Tabletop role playing games as a way forward with structurally marginalized youth: A narrative review
Bibliographic record
Abstract
• TTRPGs are experiencing a cultural renaissance, surging in popularity. • TTRPGs have evolved into safe spaces embraced by structurally marginalized youth. • TTRPGs provide a less-intimidating, youth-driven approach to engagement. • TTRPGs are used to promote growth, prepare for therapy, and to deliver intervention. • Amid anti-trans discourse, TTRPGs provide safe spaces for growth and acceptance. Tabletop roleplaying games (TTRPGs) are experiencing a remarkable surge in popularity, with sales of Dungeons and Dragons™ more than tripling during the COVID-19 pandemic. Once a stigmatized pastime, TTRPGs have evolved into inclusive community spaces for structurally marginalized youth. Alongside this cultural transformation, there is growing recognition of TTRPGs not only as tools for healthy psychological development but also as promising vehicles for delivering psychosocial interventions. This review seeks to bridge the gap between the TTRPG community and mental health clinicians and researchers, demonstrating that gaming can serve as more than recreational activity. It can offer a powerful, youth-driven space for growth and engagement. For structurally marginalized youth, particularly those who have been harmed by systems and may be resistant to traditional interventions, TTRPGs provide a strengths-based, interest-driven approach that meets them where they are at. We begin by defining structural marginalization and its impact on adolescent development, then explore the evolution of TTRPGs into inclusive environments, review emerging evidence supporting their use as interventions, and conclude with practical recommendations and directions for future research. Special attention is given to the unique ways in which TTRPGs can support trans and gender non-conforming youth, particularly in the context of escalating anti-trans rhetoric and diminishing access to gender-affirming spaces and care.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".