Serious Game Development for Public Health: Participatory Design Approach to COVID-19 Quarantine Policy Education
Notice bibliographique
Résumé
Background: Public health education plays a crucial role in effectively addressing infectious diseases such as COVID-19. However, existing educational materials often provide only foundational information, and traditional group education faces challenges due to social distancing policies. Objective: Addressing these gaps, our study introduces a serious game called "Flattening the Curve." This interactive experience immerses learners in the role of quarantine policy managers, offering unique insights into the effects and challenges of social distancing policies. Methods: The development of the game adhered to the SERES framework, ensuring a scientifically designed foundation. To achieve its learning objectives, the game incorporated learning and game mechanics including an agent-based infection model, a social distancing policy model, and an economic model, which were developed based on previous literature. After defining a broad concept of scientific and design foundations, we used a participatory design process. This study included 16 undergraduates and took place over one semester. Participants played the game, gave feedback, and answered surveys. The game was improved based on participants' feedback throughout the process. Participants' feedback was analyzed based on the Design, Play, and Experience framework. Surveys were conducted before and after the activity and analyzed to assess participants' evaluation of and satisfaction with the game. Results: The game successfully achieved its learning objectives, encompassing a comprehensive understanding of infectious disease characteristics; the disease transmission process; the necessity and efficacy of quarantine policies and their delicate balance with economic factors; and the concept of flattening the curve. To achieve this, the game includes the following: (1) an agent-based infection model based on the modified Susceptible-Exposed-Infectious-Hospitalized-Recovered (SEIHR) model with five infectious disease scenarios; (2) a quarantine policy model with social distancing, travel control, and intensive care unit management; and (3) an economic model that allows users to consider the impact of quarantine policies on a community's economy. In response to participatory design feedback, the game underwent meticulous modifications, including refining game systems, parameters, design elements, the user interface, and interactions. Key feedback included requests for more scenarios and engaging yet simple game elements, as well as suggestions for improving the scoring system and design features. Notably, concerns about the fairness of the outcome evaluation system (star rating system), which could incentivize prioritizing economic activity over minimizing casualties, were raised and addressed by replacing the star rating system with a progress-based vaccine development system. Quantitative evaluation results reflect participants' positive assessments of the game through the learner-centric approach. Conclusions: The serious game "Flattening the Curve," developed through a participatory design approach, emerges as a valuable tool for public health education, particularly concerning social distancing policies. The game and its source code are openly accessible online, enabling widespread use for research and educational purposes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».