Factors in Client–Clinician Interaction That Influence Hearing Aid Adoption
Bibliographic record
Abstract
The influence of client-clinician interactions has not been emphasized in hearing health care, despite the extensive evidence of the impact of the provider-patient interaction on health outcomes. The purpose of this study was to identify factors in the client-clinician interaction that may influence hearing aid adoption. Thirteen adults who had received a hearing aid recommendation within the previous 3 months and 10 audiologists participated in a study to generate, sort, and rate the importance of factors in client-clinician interaction that may influence the hearing aid purchase decision. A concept mapping approach was used to define meaningful clusters of factors. Quantitative analysis and qualitative interpretation of the statements resulted in eight concepts. The concepts in order of their importance are (a) Ensuring client comfort, (b) Understanding and meeting client needs, (c) Client-centered traits and actions, (d) Acknowledging client as an individual, (e) Imposing undue pressure and discomfort, (f) Conveying device information by clinician, (g) Supporting choices and shared decision making, and (h) Factors in client readiness. Two overarching themes of client-centered interaction and client empowerment were identified. Results highlight the influence of the client-clinician interaction in hearing aid adoption and suggest the possibility of improving hearing aid adoption by empowering clients through a client-centered interaction.
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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 | 0.010 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".