Technology-Enhanced Simulation and Pediatric Education: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Pediatrics has embraced technology-enhanced simulation (TES) as an educational modality, but its effectiveness for pediatric education remains unclear. The objective of this study was to describe the characteristics and evaluate the effectiveness of TES for pediatric education. METHODS: This review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards. A systematic search of Medline, Embase, CINAHL, ERIC, Web of Science, Scopus, key journals, and previous review bibliographies through May 2011 and an updated Medline search through October 2013 were conducted. Original research articles in any language evaluating the use of TES for educating health care providers at any stage, where the content solely focuses on patients 18 years or younger, were selected. Reviewers working in duplicate abstracted information on learners, clinical topic, instructional design, study quality, and outcomes. We coded skills (simulated setting) separately for time and nontime measures and similarly classified patient care behaviors and patient effects. RESULTS: We identified 57 studies (3666 learners) using TES to teach pediatrics. Effect sizes (ESs) were pooled by using a random-effects model. Among studies comparing TES with no intervention, pooled ESs were large for outcomes of knowledge, nontime skills (eg, performance in simulated setting), behaviors with patients, and time to task completion (ES = 0.80-1.91). Studies comparing the use of high versus low physical realism simulators showed small to moderate effects favoring high physical realism (ES = 0.31-0.70). CONCLUSIONS: TES for pediatric education is associated with large ESs in comparison with no intervention. Future research should include comparative studies that identify optimal instructional methods and incorporate pediatric-specific issues into educational interventions.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".