Simulation in healthcare: A taxonomy and a conceptual framework for instructional design and media selection
Bibliographic record
Abstract
BACKGROUND: Simulation in healthcare lacks a dedicated framework and supporting taxonomy for instructional design (ID) to assist educators in creating appropriate simulation learning experiences. AIMS: This article aims to fill the identified gap. It provides a conceptual framework for ID of healthcare simulation. METHODS: The work is based on published literature and authors' experience with simulation-based education. RESULTS: The framework for ID itself presents four progressive levels describing the educational intervention. Medium is the mode of delivery of instruction. Simulation modality is the broad description of the simulation experience and includes four modalities (computer-based simulation, simulated patient (SP), simulated clinical immersion, and procedural simulation) in addition to mixed, hybrid simulations. Instructional method describes the techniques used for learning. Presentation describes the detailed characteristics of the intervention. The choice of simulation as a learning medium is based on a matrix of simulation relating acuity (severity) to opportunity (frequency) of events, with a corresponding zone of simulation. An accompanying chart assists in the selection of appropriate media and simulation modalities based on learning outcomes. CONCLUSION: This framework should help educators incorporate simulation in their ID efforts. It also provides a taxonomy to streamline future research and ID efforts in 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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".