Draw & Doodle Simulation: A colorful strategy to prepare medical teams for electronic dance music festival emergencies
Bibliographic record
Abstract
EDUCATIONAL CHALLENGE: Electronic dance music (EDM) festivals - crowded, loud, low-resource environments - pose unique challenges to event medical teams. Simulation can prepare teams to manage clinical presentations in this unconventional context. Without access to simulation infrastructure, a low-technological, low-fidelity simulation modality is warranted. SOLUTION: Draw & Doodle Simulation (D&D SIM) is a low-fidelity simulation where patients are hand-drawn (i.e. on paper, whiteboard, or digitally) instead of utilizing manikins or live actors. Facilitators draw all patient findings, while participants doodle any possible interventions. SOLUTION IMPLEMENTATION: Two D&D SIM cases (serotonin toxicity and refractory anaphylaxis) were piloted in classrooms. Participants included paramedics, medical students, lifeguards, and first aiders. Facilitators conducted simulations using chart paper, with each participant doodling contributions using differently colored markers. LESSONS LEARNED: = 15). Participants appreciated the 'low-stakes', useful 'visual' representation of progress, 'fun and [interactivity]', and appropriate '[realism]' of D&D SIM. However, D&D SIM was perceived as 'less life threatening', would not be appropriate for physical skills (e.g. CPR), required everyone to be 'oriented in the same direction to see the drawing', and the chart paper risked becoming cluttered. NEXT STEPS: Next steps include writing new cases, implementing D&D SIM in other teaching contexts, exploring its use in digital platforms, and studying its effectiveness against higher-fidelity simulation.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".