Utilizing Theories and Evaluation in Digital Gaming Interventions to Increase Human Papillomavirus Vaccination Among Young Males: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. HPV attributes to most cancers including anal, oral, cervical, and penile. Despite infection rates in the United States, recommendations and communication campaigns have traditionally focused on females. Because of this, males lack knowledge about reasons for vaccination, the benefits of being vaccinated, and their HPV risk, overall. Gaming as a health education strategy can be beneficial as mechanism that can promote behavior change for this key demographic because of the popularity of gaming. OBJECTIVE: We sought to explore the relationship between gamification and HPV vaccine uptake. METHODS: Interviews were conducted with experts (n=22) in the fields of cancer prevention, sexual and reproductive health, public health, game design, technology, and health communication on how a game should be developed to increase HPV vaccination rates among males. RESULTS: Overwhelmingly, theoretical models such as the health belief model were identified with key constructs such as self-efficacy and risk perception. Experts also suggested using intervention mapping and logic models as planning tools for health promotion interventions utilizing a digital game as a medium. In game and out of game measures were discussed as assessments for quality and impact by our expert panel. CONCLUSIONS: This study shows that interventions should focus on whether greater utilization of serious games, and the incorporation of theory and standardized methods, can encourage young men to get vaccinated and to complete the series of HPV vaccinations.
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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 | 0.001 |
| 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 | 0.000 | 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 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".