Simulation-Based Neonatal Resuscitation Team Training: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Several neonatal simulation-training programs have been deployed during the last decade, and in a growing number of studies, researchers have investigated the effects of simulation-based team training. This body of evidence remains to be compiled. OBJECTIVE: We performed a systematic review of the effects of simulation-based team training on clinical performance and patient outcome. DATA SOURCES: Medline, Embase, Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library. STUDY SELECTION: Two authors included studies of team training in critical neonatal situations with reported outcomes on clinical performance and patient outcome. DATA EXTRACTION: Two authors extracted data using a predefined template and assessed risk of bias using the Cochrane risk-of-bias tool 2.0 and the Newcastle-Ottawa quality assessment scale. RESULTS: We screened 1434 titles and abstracts, evaluated 173 full texts for eligibility, and included 24 studies. We identified only 2 studies with neonatal mortality outcomes, and no conclusion could be reached regarding the effects of simulation training in developed countries. Considering clinical performance, randomized studies revealed improved team performance in simulated re-evaluations 3 to 6 months after the intervention. LIMITATIONS: Meta-analysis was impossible because of heterogenous interventions and outcomes. Kirkpatrick's model for evaluating training programs provided the framework for a narrative synthesis. Most included studies had significant methodologic limitations. CONCLUSIONS: Simulation-based team training in neonatal resuscitation improves team performance and technical performance in simulation-based evaluations 3 to 6 months later. The current evidence was insufficient to conclude on neonatal mortality after simulation-based team training because no studies were available from developed countries. In future work, researchers should include patient outcomes or clinical proxies of treatment quality whenever possible.
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How this classification was reachedexpand
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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".