Age and the persuasiveness of a game to promote the adoption of COVID-19 precautionary measures
Bibliographic record
Abstract
Research has shown that there are differences in how various age groups respond to persuasive attempts. However, there is little knowledge on whether age influences the effectiveness of persuasive games for health, specifically, games for promoting the adoption of COVID-19 precautionary measures. To advance research in this area, we designed COVID Pacman-C - a persuasive game to promote the adoption of COVID-19 precautionary measures employing the competition strategy. This paper presents the design, implementation, and evaluation of COVID Pacman-C to examine its effectiveness with respect to the overall perceived persuasiveness as well as the effect of age on the persuasiveness to establish whether there is a need to tailor the game to various age groups. The results of the study (N=131) followed by a semi-structured interview of 18 participants reveals that the game is perceived as highly persuasive overall with respect to its ability to promote the adoption of the COVID-19 precautionary measures as well as by the different age groups. The findings also revealed that there are significant differences in the persuasiveness for people belonging to different age groups with respect to its ability to motivate them to adopt the COVID-19 precautionary measures.
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.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".