Simulation technology in training students, residents and faculty
Bibliographic record
Abstract
PURPOSE OF REVIEW: We provide an overview of the developments in medical education and assessment using high-fidelity simulation. Both descriptive and research papers recently published in the English language are included in this review. RECENT FINDINGS: The majority of articles reviewed are descriptive in nature, outlining the use of simulation for various educational purposes in undergraduate, postgraduate and continuing medical education. Some articles focus on the use of simulation for the acquisition of technical skills in different surgical disciplines using part-task simulation. Other disciplines such as emergency medicine, critical care, paediatrics and nursing have also contributed to the literature in this area. Very little research in the area of simulation is evident in the literature addressing the actual value or the reliability and validity of high-fidelity simulation as an evaluation tool during this time period. A strong interest in decreasing human error and the improvement in patient safety may indicate the future direction of high-fidelity simulation. SUMMARY: Simulation is receiving increasing support as an educational tool and in its use for evaluation purposes. Research into this area is still somewhat limited. As the research impetus increases in the future, we may see simulation as a major focus in all disciplines with respect to its use in the improvement of patient safety. Team training, including both personality and attitudinal issues similar to those performed in other high hazard industries, may become increasingly evident in the literature in the coming decade.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".