Using Observation to Determine Teachable Moments Within a Serious Game: A GridlockED as Medical Education (GAME) Study
Bibliographic record
Abstract
BACKGROUND: The use of serious games as an educational tool may be an effective strategy to improve knowledge and skill among health care trainees. GridlockED is a serious board game designed to simulate a shift in the emergency department (ED) that incorporates concepts such as prioritization in a multipatient environment and stewardship of finite resources. Serious games can present concepts to learners that are not easily accessible through other teaching methods. GridlockED was designed to demonstrate the principles behind ED flow and how to prioritize in a complex multipatient environment. The objective of this study was to identify teaching points to which learners are exposed while playing the GridlockED game. METHODS: We conducted a prospective, observational study from May to August 2017. Practicing emergency physicians, residents, and nurses were recruited as participants to play GridlockED. Participants were instructed on how to play the game and then engaged in playing GridlockED, during which their gameplay was video recorded. The videos of the play sessions were qualitatively analyzed using an interpretive description technique. All teaching points explicitly stated by players or implicitly observed by researchers were recorded. RESULTS: Teaching points were identified in the GridlockED play sessions centered around the concepts of patient prioritization and staff placement. Major themes present in gameplay, as well as deviations from reality and frequent misconceptions about emergency care, were also identified. CONCLUSION: Observations of experienced ED practitioners reveal that the GridlockED board game creates opportunities for engaging medical learners in systems-level teaching. Our findings will help create the basis for future education modules, but further study is required to ensure that junior trainees actually learn when playing the game.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".