What is the impact of simulation‐based training for paediatric procedures on patient outcomes, cost and latent safety threats?
Bibliographic record
Abstract
BACKGROUND: Simulation-based training (SBT) provides a safe space for medical trainees to experience realistic scenarios. SBT has been found to improve trainee performance in paediatric procedures. However, limited evidence exists regarding its effects on higher-level outcomes. This scoping review aims to identify studies that investigate the impact of SBT for procedural skills on T3 (patient outcomes) and T4 level outcomes (latent safety threats [LSTs], and hospital level costs) in paediatrics. METHODS: Full-text articles were included if they focused on medical trainees, used simulation training for paediatric procedures and reported T3/T4 level outcomes. Six databases were searched from January 2011 to September 2022. Search strategies were developed with the assistance of a librarian. Three independent reviewers performed pilot screenings before title/abstract and full-text screenings. A data extraction sheet was created to gather information on interventions, outcomes, research design, and other study characteristics. FINDINGS: After title/abstract screening of 4,076 sources, 50 were included for full-text review, with 15 articles selected for data extraction. Four were randomised control studies (RCTs), fourteen focused on T3 level outcomes including mortality rates, and one measured LSTs. There were no studies reporting cost-related data. Three of the studies focused on bag-and-mask ventilation, and eight mentioned the use of mannequins. DISCUSSION: We highlight the potential effectiveness of simulation-based training of paediatric procedural skills in improving patient outcomes, such as reduced mortality rates and incidence of illness/injury. CONCLUSION: Though the quality of research designs was low, researchers used different simulation modalities and outcome measures and showed a positive impact of SBT(e.g., decreased mortality rates).
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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 | 0.043 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".