Feasibility study of delivering a mass casualty team training exercise virtually.
Bibliographic record
Abstract
Introduction: Virtual communication has grown in popularity due to the COVID-19 pandemic. We developed a platform to remotely deliver a scenario-based team-training to educate students and to prepare civilian hospitals for a mass-casualty event. Materials and Methods: To test feasibility, virtual training was compared with in-person delivery of the training using a cost-effectiveness analysis. Fidelity of this training’s components to decision making roles in an actual mass casualty response was the primary metric used to determine effectiveness. Costs were estimated for low, intermediate, and high-cost conditions in which in-person training could be hosted and compared to the costs estimated for virtual delivery of the training. Results: Virtual training was similar in cost to the low-cost condition, however cost burden significantly increased for intermediate and high-cost conditions. Both modalities provide fidelity where decision making is the emphasis of the exercise. Live training is more effective if a technical component dominates. For the main decision-making tasks of the curriculum, both platforms provide equivalent effectiveness. Discussion: In the low-cost condition, both formats were equivalent in cost, while in the intermediate and the high-cost conditions, virtual delivery had cost savings for both training organizers and participants. Scenario-based team-training may be delivered virtually as effectively as in-person training when decision making is emphasized. However, many skills with a technical component cannot be replicated virtually, therefore training in the future may be amenable to a hybrid model containing both virtual and in-person elements.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".