The Potential of Video Game Streaming as Exposure Therapy for Social Anxiety
Bibliographic record
Abstract
Social anxiety is a prevalent problem that affects many people with varying severity; digital exposure therapy-which involves controlled exposure to simulations of feared social situations alongside cognitive restructuring-can help treat patients with anxieties. However, the need to personalize exposure scenarios and simulate audiences are barriers to treating social anxieties through digital exposure. In this paper, we propose game streaming as an exposure therapy paradigm for social anxiety, supporting it with data from two studies. We first propose a framework describing requirements for exposure therapy and how game streaming can fulfill them. We select demand and performance visibility from these characteristics to showcase how to manipulate them for experiences of gradual exposure. With Study 1, we provide evidence for these characteristics and support for the framework by showing that a game's demand affected expected fear of streaming games. In Study 2, we show that the prospect of streaming led to elevated fear, a necessary property for effective exposure therapy. Further, we show that the effect of streaming on expected fear was similar for participants who can be considered socially anxious. These findings provide evidence for the essential effect of exposure therapy, which serves as a first step towards the validation of streaming as a social anxiety treatment. Our paper provides an initial, important step towards a novel, broadly applicable, and widely accessible digital approach for the treatment of social anxiety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".