Comparative Effectiveness of Technology-Enhanced Simulation Versus Other Instructional Methods
Bibliographic record
Abstract
To determine the comparative effectiveness of technology-enhanced simulation, we summarized the results of studies comparing technology-enhanced simulation training with nonsimulation instruction for health professions learners. We systematically searched databases including MEDLINE, Embase, and Scopus through May 2011 for relevant articles. Working in duplicate, we abstracted information on instructional design, outcomes, and study quality. From 10,903 candidate articles, we identified 92 eligible studies. In random-effects meta-analysis, pooled effect sizes (positive numbers favoring simulation) were as follows: satisfaction outcomes, 0.59 (95% confidence interval, 0.36-0.81; n = 20 studies); knowledge, 0.30 (0.16-0.43; n = 42); time measure of skills, 0.33 (0.00-0.66; n = 14); process measure of skills, 0.38 (0.24-0.52; n = 51); product measure of skills, 0.66 (0.30-1.02; n = 11); time measure of behavior, 0.56 (-0.07 to 1.18; n = 7); process measure of behavior, 0.77 (-0.13 to 1.66; n = 11); and patient effects, 0.36 (-0.06 to 0.78; n = 9). For 5 studies reporting comparative costs, simulation was more expensive and more effective. In summary, in comparison with other instruction, technology-enhanced simulation is associated with small to moderate positive effects.
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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 | 0.035 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".