Engagement and learning in simulation: recommendations of the Simnovate Engaged Learning Domain Group
Bibliographic record
Abstract
Background Health professions education (HPE) is based on deliberate learning activities and clinical immersion to achieve clinical competence. Simulation is a tool that helps bridge the knowledge-to-action gap through deliberate learning. This paper considers how to optimally engage learners in simulation activities as part of HPE. Methods The Simnovate Engaged Learning Domain Group undertook 3 teleconferences to survey the current concepts regarding pervasive learning. Specific attention was paid to engagement in the learning process, with respect to fidelity, realism and emotions, and the use of narratives in HPE simulation. Results This paper found that while many types of simulation exist, the current ways to categorise the types of simulation do not sufficiently describe what a particular simulation will entail. This paper introduces a novel framework to describe simulation by deconstructing a simulation activity into 3 core characteristics (scope, modality and environment). Then, the paper discusses how engagement is at the heart of the learning process, but remained an understudied phenomenon with respect to HPE simulation. Building on the first part, a conceptual framework for engaged learning in HPE simulation was derived, with potential use across all HPE methods. Discussion The framework considers how the 3 characteristics of simulation interplay with the dimensions of fidelity (physical, conceptual and emotional), and how these can be conveyed by and articulated through beauty (as a proxy for efficiency) as coexisting factors to drive learner engagement. This framework leads to the translation of deliberately taught knowledge, skills and attitudes into clinical competence and subsequent performance.
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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 | 0.069 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".