TEAMergizers: Energizers for teamwork and communication skills practice and reflection for medical teams
Bibliographic record
Abstract
WHAT WAS THE EDUCATIONAL CHALLENGE?: Electronic dance music festivals rely on ad hoc, interdisciplinary medical teams to manage emergencies. Since members often meet onsite for the first time, quickly building rapport and practicing teamwork/communication skills before the event is essential. WHAT WAS THE SOLUTION?: TEAMergizers-energizers to build group rapport and practice teamwork/communication skills-were introduced. TEAMergizers include debriefs to reflect on collaborative competencies. HOW WAS THE SOLUTION IMPLEMENTED?: Twenty-one TEAMergizers were piloted with event medical teams. Participants rated games on engagement and teamwork/communication skill elicitation. Results informed a TEAMergizer Manual with instructions, ratings, debrief guides, and mapping to teamwork/communication domains. WHAT LESSONS WERE LEARNED THAT ARE RELEVANT TO A WIDER GLOBAL AUDIENCE?: = 314): overall average 8.15/10, average ability to elicit teamwork/communication skills 7.27/10. Games with movement, large groups, a competitive win condition, higher noise levels, and less idea generation scored best. Games with a collaborative win condition, team-versus-team competition, and autonomy of participation extent were perceived best for eliciting teamwork/communication skills. WHAT ARE THE NEXT STEPS?: Next steps include sharing the TEAMergizer Manual with educators. Insights on game features that boost satisfaction and skill elicitation will help educators design teamwork-focused games.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".