Simulation-based crisis resource management training for pediatric critical care medicine
Bibliographic record
Abstract
OBJECTIVE: To review the essential elements of crisis resource management and provide a resource for instructors by describing how to use simulation-based training to teach crisis resource management principles in pediatric acute care contexts. DATA SOURCE: A MEDLINE-based literature source. OUTLINE OF REVIEW: This review is divided into three main sections: Background, Principles of Crisis Resource Management, and Tools and Resources. The background section provides the brief history and definition of crisis resource management. The next section describes all the essential elements of crisis resource management, including leadership and followership, communication, teamwork, resource use, and situational awareness. This is followed by a review of evidence supporting the use of simulation-based crisis resource management training in health care. The last section provides the resources necessary to develop crisis resource management training using a simulation-based approach. This includes a description of how to design pediatric simulation scenarios, how to effectively debrief, and a list of potential assessment tools that instructors can use to evaluate crisis resource management performance during simulation-based training. CONCLUSION: Crisis resource management principles form the foundation for efficient team functioning and subsequent error reduction in high-stakes environments such as acute care pediatrics. Effective instructor training is required for those programs wishing to teach these principles using simulation-based learning. Dissemination and integration of these principles into pediatric critical care practice has the potential for a tremendous impact on patient safety and outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".