Who seeks treatment for gaming? Characteristics of young and adult patients seeking treatment for gaming disorder
Bibliographic record
Abstract
Background: Gaming disorder has recently been recognized as a psychiatric condition, yet the clinical characteristics of treatment-seeking individuals remain understudied. This study examined youth and adults seeking treatment at a specialized outpatient clinic in southern Sweden. Methods: A total of 107 individuals aged 12-49 years (M = 22.1, SD = 7.2) underwent comprehensive clinical interviews, psychosocial assessments, MINI diagnostic interview, and standardized self-report measures. Results: Most participants were male (94%), and 80% met diagnostic criteria for gaming disorder. The average age of symptom onset was 16.0 years (SD = 4.6), with a mean duration of 5.5 years (SD = 4.6). Weekly gaming time averaged 50 hours (SD = 12.0, range 0-126). Although participants reported low levels of gaming disorder symptoms (measured by GDT) and psychological distress (measured by CORE-OM and RCADS), but 69% showed significant functional impairments based on clinician ratings using GAF and CGAS. ADHD symptoms were uniquely positively associated with both gaming disorder severity (β=0.39, p < 0.001) and psychological distress (β=0.34, p < 0.001). Psychological distress also increased with age (β=0.38, p=0.002). Discussion: Although many received a clinical diagnosis, the sample reported low levels of gaming disorder symptoms. They reported relatively low psychological distress but demonstrated substantial functional impairment. This may reflect gaming's role as both an avoidance strategy and a way to meet psychological needs. Conclusion: These findings suggest that impaired everyday functioning is a defining clinical feature of this group. Treatment should not only address gaming behavior but also support patients in improving functioning across important areas of life, such as school, work, and relationships.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".