The minimal relationship between simulation fidelity and transfer of learning
Bibliographic record
Abstract
CONTEXT: High-fidelity simulators have enjoyed increasing popularity despite costs that may approach six figures. This is justified on the basis that simulators have been shown to result in large learning gains that may transfer to actual patient care situations. However, most commonly, learning from a simulator is compared with learning in a 'no-intervention' control group. This fails to clarify the relationship between simulator fidelity and learning, and whether comparable gains might be achieved at substantially lower cost. OBJECTIVES: This analysis was conducted to review studies that compare learning from high-fidelity simulation (HFS) with learning from low-fidelity simulation (LFS) based on measures of clinical performance. METHODS: Using a variety of search strategies, a total of 24 studies contrasting HFS and LFS and including some measure of performance were located. These studies referred to learning in three areas: auscultation skills; surgical techniques, and complex management skills such as cardiac resuscitation. RESULTS: Both HFS and LFS learning resulted in consistent improvements in performance in comparisons with no-intervention control groups. However, nearly all the studies showed no significant advantage of HFS over LFS, with average differences ranging from 1% to 2%. DISCUSSION: The factors influencing learning, and the reasons for this surprising finding, are discussed.
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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 | 0.022 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".