Comparing the Learning Effectiveness of Healthcare Simulation in the Observer Versus Active Role: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
STATEMENT: The benefits of observation in simulation-based education in healthcare are increasingly recognized. However, how it compares with active participation remains unclear. We aimed to compare effectiveness of observation versus active participation through a systematic review and meta-analysis. Effectiveness was defined using Kirkpatrick's 4-level model, namely, participants' reactions, learning outcomes, behavior changes, and patient outcomes. The peer-reviewed search strategy included 8 major databases and gray literature. Only randomized controlled trials were included. A total of 13 trials were included (426 active participants and 374 observers). There was no significant difference in reactions (Kirkpatrick level 1) to training between groups, but active participants learned (Kirkpatrick level 2) significantly better than observers (standardized mean difference = -0.2, 95% confidence interval = -0.37 to -0.02, P = 0.03). Only one study reported behavior change (Kirkpatrick level 3) and found no significant difference. No studies reported effects on patient outcomes (Kirkpatrick level 4). Further research is needed to understand how to effectively integrate and leverage the benefits of observation in simulation-based education in healthcare.
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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 | 0.037 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".