The Moderating Role of Coping Mechanisms and Being an e-Sport Player Between Psychiatric Symptoms and Gaming Disorder: Online Survey
Bibliographic record
Abstract
BACKGROUND: The emerging popularity of playing video games (gaming) as a hobby and as a professional sport raises awareness about both the benefits and possible downsides of the activity. Although a healthy and passionate hobby for most, a minority of gamers experience addiction-like symptoms and are considered to have gaming disorder (GD). GD has previously been found to be related to aversive conditions, such as depression or anxiety, as well as putatively maladaptive coping strategies. OBJECTIVE: The aim of this study is twofold: to explore the moderating effect of different coping strategies and type of video game usage (professional [e-sport] or recreational) on the relationship between psychiatric symptoms and GD. METHODS: A sample of 3476 gamers (n=3133, 90.13% males; mean age 23.20, SD 6.48 years) was recruited via the website and social networking site of the most popular gaming magazine in Hungary (GameStar). RESULTS: The main effect of psychiatric symptoms was moderate to large in all models, whereas the moderation effects were significant (P<.001) for 4 out of 8 coping strategies (ie, self-blame/self-distraction, denial, emotional/social support, and active coping). However, the explained variance of the models only increased negligibly (from 0.3% to 0.5%) owing to the moderation effect. The direction of the moderations was as expected (ie, putatively maladaptive strategies were associated with more GD symptoms when the level of psychiatric symptoms was high, while putatively adaptive strategies were associated with less). Furthermore, no considerable moderation effect of the player type (recreational vs professional players) was found on the association between psychiatric symptoms and GD (β=.04; P=.02; 0.1% change in the explained variance). CONCLUSIONS: Future studies should be designed to better understand coping-related mechanisms in the context of video gaming and GD.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".