The Design and Evaluation of a Simulation Tool for Audiology Screening Education: Design Science Approach
Bibliographic record
Abstract
BACKGROUND: The early identification of hearing loss and ear disorders is important. Regular screening is recommended for all age groups to determine whether a full hearing assessment is necessary and allow for timely treatment of hearing problems. Procedural training is needed for new speech-language pathology students as well as continuing education for those trained to perform this screening procedure. Limited availability and access to physical training locations can make it difficult to receive the needed training. OBJECTIVE: The aims of this study were to (1) develop a new hearing screening simulation software platform and (2) assess its effectiveness in training a group of graduate-level speech-language pathology students in hearing screening procedures. METHODS: An audiology simulator modeled after the commercial Grason-Stadler GSI39 combination audiometer and tympanometer device was developed to serve as a precursor to traditional face-to-face clinical instruction. A description of the simulator development process, guided by a design science approach, is presented. The initiation phase established the initial criteria for the simulator design. This was followed by an iterative process involving prototype development, review, and critique by the clinical faculty. This feedback served as input for the subsequent iteration. The evaluation of the final prototype involved 33 speech-language pathology graduate students as part of an introductory audiology class. These students were randomly assigned to control (receiving in-person instruction) and test (in-person instruction and simulation tool use) groups. Students in both groups were subsequently evaluated as they performed audiology screenings on human participants and completed a 25-item pretest and posttest survey. Nonparametric Mann-Whitney U tests were conducted on the mean differences between pretest and posttest ordinal survey response data to compare the control and intervention groups. RESULTS: The results indicated that the students who used the simulation tool demonstrated greater confidence in their ability to (1) explain hearing screening procedures to a child (P=.02), (2) determine whether otoscopy results are normal (P=.02), and (3) determine whether otoscopy results are abnormal (P=.03). Open-ended responses indicated that the students found that the hands-on experience provided by the simulator resulted in an easy-to-use and useful learning experience with the audiometer, which increased their confidence in their ability to perform hearing screenings. CONCLUSIONS: Software-based education simulation tools for audiology screening may provide a beneficial approach to educating students and professionals in hearing screening training. The tool tested in this study supports individualized, self-paced learning with context-sensitive feedback and performance assessment, incorporating an extensible approach to supporting simulated subjects.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".