Virtual patients as activities: exploring the research implications of an activity theoretical stance
Bibliographic record
Abstract
Virtual patients are computer-based simulators of patient encounters for the purposes of instruction, practice, and assessment. Although virtual patients have been around for some time they have yet to become part of mainstream medical education. A major reason for this would seem to be a lack of clarity as to what educational value virtual patients actually have. This paper argues that virtual patients should be seen as activities rather than artifacts and that activity theory can be used to generate different ways to frame scholarship in and around virtual patients. Drawing on the work of Leont'ev and Engeström this paper describes a range of perspectives based on the operations, actions, and objectives in and around virtual patients; the use of virtual patients to mediate activities; and the sociocultural context and the participants in virtual patient activities. This approach allows us to move beyond the 'does or does not work' discourse of much of the existing scholarship around virtual patients and, to an extent, around educational technologies as a whole. Activity perspectives, and activity theory in particular, offer new horizons for research and evaluation that address many of the limitations of intervention-based paradigms of inquiry.
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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 | 0.034 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.073 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".