Virtual patient activity patterns for clinical learning
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".