The Design and Evaluation of a Simulation Tool for Audiology Screening Education: Design Science Approach
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».