Creating GridlockED: A Serious Game for Teaching About Multipatient Environments
Bibliographic record
Abstract
PROBLEM: As patient volumes increase, it is becoming increasingly important to find novel ways to teach junior medical learners about the intricacies of managing multiple patients simultaneously and about working in a resource-limited environment. APPROACH: Serious games (i.e., games not intended purely for fun) are a teaching modality that have been gaining momentum as teaching tools in medical education. From May 2016 to August 2017, the authors designed and tested a serious game, called GridlockED, to provide a focused educational experience for medical trainees to learn about multipatient care and patient flow. The game allows as many as six people to play it at once. Gameplay relies on the players working collaboratively (as simulated members of a medical team) to triage, treat, and disposition "patients" in a manner that simulates true emergency department operations. After researching serious games, the authors developed the game through an iterative design process. Next, the game underwent preliminary peer review by experienced gamers and practicing clinicians, whose feedback the authors used to adjust the game. Attending physicians, nurses, and residents have tested GridlockED for usability, fidelity, acceptability, and applicability. OUTCOMES: On the basis of initial testing, clinicians suggest that this game will be useful and has fidelity for teaching patient-flow concepts. NEXT STEPS: Further play testing will be needed to fully examine learning opportunities for various populations of trainees and for various media. GridlockED may also serve as a model for developing other games to teach about processes in other environments or specialties.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".