The Role of Transfer in Designing Games and Simulations for Health: Systematic Review
Bibliographic record
Abstract
BACKGROUND: The usefulness and importance of serious games and simulations in learning and behavior change for health and health-related issues are widely recognized. Studies have addressed games and simulations as interventions, mostly in comparison with their analog counterparts. Numerous complex design choices have to be made with serious games and simulations for health, including choices that directly contribute to the effects of the intervention. One of these decisions is the way an intervention is expected to lead to desirable transfer effects. Most designs adopt a first-class transfer rationale, whereas the second class of transfer types seems a rarity in serious games and simulations for health. OBJECTIVE: This study sought to review the literature specifically on the second class of transfer types in the design of serious games and simulations. Focusing on game-like interventions for health and health care, this study aimed to (1) determine whether the second class of transfer is recognized as a road for transfer in game-like interventions, (2) review the application of the second class of transfer type in designing game-like interventions, and (3) assess studies that include second-class transfer types reporting transfer outcomes. METHODS: A total of 6 Web-based databases were systematically searched by titles, abstracts, and keywords using the search strategy (video games OR game OR games OR gaming OR computer simulation*) AND (software design OR design) AND (fidelity OR fidelities OR transfer* OR behaviour OR behavior). The databases searched were identified as relevant to health, education, and social science. RESULTS: A total of 15 relevant studies were included, covering a range of game-like interventions, all more or less mentioning design parameters aimed at transfer. We found 9 studies where first-class transfer was part of the design of the intervention. In total, 8 studies dealt with transfer concepts and fidelity types in game-like intervention design in general; 3 studies dealt with the concept of second-class transfer types and reported effects, and 2 of those recognized transfer as a design parameter. CONCLUSIONS: In studies on game-like interventions for health and health care, transfer is regarded as a desirable effect but not as a basic principle for design. None of the studies determined the second class of transfer or instances thereof, although in 3 cases a nonliteral transfer type was present. We also found that studies on game-like interventions for health do not elucidate design choices made and rarely provide design principles for future work. Games and simulations for health abundantly build upon the principles of first-class transfer, but the adoption of second-class transfer types proves scarce. It is likely to be worthwhile to explore the possibilities of second-class transfer types, as they may considerably influence educational objectives in terms of future serious game design for health.
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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 | 0.031 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".