Feeling the flow with a serious game workshop: GridlockED as Medical Education 2 study (GAME2 study)
Bibliographic record
Abstract
OBJECTIVE: GridlockED gameplay workshops were delivered in Canada. This project investigated workshop attendees' experiences, seeking to identify learning points to inform improvement of the workshop. METHODS: GridlockED sessions were held through 2018 and 2019. Workshops targeted medical trainees. After a standardized video, learners played for approximately 90 minutes. Learners completed a postgameplay survey with 7-point Likert scale questions about their experience. RESULTS: Seventy-two participants responded to our survey (41 medical students, 13 physician assistant students, 12 emergency medicine residents, and six faculty members). Trainees rated GridlockED as both enjoyable and a meaningful educational experience, with a mean (±SD) rating of 6.53 (±0.96) of 7 for enjoyment and 6.17 (±1.13) for education. Attendees identified teamwork and communication (49%) as the most helpful learning domain, with patient flow (43%) being second and basics of how the ED worked (31%) being third. The respondents self-identified top areas of learning as resource management (38.9%), improved understanding of various provider roles in the ED (33%), and improved communication skills (33%). CONCLUSION: Medical learners identified GridlockED to be an educational and enjoyable learning experience. Attendees reported that playing this serious game assisted with learning about health systems and communication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".