Use of simulator-based medical procedural curriculum: the learner's perspectives
Bibliographic record
Abstract
BACKGROUND: Simulation is increasingly used for teaching medical procedures. The goal of this study was to assess learner preferences for how simulators should be used in a procedural curriculum. METHODS: A 26-item survey was constructed to assess the optimal use of simulators for the teaching of medical procedures in an internal medicine residency curriculum. Survey domains were generated independently by two investigators and validated by an expert panel (n = 7). Final survey items were revised based on pilot survey and distributed to 128 internal medicine residents. RESULTS: Of the 128 residents surveyed, 106 (83%) responded. Most responders felt that simulators should be used to learn technical skills (94%), refine technical skills (84%), and acquire procedural teaching skills (87%). Respondents felt that procedures most effectively taught by simulators include: central venous catheterization, thoracentesis, intubation, lumbar puncture, and paracentesis. The majority of learners felt that teaching should be done early in residency (97%). With regards to course format, 62% of respondents felt that no more than 3-4 learners per simulator and an instructor to learner ratio of 1:3-4 would be acceptable.The majority felt that the role of instructors should include demonstration of technique (92%), observe learner techniques (92%), teach evidence behind procedural steps (84%) and provide feedback (89%). Commonly cited barriers to procedural teaching were limitations in time, number of instructors and simulators, and lack of realism of some simulators. CONCLUSIONS: Our results suggest that residents value simulator-based procedural teaching in the form of small-group sessions. Simulators should be an integral part of medical procedural education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".