Games in Times of a Pandemic: Structured Overview of COVID-19 Serious Games
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic introduced an urgent need for effective strategies to disseminate crucial knowledge and improve people's subjective well-being. Complementing more conventional approaches to knowledge dissemination, game-based interventions were developed to create awareness and educate people about the pandemic, hoping to change their attitudes and behavior. OBJECTIVE: This study provided an overview and analysis of digital and analog game-based interventions in the context of the COVID-19 pandemic. As major pandemics and other large-scale disruptive events are expected to increase in frequency in the coming decades, this analysis aimed to inform the design, uptake, and effects of similar future interventions. METHODS: From November 2021 to April 2022, Scopus, Google, and YouTube were searched for articles and videos describing COVID-19-themed game-based interventions. Information regarding authorship, year of development or launch, country of origin, license, deployment, genre or type, target audience, player interaction, in-game goal, and intended transfer effects was extracted. Information regarding intervention effectiveness was retrieved where possible. RESULTS: A diverse assortment of 23 analog and 43 digital serious games was identified, approximately one-third of them (25/66, 38%) through scientific articles. Most of these games were developed by research institutions in 2020 (13/66, 20%) and originated in Europe and North America (38/66, 58%). A limited number (20/66, 30%) were tested on relatively small samples, using a diversity of research methods to assess the potential changes in participants' knowledge, attitudes, and behaviors as well as their gameplay experience. Although most of the evaluated games (11/20, 55%) effectively engaged and motivated the players, increased awareness, and improved their understanding of COVID-19-related issues, the games' success in influencing people's behavior was often unclear or limited. CONCLUSIONS: To increase the impact of similar future interventions aimed at disseminating knowledge and influencing people's attitudes and behaviors during a large-scale crisis, some considerations are suggested. On the basis of the study results and informed by existing game theories, recommendations are made in relation to game development, deployment, and distribution; game users, design, and use; game design terminology; and effectiveness testing for serious games.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 tête enseignante, 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 ».