Virtual Patients: ED-2 Band-Aid or Valuable Asset in the Learning Portfolio?
Bibliographic record
Abstract
The challenge of planning a clinical clerkship curriculum is to create order from chaos. Fortunately, the Liaison Committee for Medical Education has thrown clerkship directors a lifeline by recognizing simulated learning experiences--including virtual patients--as equivalents to real-life clinical encounters for accreditation purposes. Although virtual patients offer a more consistent and learner-centered curriculum that provides greater practice opportunities and reduces the demand for busy clinical preceptors, going virtual does involve potential risks. Here, the authors discuss some of the pros and cons of virtual patients, especially the concerns that virtual learning experiences may not produce effective feedback and that learning may not transfer from the virtual to the clinical environment. To match teaching to different learning needs, the authors propose "adaptive feedback" whereby learners choose from three levels of feedback: seeing the correct diagnosis and patient outcomes, seeing an expert "trace," and/or meeting with their preceptor to discuss the case. Medical educators can facilitate automatic transfer of learning from the virtual to the clinical setting by making all aspects of the learning and retrieval environments as similar as possible and by integrating the virtual and clinical environments--thus sparing learners the burden of "forward reaching" transfer and providing an anchor for virtual learning experiences. Medical educators can promote intentional transfer of learning if they make the virtual learning environment both the place students practice their skills before clinical encounters and the place to which they return after clinical encounters to reflect on and improve their skills.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".