Co-designing and pilot testing a digital game to improve vaccine attitudes and misinformation resistance in Ghana
Bibliographic record
Abstract
Misinformation related to vaccines has been shown to potentially negatively impact public perceptions and intentions to vaccinate in many contexts including COVID-19 vaccination in Ghana. Psychological inoculation – where recipients are warned about the misleading techniques used in misinformation – is a potential intervention which could preemptively boost public resistance against misinformation. Cranky Uncle Vaccine is an interactive, digital game that applies inoculation, offering a scalable tool building public resilience against vaccine misinformation and promoting positive health-related behaviors. In this study, we document the process of developing and testing a West African version of Cranky Uncle Vaccine, with co-design workshops and a pilot test conducted in urban and peri-urban areas of the Greater Accra region of Ghana with 829 young people who had access to mobile and computer devices. The average age was 21.8 and participants were highly educated (median education level “Some/all university”) with slightly more females (51.2%) than males (48.4%). Pilot participants filled out surveys before and after playing the game, measuring vaccine attitudes (pre-game M = 3.4, post-game M = 3.6), intent to get vaccinated (pre-game M = 3.5, post-game M = 3.6), and discernment between vaccine facts and fallacies (pre-game AUC = 0.72, post-game AUC = 0.75). We observed a significant improvement in attitudes toward vaccines, with players demonstrating increased likelihood to get vaccinated after completing the game. Among players who indicated that they were unlikely to get vaccinated in the pre-game survey (N = 52, or 6.3% of participants), just over half of these participants (53%) switched to likely to get vaccinated after playing the game. Perceived reliability of vaccine facts remained the same, while perceived reliability of vaccine fallacies significantly decreased, indicating improved ability to spot misleading arguments about vaccines. These results demonstrate the effectiveness of a digital game in building public resilience against vaccine misinformation as well as improving vaccine attitudes and intent to get vaccinated.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".