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Record W2790399109 · doi:10.3766/jaaa.17114

Face and Content Validity of a Probe Tube Placement Training Simulator

2018· article· en· W2790399109 on OpenAlexaff
Robert Koch, Sheila Moodie, Paula Folkeard, Susan Scollie, Conner Janeteas, Sumit Agrawal, Hanif M. Ladak

Bibliographic record

VenueJournal of the American Academy of Audiology · 2018
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsCimetrix Solutions (Canada)Western University
Fundersnot available
KeywordsTube (container)SimulationPracticumMedical physicsComputer scienceFace validityContent validityAudiologyMedicineMedical educationEngineeringMechanical engineeringPsychometrics

Abstract

fetched live from OpenAlex

BACKGROUND: Probe tube placement is an important skill audiologists must learn to make real-ear measurements in an audiology clinic. With current evidence-based guidelines recommending insertion of the probe tube within 5 mm of the tympanic membrane (TM) for proper acoustical measurements, students must be well trained to ensure they are capable to perform this placement in clinical practice. This is not always the case as it has been found that real-ear measurements are not performed in a clinic as often as required. To address this, a simulator consisting of a 3D-printed ear model and an optical tracking system was developed to provide a training system for students to practice probe tube placement and to provide a method to evaluate competency before starting clinical practicum placements. Two simulators were developed, an adult model and a pediatric model. PURPOSE: To assess the face and content validity of the two probe tube placement simulators (adult and pediatric) and define barriers and facilitators to implementing this system into an educational setting. RESEARCH DESIGN: Participants followed the setup and operating instructions designed to guide them through each functionality of the simulator. A questionnaire was used to assess face and content validity, applicability to an educational setting, and to determine perceived barriers and facilitators to using the probe tube simulators for training purposes. Five additional probe tube placements with each simulator were performed in which distance-to-TM was recorded. STUDY SAMPLE: Twelve participants with significant probe tube placement experience. DATA COLLECTION AND ANALYSIS: Participants rated each question in the questionnaire from 0% to 100% depending on their level of agreement. Averages and standard deviations (SDs) were compiled and presented for each section (face validity, content validity, and applicability to an educational setting). Final facilitators and barriers for the simulator were compiled and the top answers of each are presented. The five quantitative probe tube placement measurements for each participant were averaged, SDs were calculated, and contacts with the TM while placing the probe tube were recorded. RESULTS: The average face validity score over all questions for the adult model was 65% (SD = 18.2) whereas the pediatric model received a score of 64% (16.4). The overall content validity average score was 78.7% (17) and applicability to an educational setting had an average score of 80% (5.33). The average distance-to-TM across all trials and participants was 3.74 mm (1.82) for the adult model and 2.77 mm (0.94) for the pediatric model with only one participant exceeding the recommended maximum of 5 mm. Listed shortcomings of the current simulator included realism of the 3D-printed ear, ease of insertion of an otoscope tip into the ear, ability to visualize the ear canal "landmarks" and the TM, and foam tip insertion experience. CONCLUSIONS: Results were generally very positive for the simulator, and future iterations will look to improve the flexibility and texture of the ear, as well as the otoscopic view of the ear canal and TM.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.134
GPT teacher head0.343
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations6
Published2018
Admission routes1
Has abstractyes

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