A Qualitative Case Study of Smartphone-Connected Hearing Aids: Influences on Patients, Clinicians, and Patient‐Clinician Interactions
Notice bibliographique
Résumé
BACKGROUND: Innovations in hearing aid technology influence clinicians and individuals who use hearing aids. Little research, to date, explains the innovation adoption experiences and perspectives of clinicians and patients, which matter to a field like audiology, wherein technology innovation is constant. By understanding clinician and patient experiences with such innovations, the field of audiology may develop technologies and ways of practicing in a manner more responsive to patients' needs, and attentive to society's influence. PURPOSE: The authors aimed to understand how new innovations influence clinician and patient experiences, through a study focusing on connected hearing aids. "Connected" refers to the wireless functional connection of hearing aids with everyday technologies like mobile phones and tablets. RESEARCH DESIGN: The authors used a qualitative collective case study methodology, borrowing from constructivist grounded theory for data collection and analysis methods. Specifically, the authors designed a collective case study of a connected hearing aid and smartphone application, composed of two cases of experience with the innovation: the case of clinician experiences, and the case of patient experiences. STUDY SAMPLE: The qualitative sampling methods employed were case sampling, purposive within-case sampling, and theoretical sampling, and culminated in a total collective case n = 19 (clinician case n = 8; patient case n = 11). These data were triangulated with a supplementary sample of ten documents: relevant news and popular media collected during the study time frame. DATA COLLECTION AND ANALYSIS: The authors conducted interviews with the patients and clinicians, and analyzed the interview and document data using the constant comparative method. The authors compared their two cases by looking at trends within, between, and across cases. RESULTS: The clinician case highlighted clinicians' heuristic-based candidacy judgments in response to the adoption of the connected hearing aids into their practice. The patient case revealed patients' perceptions of themselves as technologically competent or incompetent, and descriptions of how they learned to use the new technology. Between cases, the study found a difference in the response to how the connected hearing aid changed the clinician-patient relationship. While clinicians valued the increased time they spent "getting to know" their patients, patients experienced some frustration specific to the additional troubleshooting related to Bluetooth connectivity. Across cases, there was a resounding theme of "normalization" of hearing aids via their integration with a "normal" technology (mobile phones) and general lack of concern about privacy in relation to the smartphone application and its tracking and geotagging features. Both audiologists and patients credited the connected hearing aids with increased opportunities to participate more fully in everyday life. CONCLUSIONS: The introduction of smartphone-connected hearing aids influenced the identities and candidate profiles of hearing aid users, and the nature of time spent in clinical interactions, in important and interesting ways. The influence of connected hearing aids on patient experience and audiology practice calls for continued research and clinical consideration, with implications for clinical decision-making regarding hearing aid candidacy. Further study should look critically at normalization and possible unintended stigmatizing effects of making hearing aids increasingly discreet.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,013 | 0,008 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».