Research Design Processes in Serious Games for Adolescent Mental Health: Systematic Review
Bibliographic record
Abstract
Background: Serious games are increasingly recognized as effective tools in adolescent mental health interventions, providing engaging platforms for emotional regulation, skill development, and behavioral change. However, the ways in which core theoretical concepts such as transfer, boundary crossing, and models of reality are incorporated into serious game designs are not consistently described in the literature. Clarifying how these concepts are addressed is important for understanding how game-based learning may connect to real-world health care practice. Objective: This systematic review aims to examine how serious games for adolescent health care are designed to support learning and facilitate outcomes. Specifically, it examines how the design incorporates constructs of transfer, boundary crossing, and models of reality, and how these elements are represented across published studies. Methods: We conducted a systematic search across 5 databases (PubMed, Scopus, ERIC, PsycINFO, and EMBASE) covering publications up to 2023. Studies were included if they involved serious games targeting adolescents with behavioral or developmental health concerns. Titles and abstracts were screened independently by 2 reviewers, with disagreements resolved by a third party. A qualitative analytical framework was applied to identify elements of design, with a particular focus on transfer, boundary crossing, and models of reality. Results: Thirty-three studies met the inclusion criteria. Figural transfer was identified in 24 studies, while literal transfer was identified in 10 studies. Among boundary-crossing mechanisms, reflection occurred most frequently (22 studies), whereas transformation was observed in 3 studies. Causal and procedural models of reality were most commonly identified as primary model types, whereas relational and structural models were more often reported as secondary. Explicit design rationales were infrequently reported across studies. Conclusions: This review demonstrates that serious games for adolescent mental health most frequently emphasize reflective and representational forms of learning. Across the reviewed studies, theoretical constructs related to transfer, boundary crossing, and models of reality were often implicitly embedded rather than explicitly articulated. The proposed analytical framework offers a structured approach for analyzing these design characteristics and may support designers, researchers, and health care professionals in more explicitly aligning design choices with intended learning mechanisms and real-world applications.
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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 | 0.064 | 0.228 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".