Clinician-developed modifiable script to support informational counselling during real-ear measurements
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to have experienced audiologists identify the key ideas to convey to clients about real-ear measurement (REM) verification and create a modifiable script to use for REM informational counselling. DESIGN: A modified concept mapping design was used. STUDY SAMPLE: Seventeen experienced audiologists participated in one or more research activities: brainstorming individual counselling statements; rating statements on importance and feasibility; and discussing in a focus group how the statements can be used in a counselling script. RESULTS: Brainstorming generated 227 statements. Researchers cleaned, combined, and recirculated the statements to participants for further ideas, resulting in 136 statements for sorting and rating. Sorting yielded eight categories: Other uses of the hearing aid (HA) test system (11 statements); Purpose of REM (24 statements); Description of procedure (14 statements); Personalisation of hearing aids (14 statements); Setting expectations (21 statements); Speech map stimulus levels (17 statements); Client preparation (19 statements); and On-screen orientation (16 statements). After rating and discussion in the focus group, the final script included 57 statements. CONCLUSIONS: This study identified the key ideas to convey during REM informational counselling and produced a modifiable script that individuals can use when conducting REM.
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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 | 0.019 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".