“I wanted to be a responsible public health professional”: applying serious games for deep learning in global health courses
Bibliographic record
Abstract
ABSTRACT Serious games, including simulations, are increasingly used in university teaching, including medical and humanitarian studies as well as political science and international relations. There is less evidence of application in global health pedagogy. This article reports and reflects on the application of a simulation of negotiations around a global pandemic treaty in a Master of Public Health class on Global Health and International Affairs. Through participant observation and thematic analysis of students’ reflective essays we find that the simulation enabled deep learning in line with the assignment objectives (to apply learning from past health crisis, engage with key concepts, and experience global health cooperation and challenges), as well as prompted critical reflections on moral dilemmas related to global health cooperation and decolonizing global health. The simulation provided an opportunity for students to engage with the wicked problems embedded within global health by drawing on multiple perspectives and approaches. While the students failed in negotiating a successful pandemic treaty, it was these failures that provided opportunities for deep learning and critical reflection as they questioned constraints on their underlying motivations and actions. This experience suggests simulations can serve as a particularly apt approach for teaching interdisciplinary approaches to global health as they enable to students to apply different sets of knowledge to a particular problem, explore unfamiliar concepts and critically asses their assumptions.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".