Evaluating ExpandED: Evaluating the effectiveness of a serious game expansion pack in teaching health professional students about interprofessional care
Bibliographic record
Abstract
Background: The emergency department (ED) is a challenging fast-paced environment with high-acuity, undifferentiated patients who often require extensive interdisciplinary care. This paper introduces ExpandED, an expansion pack to the serious board game GridlockED, designed to enhance players' understanding of interprofessional collaboration in the ED and the diverse scope of practice of different ED professionals including physicians, residents, registered nurses, registered practical nurses, social workers, occupational therapists, and physiotherapists. This investigation evaluates the effectiveness of ExpandED as a teaching tool for medical and allied health professions students about interprofessional collaboration in the ED. Methods: A program evaluation harnessing a playtest framework was employed. Participants completed pre- and postgame surveys including quantitative measures (e.g., Likert scales) and qualitative free-text feedback that focused on participant familiarity with ED functioning, valuation of interprofessional collaboration before and after playing, and feedback on game usability and effectiveness. Results: Recruitment was open to students in all health care and allied health professional programs at the institution. Forty-five participants were recruited from medical doctor, nursing, physiotherapy, and speech language pathology programs. ExpandED enhances participants' understanding of ED workflow (p < 0.001) and provides an enjoyable playing experience. However, participants' valuation of interdisciplinary teamwork did not change significantly before and after game play (p = 0.17). Participants expressed satisfaction with the game's accuracy in simulating the ED environment and appreciated the opportunity to collaborate with peers from different disciplines. Challenges reported included some tension among players, potential biases, and limitations of fidelity to a real-life ED. Conclusions: While this study has limitations regarding participant sampling and duration of gameplay sessions, it highlights the potential of ExpandED for teaching interprofessional collaboration in the ED. These findings will guide further development to optimize the expansion pack's effectiveness and its implementation into health care curricula.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".