Teaching and Evaluating the Virtual Physical Exam in Telemedicine
Bibliographic record
Abstract
With the rapid adoption of telemedicine since the COVID-19 pandemic, it has become imperative to teach and evaluate health professional trainees on skills important to conducting effective virtual visits. We developed a simulation-based workshop with (1) readings, (2) a lecture covering online communication and the virtual head and neck exam, (3) a telemedicine simulation with a standardized patient observed by faculty, (4) personalized feedback from faculty, and (5) a group debrief session. We created an evaluation rubric based on three of 20 Association of American Medical Colleges (AAMC) telemedicine competencies to assess learner performance during the simulations. Students (medical and physician assistant students; n = 50), and internal medicine residents (n = 20) completed this workshop in 2023. At least 90% of trainees across the two groups were rated as approaching entrustment or entrustable in each competency. This workshop is an example of a scalable telemedicine curriculum that can be used to teach and evaluate learners in the virtual physical exam across the training continuum.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".