The Role of Simulation in Continuing Medical Education for Acute Care Physicians
Bibliographic record
Abstract
OBJECTIVES: We systematically reviewed the effectiveness of simulation-based education, targeting independently practicing qualified physicians in acute care specialties. We also describe how simulation is used for performance assessment in this population. DATA SOURCES: Data source included: DataMEDLINE, Embase, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Database of Controlled Trials, and National Health Service Economic Evaluation Database. The last date of search was January 31, 2013. STUDY SELECTION: All original research describing simulation-based education for independently practicing physicians in anesthesiology, critical care, and emergency medicine was reviewed. DATA EXTRACTION: Data analysis was performed in duplicate with further review by a third author in cases of disagreement until consensus was reached. Data extraction was focused on effectiveness according to Kirkpatrick's model. For simulation-based performance assessment, tool characteristics and sources of validity evidence were also collated. DATA SYNTHESIS: Of 39 studies identified, 30 studies focused on the effectiveness of simulation-based education and nine studies evaluated the validity of simulation-based assessment. Thirteen studies (30%) targeted the lower levels of Kirkpatrick's hierarchy with reliance on self-reporting. Simulation was unanimously described as a positive learning experience with perceived impact on clinical practice. Of the 17 remaining studies, 10 used a single group or "no intervention comparison group" design. The majority (n = 17; 44%) were able to demonstrate both immediate and sustained improvements in educational outcomes. Nine studies reported the psychometric properties of simulation-based performance assessment as their sole objective. These predominantly recruited independent practitioners as a convenience sample to establish whether the tool could discriminate between experienced and inexperienced operators and concentrated on a single aspect of validity evidence. CONCLUSIONS: Simulation is perceived as a positive learning experience with limited evidence to support improved learning. Future research should focus on the optimal modality and frequency of exposure, quality of assessment tools and on the impact of simulation-based education beyond the individuals toward improved patient care.
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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 | 0.040 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".