How Gameful Experience Affects Public Knowledge, Attitudes, and Practices Regarding COVID-19 Among the Taiwanese Public: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: In 2019, with the COVID-19 pandemic sweeping across the globe, public health systems worldwide faced severe challenges. Amid the pandemic, one simulation game, Plague Inc., has received substantial attention. This game has indirectly drawn greater public attention to public health issues by simulating pathogen transmission and disease symptoms. OBJECTIVE: Against this backdrop, this research investigates whether the gameful experience of Plague Inc. has indirectly affected public knowledge, attitudes, and practices (KAP) regarding COVID-19. METHODS: An online survey was conducted through social networking services in Taiwan from May 6-28, 2020. RESULTS: A total of 486 subjects participated in this study, of which 276 (56.8%) had played Plague Inc. This study had several findings. First, participants who had played Plague Inc. demonstrated higher levels of knowledge (P=.03, median 7, IQR 7-8) and attitudes (P=.007, median 8, IQR 7-8) than participants who had not played Plague Inc. (knowledge: median 7, IQR 6-8; attitude: median 7, IQR 6-8). Second, there was a significant correlation between creative thinking (ρ=.127, P=.04) and dominance (ρ=.122, P=.04) in attitude. Finally, there was a significant correlation between creative thinking (ρ=.126, P<.001) and dominance (ρ=.119, P=.049) in practice. CONCLUSIONS: Serious games highlighting the theme of pathogen transmission may enhance public knowledge and attitudes regarding COVID-19. Furthermore, the creative thinking and dominance involved in gameful experiences may act as critical factors in public attitudes and practices regarding COVID-19. These findings should be further verified through experimental research in the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".