Hybrid Usability Analysis to Improve an Educational Telemedicine Simulation: Task Analysis and Survey Results
Bibliographic record
Abstract
Medical educators frequently use simulations with standardised patients in their curriculum to expose learners to high-stakes scenarios in a safe, monitored context. It can be challenging to ensure a standardised experience for learners and provide consistent opportunities for faculty to measure competencies before piloting with target learners. We, therefore, designed a mixed-methods evaluation instrument based on our work conducting usability tests with health information technologies. We gathered quantitative data on task completion rates, competency assessment rates, and user perceptions of the task. We also gathered qualitative information on usability issues. Half of the testers did not complete the telehealth safety checks, and one tester did not complete an audio/visual cross-check. These issues interfered with the faculty assessment of three competencies: clinical data collection, proper equipment use, and meeting professional standards. We used testers' qualitative feedback to identify easy improvements that we plan to test another round of testers. We believe the method illustrated here is an easily reproducible approach that clinician educators can adapt for various medical education simulations.
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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 | 0.031 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".