P073: The GridlockED board game: using serious games for medical education
Bibliographic record
Abstract
Introduction: The management of patient flow in the emergency department (ED) is crucial for the practice of emergency medicine (EM). However, this skill is difficult to teach didactically and is learned implicitly in the latter half of residency training. To help expedite the learning process, we developed the GridlockED board game as an educational tool to simulate ED patient flow. By having junior medical trainees play this game, we believe that they will develop a greater understanding of patient flow and resource management in the ED. Additionally, since GridlockED is a cooperative game, players may also benefit by improving their communication and teamwork skills. Methods: GridlockED was developed over twenty months of iterative gameplay and review. Feedback from attending emergency physicians, residents, and medical students was integrated into the game through a Plan-Do-Study-Act (PDSA) model. Emergency medicine nurses, physicians and residents at McMaster University were recruited to play GridlockED. Each player completed a pre-survey to collect demographic data and to assess their prior experience with playing board games. All play sessions were recorded for data collection purposes. Following each game session, a member of the research team conducted an exit interview with the players to gather information about their play experience and the educational value of the game. A post-survey was also sent to each participant for further feedback. Results: Eighteen gameplay sessions were conducted from June to August 2017. A total of thirty-two participants played the game (13 emergency physicians, 15 residents, and four nurses). Overall responses to the post-gameplay survey showed that players endorsed GridlockED as a useful potential teaching tool (75%, n=24/32) and the majority felt that it had the potential to improve patient flow in the ED (56%, n=18/32). Most participants found that the game was easy to play (91%, n=27/29), and that the instructions were clear (87.5%, n=28/32). Respondents also felt that the game reflected real life scenarios (56%, n=18) and that cases reflected the types of patients that they saw in the ED (78%, n=25). Conclusion: Our results have shown an overall positive response to GridlockED, with most participants supporting it as both an engaging board game and potential teaching tool. We believe that future studies with larger sample sizes and medical students will further validate the use of serious games in medical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.000 |
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 teacher head, 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".