Serious games for serious crises: reflections from an infectious disease outbreak matrix game
Bibliographic record
Abstract
BACKGROUND: While there is widespread recognition of global health failures when it comes to infectious disease outbreaks, there is little discussion on how policy-makers and global health organizations can learn to better prepare and respond. Serious games provide an underutilized tool to promote learning and innovation around global health crises. In order to explore the potential of Serious Games as a policy learning tool, Global Affairs Canada, in collaboration with the Department of National Defense and academic partners, developed and implemented a matrix game aimed at prompting critical reflection and gender-based analysis on infectious disease outbreak preparedness and response. This commentary, written by the core development team, reflects on the process and outcomes of the gaming exercise, which we believe will be of interest to others hoping to promote innovative thinking and learning around global health policy and crisis response, as well as the application of serious games more broadly. MAIN BODY: Participants reported, through discussions and a post-game survey, that they felt the game was reflective of real-world decision-making and priority-setting challenges during a crisis. They reflected on the challenges that emerge around global health co-operation and outbreak preparedness, particularly noting the importance of learning to work with private actors. While participants only sporadically applied gender-based analysis or considered the social determinants of health during the game, post-game discussions led to reflection on the ways in which equity concerns are put aside during a crisis scenario and on why this happens, offering critical learning opportunities. CONCLUSION: Matrix games provide opportunities for policy-makers and health professionals to experience the challenges of global health co-operation, test ideas and explore how biases, such as those around gender, influence policy-making and implementation. Due to their flexibility, adaptability and accessibility, serious games offer a potentially powerful learning tool for global health policy-makers and practitioners.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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".