A Systematic Review and Meta-Analysis of Simulation in Obstetrics Training
Bibliographic record
Abstract
PURPOSE: To summarize the available data on the benefits of the use of simulation in training obstetric providers. BACKGROUND: Although simulation is being increasingly used to train obstetric providers, its overall benefit has not been systematically assessed. METHODS: A systematic search of MEDLINE was performed from May 2011 until April 2017 to identify research evaluating simulation in obstetrics. Data regarding provider type, instructional design, outcomes, and research quality, using the medical education research study quality index (MERSQI) and Newcastle-Ottawa Scale for Education (NOS-E), was abstracted. The average weighted Hedges g effect size was calculated. RESULTS: 1423 articles were identified, of which 63 met inclusion criteria. 39 were excluded because they were 1 group pretest-posttest studies (which nearly always show positive effects), and 5 were excluded because they did not contain sufficient information to calculate effect size, leaving 19 articles in the meta-analysis. Of the selected articles 15 were randomized, 3 were nonrandomized, and 1 was a cross-over study. Provider satisfaction was assessed in 2 studies, knowledge in 1, simulated skills in 11, clinical behaviors in 4, and patient effects in 1. The average MERSQI and NOS-E quality scores were 13.3±23 (out of 18) and 5±0.8 (out of 6), respectively. The weighted average effect size for the 19 studies was 0.71 (95% CI, 0.58-0.83). DISCUSSION: Simulation training in obstetrics is associated with moderate effects in provider satisfaction, knowledge, skills, behaviors, and impact on patients. The effects sizes described here were slightly smaller and methodological quality slightly higher compared to previous meta-analyses in Anesthesiology and Emergency Medicine.
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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 | 0.021 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".