Socially-Oriented Persuasive Game to Promote Disease Awareness and Prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Persuasive games are widely implemented in the health domain to promote desirable behaviour change. Previous research shows that using persuasive games employing various strategies results in increased motivation and awareness that led to a positive change in behaviour. This paper investigates the efficacy of a competition-based persuasive game at creating awareness and motivating people to adhere to COVID-19 precautionary measures. To achieve this goal, we developed and evaluated a competition-based persuasive game to promote the awareness and adoption of COVID-19 precautionary measures. The results of our pretest and posttest study ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>N</a:mi> <a:mo>=</a:mo> <a:mn>67</a:mn> </a:math> ) followed by a semistructured interview of 18 participants show the efficacy of the game with respect to promoting a positive change in attitude, intention, self-efficacy, knowledge, and promoting motivation and positive player experience among participants. The qualitative results provide insight into how and why persuasive games promote desirable behaviour. The paper contributes to the knowledge of how emerging technologies in the form of persuasive games can be designed and used to contribute to solving problems in our society.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it