Skill Transference of a Probe-Tube Placement Training Simulator
Bibliographic record
Abstract
BACKGROUND: Probe-tube placement is a necessary step in hearing aid verification which needs ample hands-on experience and confidence before performing in clinic. To improve the methods of training in probe-tube placement, a manikin-based training simulator was developed consisting of a 3D-printed head, a flexible silicone ear, and a mounted optical tracking system. The system is designed to provide feedback to the user on the depth and orientation of the probe tube, and the time required to finish the task. Although a previous validation study was performed to determine its realism and teachability with experts, further validation is required before implementation into educational settings. PURPOSE: This study aimed to examine the skill transference of a newly updated probe-tube placement training simulator to determine if skills learned on this simulator successfully translate to clinical scenarios. RESEARCH DESIGN: All participants underwent a pretest in which they were evaluated while performing a probe-tube placement and real-ear-to-coupler difference (RECD) measurement on a volunteer. Participants were randomized into one of two groups: the simulator group or the control group. During a two-week training period, all participants practiced their probe-tube placement according to their randomly assigned group. After two weeks, each participant completed a probe-tube placement on the same volunteer as a posttest scenario. STUDY SAMPLE: Twenty-five novice graduate-level student clinicians. DATA COLLECTION AND ANALYSIS: Participants completed a self-efficacy questionnaire and an expert observer completed a questionnaire evaluating each participant's performance during the pre- and posttest sessions. RECD measurements were taken after placing the probe tube and foam tip in the volunteer's ear. Questionnaire results were analyzed through nonparametric t-tests and analysis of variance, whereas RECD results were analyzed using a nonlinear mixed model method. RESULTS: Results suggested students in the simulator group were less likely to contact the tympanic membrane when placing a probe tube, appeared more confident, and had better use of the occluding foam tip, resulting in more improved RECD measurements. CONCLUSIONS: The improved outcomes for trainees in the simulator group suggest that supplementing traditional training with the simulator provides useful benefits for the trainees, thereby encouraging its usage and implementation in educational 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".