Investigating the implementation of a new protocol and clinical tool designed to assess the audiological needs of individuals with hearing loss in clinical settings
Bibliographic record
Abstract
Introduction Few clinical protocols are presently available to guide hearing healthcare professionals who are responsible for conducting comprehensive audiological needs assessments with their clients. The Québec Audiological Assessment Protocol for Younger and Older Adults (QAAP-YOA) was recently developed for this purpose. This pilot study is the third phase in the development of the QAAP-YOA. Its objective was to assess the implementation of the QAAP-YOA in clinical settings. Methods Audiologists (n = 5) and adults with hearing loss (n = 29) participated in the study. Audiologists were trained to use the QAAP-YOA. Then, they administered the QAAP-YOA to clients. Needs assessment reports and QAAP-YOA clinical tools written by audiologists following these assessments were analyzed. Data related to the audiologists' workflow were collected. Individual semi-structured interviews were also conducted with participants to explore their experience. Results Audiologists judged the QAAP-YOA relevant and useful. It allowed them to perform more comprehensive and consistent needs assessments, and to formulate more adequate recommendations. Compliance ratings for assessment reports were higher after training (p < 0.001), particularly when the clinical tool was used. Participants were satisfied with the QAAP-YOA, but longer appointments and additional time for record keeping was required to implement it. Conclusions Audiologists can benefit from using the QAAP-YOA. Digitalizing the clinical tool may help reduce the time required to administer the procedure, facilitate its use and allow for possibility of adapting the protocol to specific clientele and work settings.
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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 | 0.229 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".