Can playing Dungeons and Dragons be good for you? A registered exploratory pilot programme using offline tabletop role-playing games to mitigate social anxiety and reduce problematic involvement in multiplayer online video games
Bibliographic record
Abstract
Gamers with poor self-concept, high social anxiety and high loneliness are more at risk of problematic involvement in video games, such as massively multiplayer online role-playing games. There is a research gap concerning treatment approaches to cater to socially anxious gamers with problematic patterns of gaming involvement. This registered exploratory pilot programme tests the feasibility and initial effect of a structured protocol in which socially anxious online gamers are exposed to real-life social interactions while playing an offline tabletop role-playing game (TTRPG). Our structured protocol lasted 10 weeks and involved 10 sessions organized into three modules in which participants played a TTRPG inspired by the game 'Dungeons and Dragons'. Each module deployed a role-playing scenario designed to challenge the players in game terms and involve them in a story based on maturing relationships with other characters and solving challenges by social means and investigation. Our study used a quasi-experimental multiple single-case design with a three-week baseline across groups (four groups of five gamers with sub-clinical problematic video game use and social anxiety) and a three-month follow-up. Primary outcomes were time spent gaming, gaming disorder symptoms and social anxiety symptoms. Secondary outcomes were assertiveness/social skills, self-concepts and perceived loneliness. In terms of feasibility, we observed that most participants completed the programme (two of the 20 participants dropped out) and were involved in terms of participation and weekly psychometric assessments. Moreover, participants were largely able to attain the progressively more difficult objectives implemented in the TTRPG programme. Multiple single-case analyses showed that most participants benefited from the intervention through a reduction in social anxiety symptoms and problematic gaming symptoms, although to varying degrees. Some participants also reduced their gaming time or presented with reduced perceived loneliness. Assertiveness and self-concepts were not improved. This pilot study shows that a TTRPG intervention approach is feasible and may be used to reduce social anxiety and gaming disorder symptoms. The present programme must now be tested with clinical participants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".