More is Not Always Better in Simulation. Learners’ Evaluation of a “Chest Model”
Bibliographic record
Abstract
Abstract Background Fidelity in simulation is an important design feature. Although it is typically seen as bipolar (i.e., “high” or “low”), fidelity is actually multidimensional. There are concerns that “low fidelity” might impede the immersion of learners during simulation training. “Locally built models” are characterized by decreased cost and reduced “structural” fidelity (how the simulator looks) while satisfying “functional” fidelity (what the simulator does). Objective To 1) describe the use of a locally built chest tube model in building a mastery-based simulation curriculum and 2) describe evaluation of the model from learners in different stages and contexts. Methods The model was built on the basis of key functional features of the assigned training task. A curriculum that combined progressive difficulty and opportunities for deliberate practice and mastery was developed. An analysis of the learner’s survey responses was performed using SAS studio (SAS Software). Results We describe the process of creating the chest tube model and a curriculum in which the model is used for increasing levels of difficulty to reach skill mastery. Learners at different stages and in different contexts, such as practicing physicians and trainees from developed and developing countries, evaluated the model similarly. We provide validity evidence for the content, response process, and relationship with other variables when using the model in the assessment of chest tube insertion skills. Conclusion As demonstrated in our chest tube critical care medicine curriculum, the locally built models are simple to build and feasible to use. Contrary to current thinking that low-fidelity models might impede immersion in simulation training for experienced learners, the survey results show that different learners provide very similar evaluations after practicing with the model.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".