Online Video Game Therapy for Mental Health Concerns: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There has been research on the use of offline video games for therapeutic purposes but online video game therapy is still fairly under-researched. Online therapeutic interventions have only recently included a gaming component. Hence, this review represents a timely first step toward taking advantage of these recent technological and cultural innovations, particularly for the treatment of special-needs groups such as the young, the elderly and people with various conditions such as ADHD, anxiety and autism spectrum disorders. MATERIAL: A review integrating research findings on two technological advances was conducted: the home computer boom of the 1980s, which triggered a flood of research on therapeutic video games for the treatment of various mental health conditions; and the rise of the internet in the 1990s, which caused computers to be seen as conduits for therapeutic interaction rather than replacements for the therapist. DISCUSSION: We discuss how video games and the internet can now be combined in therapeutic interventions, as attested by a consideration of pioneering studies. CONCLUSION: Future research into online video game therapy for mental health concerns might focus on two broad types of game: simple society games, which are accessible and enjoyable to players of all ages, and online worlds, which offer a unique opportunity for narrative content and immersive remote interaction with therapists and fellow patients. Both genres might be used for assessment and training purposes, and provide an unlimited platform for social interaction. The mental health community can benefit from more collaborative efforts between therapists and engineers, making such innovations a reality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it