Virtual patient activity patterns for clinical learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Virtual patients are software tools that present learners with patient case situations and tasks. Some virtual patients take the learner through a guided case scenario, whereas others require learners to make diagnostic and therapeutic decisions. Much attention has been paid to the design of virtual patients and their use as standalone activities, but rather less attention has been paid to their use in broader educational activities. This article describes a series of activity patterns that make use of virtual patients. CONTEXT: The article describes five patterns of clinical teaching activities that make use of virtual patients: independent study activities; collaborative group activities; blended activities; bridging activities; and reference activities. These patterns were developed inductively from the authors' teaching practices over a number of years. These are not the only activity patterns and designs that can make use of virtual patients but they are ones that have been found to be particularly useful over time and in many different contexts. INNOVATION: Although the design of educational artifacts such as virtual patients is important, clinical teachers also need to consider the ways in which they are used. Different kinds of activity can employ different kinds of virtual patients of varying levels of complexity. An activity focus can allow clinical teachers to make more effective and broader use of virtual patients. IMPLICATIONS: Virtual patients can be used for more than independent study. Clinical teachers are encouraged to explore the multitude of uses that virtual patients can be put to, and the ways in which activities can be constructed around them. Different kinds of activity can employ different kinds of virtual patients, of varying levels of complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it