Development and initial evaluation of the Hearing Aid Attribute and Feature Importance Evaluation (HAFIE) questionnaire
Bibliographic record
Abstract
OBJECTIVES: To develop and validate a novel questionnaire aimed at providing a structured, evidence-based methodology for hearing aid recommendation and selection using self-reported importance ratings for different modern hearing aid features. DESIGN: = 10) were conducted to assess questionnaire content and design, and to guide modifications. Validation of this initial 34-item version of the questionnaire was conducted using an anonymous online survey tool (Qualtrics). Exploratory factor analysis was used to assess the factor structure of the dataset, using principal axis factoring. Questionnaire reliability and inter-item correlation were assessed. Items with low factor loading and high cross-loading were removed. STUDY SAMPLE: Two hundred and eighteen adult participants with a self-reported hearing loss (median age = 48 years, range = 18-95 years) completed the questionnaire. RESULTS: Analysis and item removal resulted in a 28-item questionnaire. Three factors were identified, dividing the hearing aid features into the subscales: "Advanced connectivity & streaming", "Physical attributes & usability", and "Sound quality & intelligibility". CONCLUSION: This study has resulted in a patient-oriented questionnaire that allows clinicians to gather patient input in a structured manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".