIAL REGISTRATION: ClinicalTrials.gov NCT03136718; https://clinicaltrials.gov/ct2/show/NCT03136718.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".