Counseling During Real Ear Measurements: The Clients' Perspective
Bibliographic record
Abstract
BACKGROUND: When dispensing hearing aids, audiologists must follow validated fitting and verification procedures to ensure that the hearing aids are properly fitted to the client's hearing. Real ear measurements (REMs) are best practice for verifying hearing aids. Prior literature regarding REMs has mainly focused on the clinicians' perspective. PURPOSE: This study investigated informational counseling throughout REMs by gathering perspectives of first-time hearing aid users regarding the content and format of counseling. RESEARCH DESIGN: The study used an interpretive description approach with focus groups. STUDY SAMPLE: There were 16 adult participants (4 males, 12 females) who were first-time hearing aid users and who all had memory of REMs occurring during their own hearing aid verification. INTERVENTION: We investigated the addition of informational counseling during REM verification. DATA COLLECTION AND ANALYSIS: Four focus groups were conducted to elicit feedback on a demonstration of informational counseling during REM hearing aid verification. The data from the focus groups were transcribed verbatim and analyzed using qualitative content analysis. RESULTS: Analysis revealed positive aspects, negative aspects, and suggested changes in relation to the verbal and visual information presented during the REM verification demonstration. These data fell into two broad categories: the interaction and transaction of informational counseling. CONCLUSION: Most clients were interested in learning more about REMs if the information was accessible. Results provide recommendations for clinical audiologists and REM system manufacturers to make the information presented during informational counseling more client-friendly and individualized for client-centered care. To continue exploring this new inquiry, further experimental research is required to determine if there is any added value of incorporating informational counseling during REMs.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".