Role of Gaming Devices Associated With Internet Gaming Disorder in China: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Chinese gamers use computer and mobile phone games widely. Consequently, concerns regarding the development of internet gaming disorder (IGD) in adolescents have been raised. However, only a few studies have focused on the influence of gaming devices on IGD. OBJECTIVE: This study aims to compare sociodemographic information, gaming use characteristics, personality traits, and gaming motivations between computer game users (CGUs) and mobile phone game users (MGUs), as well as identifying IGD predictors. METHODS: This was a cross-sectional study. A total of 3593 internet game players took part in an online survey, which included sociodemographic information, gaming patterns, gaming motivations, the Chinese version of the Video Game Dependency Scale, and the Chinese Big Five Personality Inventory brief version. The population was divided into 2 groups for comparison by mobile phone or computer use, and the IGD population was also compared within the 2 groups. RESULTS: =3.68, P<.001). Individuals using different gaming devices had different game motivations and personality traits and preferred different genres of games. Results showed that IGD predictors were different for the 2 groups, for example, strategy (odds ratio [OR] 4.452, 95% CI 1.938-10.227; P<.001) and action shooter (OR 3.725, 95% CI 1.465-9.474; P=.01) games increased the risk for MGUs. CONCLUSIONS: Gaming devices should be considered during early identification, such as long daily gaming time, much money spent on gaming, neuroticism, and conscientiousness. In addition, more research should be conducted on new gaming devices and IGD treatment.
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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 | 0.001 |
| 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".