EMT Simulation-Based Team Training: Converting TEAMS 3.0 to a Virtual Format
Bibliographic record
Abstract
Background/Introduction: Effective disaster response requires standardized training of Emergency Medical Teams (EMTs). The TEAMS 3.0 training initiative, initially designed for in-person training, has been shown to significantly improve team efficacy. However, logistical challenges and the COVID-19 pandemic have prompted the need to develop and assess virtual training alternatives. Objectives: This study compares EMT trainee experiences in TEAMS 3.0 virtual and in-person programs using qualitative thematic analysis. Method/Description: Sixteen Canadian EMT volunteers participated in a condensed, one-day TEAMS 3.0 program. Sessions were held in-person (6 trainees, 4 trainers) and virtually (10 trainees, 6 trainers). Each session included four exercises with 30-minute debriefs, which were recorded and transcribed. Thematic analysis of transcripts was done in NVivo version 14. Results/Outcomes: Thematic analysis revealed key components of effective training in both formats. Access to EMT-specific SOPs and documentation templates were identified as being crucial for learning and exercise success. However, the virtual format negatively impacted communication and team connection during training activities. Conclusion: Both formats supported the development of team skills and sparked essential discussions for successful deployment. Despite challenges in virtual training, such as impaired communication and participant connection, converting TEAMS 3.0 to a virtual format is a viable method of EMT training.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".