Creation and Delphi-method Refinement of Pediatric Disaster Triage Simulations
Bibliographic record
Abstract
OBJECTIVE: There is a need for rigorously designed pediatric disaster triage (PDT) training simulations for paramedics. First, we sought to design three multiple patient incidents for EMS provider training simulations. Our second objective was to determine the appropriate interventions and triage level for each victim in each of the simulations and develop evaluation instruments for each simulation. The final objective was to ensure that each simulation and evaluation tool was free of bias toward any specific PDT strategy. METHODS: We created mixed-methods disaster simulation scenarios with pediatric victims: a school shooting, a school bus crash, and a multiple-victim house fire. Standardized patients, high-fidelity manikins, and low-fidelity manikins were used to portray the victims. Each simulation had similar acuity of injuries and 10 victims. Examples include children with special health-care needs, gunshot wounds, and smoke inhalation. Checklist-based evaluation tools and behaviorally anchored global assessments of function were created for each simulation. Eight physicians and paramedics from areas with differing PDT strategies were recruited as Subject Matter Experts (SMEs) for a modified Delphi iterative critique of the simulations and evaluation tools. The modified Delphi was managed with an online survey tool. The SMEs provided an expected triage category for each patient. The target for modified Delphi consensus was ≥85%. Using Likert scales and free text, the SMEs assessed the validity of the simulations, including instances of bias toward a specific PDT strategy, clarity of learning objectives, and the correlation of the evaluation tools to the learning objectives and scenarios. RESULTS: After two rounds of the modified Delphi, consensus for expected triage level was >85% for 28 of 30 victims, with the remaining two achieving >85% consensus after three Delphi iterations. To achieve consensus, we amended 11 instances of bias toward a specific PDT strategy and corrected 10 instances of noncorrelation between evaluations and simulation. CONCLUSIONS: The modified Delphi process, used to derive novel PDT simulation and evaluation tools, yielded a high degree of consensus among the SMEs, and eliminated biases toward specific PDT strategies in the evaluations. The simulations and evaluation tools may now be tested for reliability and validity as part of a prehospital PDT curriculum.
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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 | 0.113 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".