LO13: GridlockED: an emergency medicine game and teaching tool
Bibliographic record
Abstract
Introduction/Innovation Concept: In the controlled chaos of the emergency department (ED) it can be difficult for medical trainees similarly recognize that there is definite order to the chaos, and many may never truly appreciate its complexity. How should medical learners develop this skill? Didactic teaching cannot effectively portray the complexities of managing the ED. Much like education in cardiac arrest, trauma, and multi-casualty incident management, it is our belief that the management of patient flow through the ED is best learned through simulation. Thus, we developed GridlockED, a board game that requires players to work cooperatively to manage a simulated ED to win the game. Methods: GridlockED development took place over a six-month period during which iterative cycles of gameplay and redevelopment were used to optimize game mechanics and improve player engagement. The patient cases were created by medical students (PS, DT, JR) and subsequently reviewed for content validity by two attending emergency physicians (TC, AP). Input from attending emergency physicians, residents, medical students, and laypeople was integrated into the game through a Plan-Do-Study-Act (PDSA) model. Curriculum, Tool, or Material: Our game includes: 1) The game board; 2) Patient cards, which describe a patient, their level of acuity, and the tasks that must be completed in order to disposition the patient; 3) Event cards, which cause random positive or negative events to occur-much like random events occur in real life that change the dynamics of the ED; 4) Game Characters, which move around the board to denote where tasks are being completed; 5) A tracking sheet to follow how many tasks each character has performed in each turn; 6) A shift-time clock, which is used to track the ‘hours’ of your shift; 7) A ‘Gridlock counter’, which tracks how many ED backups or adverse patient outcomes occur (‘Gridlocks’). The goal of the game is to work cooperatively with your teammates to complete patient tasks and move patients through the ED to an ultimate disposition (e.g. admission, discharge). The game is won if you finish your shift before reaching the maximum number of ‘Gridlocks’ allowed. Conclusion: Initial responses to GridlockED have been very positive, supporting it as both an engaging board game and potential teaching tool. We are excited to see it validated through research trials and possibly incorporated into emergency medicine training at both student and postgraduate training levels.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".