Fostering Adaptive Expertise Through Simulation
Bibliographic record
Abstract
Technology-enhanced simulation has been used to tackle myriad challenges within health professions education. Recently, work has typically adopted a mastery learning orientation that emphasizes trainees' sequential mastery of increasingly complex material. Doing so has privileged a focus on performance and task completion, as captured by trainees' observable behaviors and actions. Designing simulation in these ways has provided important advances to education, clinical care, and patient safety, yet also placed constraints around how simulation-based activities were enacted and learning outcomes were measured. In tracing the contemporary manifestations of simulation in health professions education, this article highlights several unintended consequences of this performance orientation and draws from principles of adaptive expertise to suggest new directions. Instructional approaches grounded in adaptive expertise in other contexts suggest that uncertainty, struggle, invention, and even failure help learners to develop deeper conceptual understanding and learn innovative approaches to novel problems. Adaptive expertise provides a new lens for simulation designers to think intentionally around how idiosyncrasy, individuality, and inventiveness could be enacted as central design principles, providing learners with opportunities to practice and receive feedback around the kinds of complex problems they are likely to encounter in practice. Fostering the growth of adaptive expertise through simulation will require a fundamental reimagining of the design of simulation scenarios, embracing the power of uncertainty and ill-defined problem spaces, and focusing on the structure and pedagogical stance of debriefing. Such an approach may reveal untapped potential within health care simulation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".