Development of Competency Criteria for Real-Ear Measurement: Findings From a Modified E-Delphi Round 1
Bibliographic record
Abstract
BACKGROUND: Hearing aid verification ensures that appropriate audibility and access to the speech signal are provided to the hearing aid user. Best practice guidelines recommend evidence-based verification measures (on-ear real-ear aided response [REAR] and simulated REAR) to match the hearing aid output with prescribed targets. Skill development for measuring and interpreting REAR involves six stages: novice, advanced, beginner, competent, proficient, and expert. At the competent stage, trainees can perform entrustable professional activities independently, demonstrating sufficient competence for unsupervised practice. PURPOSE: The purpose of this study was to obtain consensus on key competency criteria expected for audiologists performing hearing aid verification across the lifespan to ensure effective real-ear performance evaluation. METHOD: for each competency. An a priori threshold of 70% was required for consensus. RESULTS: Twenty-nine expert audiologists completed the first survey round. Consensus was achieved for 52 out of the 54 competency items. Competencies related to calibration, equipment setup, and interpretation of REAR had over 90% agreement. Items not reaching initial consensus were revised for Round 2 based on expert feedback. CONCLUSIONS: Results from Round 1 and the new consolidated items will be shared in Round 2. This study identifies key competencies for audiologists to perform on-ear and simulated REAR, forming the basis for knowledge, training, and improved clinical outcomes.
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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 | 0.085 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".