Digital Gamification to Enhance Vaccine Knowledge and Uptake: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Vaccine hesitancy is a growing threat to population health, and effective interventions are needed to reduce its frequency. Digital gamification is a promising new approach to tackle this public health issue. OBJECTIVE: The purpose of this scoping review was to assess the amount and quality of outcomes in studies evaluating gamified digital tools created to increase vaccine knowledge and uptake. METHODS: We searched for peer-reviewed articles published between July 2009 and August 2019 in PubMed, Google Scholar, Journal of Medical Internet Research, PsycINFO, PsycARTICLES, Psychology and Behavioral Sciences Collection, and SocINDEX. Studies were coded by author, year of publication, country, journal, research design, sample size and characteristics, type of vaccine, theory used, game content, game modality, gamification element(s), data analysis, type of outcomes, and mean quality score. Outcomes were synthesized through the textual narrative synthesis method. RESULTS: A total of 7 articles met the inclusion criteria and were critically reviewed. Game modalities and gamification elements were diverse, but role play and a reward system were present in all studies. These articles included a mixture of randomized controlled trials, quasi-experimental studies, and studies comprising quantitative and qualitative measures. The majority of the studies were theory-driven. All the identified gamified digital tools were highly appreciated for their usability and were effective in increasing awareness of vaccine benefits and motivation for vaccine uptake. CONCLUSIONS: Despite the relative paucity of studies on this topic, this scoping review suggests that digital gamification has strong potential for increasing vaccination knowledge and, eventually, vaccination coverage.
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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 | 0.022 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".