Reconsidering Fidelity in Simulation-Based Training
Bibliographic record
Abstract
In simulation-based health professions education, the concept of simulator fidelity is usually understood as the degree to which a simulator looks, feels, and acts like a human patient. Although this can be a useful guide in designing simulators, this definition emphasizes technological advances and physical resemblance over principles of educational effectiveness. In fact, several empirical studies have shown that the degree of fidelity appears to be independent of educational effectiveness. The authors confronted these issues while conducting a recent systematic review of simulation-based health professions education, and in this Perspective they use their experience in conducting that review to examine key concepts and assumptions surrounding the topic of fidelity in simulation.Several concepts typically associated with fidelity are more useful in explaining educational effectiveness, such as transfer of learning, learner engagement, and suspension of disbelief. Given that these concepts more directly influence properties of the learning experience, the authors make the following recommendations: (1) abandon the term fidelity in simulation-based health professions education and replace it with terms reflecting the underlying primary concepts of physical resemblance and functional task alignment; (2) make a shift away from the current emphasis on physical resemblance to a focus on functional correspondence between the simulator and the applied context; and (3) focus on methods to enhance educational effectiveness using principles of transfer of learning, learner engagement, and suspension of disbelief. These recommendations clarify underlying concepts for researchers in simulation-based health professions education and will help advance this burgeoning field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.218 | 0.530 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| 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".