Are Pediatric Emergency Physicians More Knowledgeable and Confident to Respond to a Pediatric Disaster after an Experiential Learning Experience?
Bibliographic record
Abstract
OBJECTIVES: Pediatric hospital disaster responders must be well-trained and prepared to manage children in a mass-casualty incident. Simulations of various types have been the traditional way of testing hospital disaster plans and training hospital staff in skills that are used in rare circumstances. The objective of this longitudinal, survey-based, observational study was to assess the effect of disaster response and management-based experiential learning on the knowledge and confidence of advanced learners. METHODS: A simulation-based workshop was created for practicing Pediatric Emergency Medicine (PEM) physicians, senior PEM physicians, and critical care and pediatric surgery residents to learn how to manage a disaster response. Given that this particular group of learners had never been exposed to such a disaster simulation, its educational value was assessed with the goal of improving the quality of the hospital pediatric medical response to a disaster by increasing the responders' knowledge and confidence. Objective and subjective measures were analyzed using both a retrospective, pre-post survey, as well as case-based evaluation grids. RESULTS: The simulation workshop improved the learners' perceived ability to manage patients in a disaster context and identified strengths and areas needing improvement for patient care within the disaster context. CONCLUSION: Advanced learners exposed to an experiential learning activity believed that it improved their ability to manage patients in a disaster situation and felt that it was valuable to their learning. Their confidence was preserved six months later. Bank I , Khalil E . Are pediatric emergency physicians more knowledgeable and confident to respond to a pediatric disaster after an experiential learning experience? Prehosp Disaster Med. 2016;31(5):551-556.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".