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Record W3009298134 · doi:10.1186/s12992-020-00547-6

Serious games for serious crises: reflections from an infectious disease outbreak matrix game

2020· letter· en· W3009298134 on OpenAlex
Julia Smith, Nathan Alexander Sears, Ben Taylor, Madeline Johnson

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGlobalization and Health · 2020
Typeletter
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsGlobal Affairs CanadaGovernment of CanadaUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsPreparednessPublic relationsGlobal healthPublic healthHealth policyCrisis managementMedicinePolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.426
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it