The Effectiveness of Medical Simulation in Teaching Medical Students Critical Care Medicine
Bibliographic record
Abstract
STATEMENT: We aimed to assess effectiveness of simulation for teaching medical students critical care medicine and to assess which simulation methods were most useful. We searched AMED, EMBASE, MEDLINE, Education Resources Information Centre, British Education Index, Australian Education Index, and bibliographies and citations, in July 2013. Randomized controlled trials comparing effectiveness of simulation with another educational intervention, or no teaching, for teaching medical students critical care medicine were included. Assessments for inclusion, quality, and data extraction were duplicated and results were synthesized using meta-analysis.We included 22 randomized control trials (n = 1325). Fifteen studies comparing simulation with other teaching found simulation to be more effective [standardized mean difference (SMD) = 0.84; 95% confidence interval (CI) = 0.43 to 1.24; P < 0.001; I = 89%]. High-fidelity simulation was more effective than low-fidelity simulation, and subgrouping supported high-fidelity simulation being more effective than other methods. Simulation improved skill acquisition (SMD = 1.01; 95% CI = 0.49 to 1.53) but was no better than other teaching in knowledge acquisition (SMD = 0.41; 95% CI = -0.09 to 0.91).
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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 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".