Virtual serious games for women’s health education: A scoping review
Bibliographic record
Abstract
BACKGROUND: Virtual serious games (VSGs) offer an engaging approach to women's health education. This review examines the state of research on VSGs, focusing on intended users, design characteristics, and assessed outcomes. METHODS: Following JBI methodology guidance for the scoping review, searches were conducted in the MEDLINE, CINAHL, EMBASE, Web of Science, and PsycINFO databases from inception to April 22, 2024. Eligible sources included participants: women or females aged 18 years and older, with no restrictions based on health condition or treatment status; concept: VSGs; context: settings where health education is provided. Sources were restricted to English language and peer-reviewed articles. Two reviewers independently screened titles, abstracts, and full texts using eligibility criteria. Data extraction was performed by one reviewer and verified by another using a custom tool. Quantitative (e.g., frequency counting) and qualitative (content analysis) methods were employed. The findings were organized into figures and tables accompanied by a narrative description. RESULTS: 12 studies from 2008 to 2023, mostly in the U.S. (66.7%), explored various age groups and women's health, focusing on breast and gynecological cancer (67%). Half (50%) of the VSGs were theory-informed; 41.7% involved users, and 58.3% had partnerships. Game types included tablet (41.7%), mobile (25%), and web (33.3%). Gameplay dosage varied from single session (50%) to self-directed (25%) and specific frequency (25%). Gameplay duration was self-directed (50%) or fixed lengths (50%). Outcomes included knowledge (50%), skills (16.7%), satisfaction (58.3%), health-related metrics (41.7%), and gameplay analysis (16.7%). CONCLUSIONS: Studies show increased interest in VSGs for women's health education, especially regarding breast and gynecological cancer. The focus on theoretical frameworks, user involvement, and collaborations highlights a multidisciplinary approach. Varied game modalities, dosage, and assessed outcomes underscore VSG adaptability. Future research should explore long-term effects of VSGs to advance women's health education.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".