12 Tips: Guidelines for authoring virtual patient cases
Bibliographic record
Abstract
BACKGROUND: Virtual patient cases are an increasingly utilized and compelling pedagogical strategy for medical education informatics. They provide educators with the opportunity to develop richly layered, multidimensional teaching situations for their learners. However, 'virtual patients are notoriously difficult to author, adapt and exchange' (MedBiquitous Virtual Patient Specification, Virtual Patient Working Group 2007), and case creation can be daunting. Authors may be uncertain about the process of virtual patient case development and this can translate into ambiguity and hesitation. AIMS: This installment of the '12 tips' presents specific guidelines that are intended to provide medical educators with guidelines to facilitate the development of virtual patient cases. METHODS: These 12 tips are based upon comprehensive, research-based, theory-grounded and criterion-referenced guidelines and founded in pedagogical principles, theories of cognition, and recognition of current technology and availability of authoring applications. RESULTS: It is anticipated that the 12 tips will provide medical educators interested in authoring virtual patient cases one set of useful guidelines to facilitate the process. CONCLUSIONS: Virtual patient cases provide medical educators with an innovative tool for medical education. These guidelines will assist authors in case development.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 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".