Comparative effectiveness of instructional design features in simulation-based education: Systematic review and meta-analysis
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Bibliographic record
Abstract
BACKGROUND: Although technology-enhanced simulation is increasingly used in health professions education, features of effective simulation-based instructional design remain uncertain. AIMS: Evaluate the effectiveness of instructional design features through a systematic review of studies comparing different simulation-based interventions. METHODS: We systematically searched MEDLINE, EMBASE, CINAHL, ERIC, PsycINFO, Scopus, key journals, and previous review bibliographies through May 2011. We included original research studies that compared one simulation intervention with another and involved health professions learners. Working in duplicate, we evaluated study quality and abstracted information on learners, outcomes, and instructional design features. We pooled results using random effects meta-analysis. RESULTS: From a pool of 10,903 articles we identified 289 eligible studies enrolling 18,971 trainees, including 208 randomized trials. Inconsistency was usually large (I2 > 50%). For skills outcomes, pooled effect sizes (positive numbers favoring the instructional design feature) were 0.68 for range of difficulty (20 studies; p < 0.001), 0.68 for repetitive practice (7 studies; p = 0.06), 0.66 for distributed practice (6 studies; p = 0.03), 0.65 for interactivity (89 studies; p < 0.001), 0.62 for multiple learning strategies (70 studies; p < 0.001), 0.52 for individualized learning (59 studies; p < 0.001), 0.45 for mastery learning (3 studies; p = 0.57), 0.44 for feedback (80 studies; p < 0.001), 0.34 for longer time (23 studies; p = 0.005), 0.20 for clinical variation (16 studies; p = 0.24), and -0.22 for group training (8 studies; p = 0.09). CONCLUSIONS: These results confirm quantitatively the effectiveness of several instructional design features in simulation-based education.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it