Using Observation to Determine Teachable Moments Within a Serious Game: A GridlockED as Medical Education (GAME) Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it