Face and content validity of a novel, web-based otoscopy simulator for medical education
Bibliographic record
Abstract
BACKGROUND: Despite the fact that otoscopy is a widely used and taught diagnostic tool during medical training, errors in diagnosis are common. Physical otoscopy simulators have high fidelity, but they can be expensive and only a limited number of students can use them at a given time. OBJECTIVES: 1) To develop a purely web-based otoscopy simulator that can easily be distributed to students over the internet. 2) To assess face and content validity of the simulator by surveying experts in otoscopy. METHODS: An otoscopy simulator, OtoTrain™, was developed at Western University using web-based programming and Unity 3D. Eleven experts from academic institutions in North America were recruited to test the simulator and respond to an online questionnaire. A 7-point Likert scale was used to answer questions related to face validity (realism of the simulator), content validity (expert evaluation of subject matter and test items), and applicability to medical training. RESULTS: The mean responses for the face validity, content validity, and applicability to medical training portions of the questionnaire were all ≤3, falling between the "Agree", "Mostly Agree", and "Strongly Agree" categories. The responses suggest good face and content validity of the simulator. Open-ended questions revealed that the primary drawbacks of the simulator were the lack of a haptic arm for force feedback, a need for increased focus on pneumatic otoscopy, and few rare disorders shown on otoscopy. CONCLUSION: OtoTrain™ is a novel, web-based otoscopy simulator that can be easily distributed and used by students on a variety of platforms. Initial face and content validity was encouraging, and a skills transference study is planned following further modifications and improvements to the simulator.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".