Persuasiveness of a Game to Promote the Adoption of COVID-19 Precautionary Measures and the Moderating Effect of Gender
Bibliographic record
Abstract
Persuasive games are widely being implemented in the healthcare domain to drive behaviour change among individuals. Previous research has shown that there are differences in how males and females respond to persuasive attempts. However, there is little knowledge on whether gender moderates the effectiveness of persuasive games for health, specifically, games for promoting the adoption of COVID-19 precautionary measures. To address this gap, we designed COVID Pacman-R – a persuasive game to promote the adoption of COVID-19 precautionary measures. This paper presents the design and evaluation of COVID Pacman-R to examine its overall perceived persuasiveness as well as gender differences in persuasiveness to establish whether there is a need to tailor the game to various gender groups. Study results (N=131) revealed that the game is perceived as highly persuasive overall as well as by the different gender groups. The findings also revealed that there are no significant differences in the persuasiveness across gender groups with respect to COVID-19 ability to motivate them to adopt the precautionary measures.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".