Evaluation of Virtually Delivered TEAMS 3.0 Tabletop Modules to Train a Canadian Emergency Medical Team: A Pilot Study
Bibliographic record
Abstract
Background/Introduction: The World Health Organization established the Emergency Medical Team (EMT) initiative in 2013 to standardize disaster response, emphasizing robust education and training programs. The Canadian Medical Assistance Teams (CMAT), a volunteer-run NGO with over 1,000 members, struggles with logistical and financial constraints for in-person training. Objectives: This study evaluates the effectiveness of virtually delivered TEAMS 3.0 tabletop modules for training CMAT’s volunteers, hypothesizing that virtual training is effective and comparable to in-person training. Adapt TEAMS 3.0 tabletop exercises into a virtual format and assess their effectiveness. Compare the effectiveness of virtual and in-person training. Method/Description: A quasi-experimental design with non-randomized groups was used. CMAT members were assigned to in-person or virtual training based on availability. Pre- and post-training surveys assessed self-efficacy, teamwork, and training quality. Statistical analysis using SPSS employed non-parametric tests to compare pre- and post-training scores and between-group differences. Qualitative feedback was collected via a post-training anonymous form. Results/Outcomes: Four TEAMS 3.0 exercises were adapted for virtual delivery using Google Meet and Google collaborative tools. Among 26 participants (10 in-person, 16 virtual), both formats showed no significant changes in self-efficacy or teamwork scores from pre- to post-training. In-person training received significantly higher quality ratings from trainees compared to virtual training (p=0.026). Trainers’ quality ratings also favored in-person training but were not statistically significant (p=0.091). Conclusion: Virtual TEAMS 3.0 exercises yielded similar self-efficacy and teamwork results as in-person training, though in-person sessions were rated higher quality. This supports virtual training as a scalable, cost-effective alternative, though further research with larger samples is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".