Associations of binge gaming (5 or more consecutive hours played) with gaming disorder and mental health in young men
Bibliographic record
Abstract
Background: Video gaming is a popular activity among young people. Time spent with gaming was found to be only moderately associated with gaming disorder. However, patterns of binge gaming (playing more than 5 h consecutively) were rarely considered in research on gaming. This study explores how binge gaming frequency is related with gaming disorder and mental health. Methods: The sample came from the Cohort study on substance use risk factors (C-SURF) and comprised 5,358 young men aged 28.26 years (SD = 1.27). ANCOVA was conducted to estimate the association between binge gaming frequency (gaming at least 5 h consecutively) and gaming disorder (measured with the Game Addiction Scale) as well as indicators of mental health. Results: A total of 33.3% of the sample engaged in binge gaming at least once in the previous year, and 6.1% at least weekly. Frequency of binge gaming was associated with gaming disorder score in a linear dose-response relationship (linear trend = 2.30 [2.14, 2.46]) even if adjusted for time spent gaming (linear trend = 1.24 [1.03, 1.45). More frequent binge gaming was associated with lower life satisfaction and sleep quality, and with more major depression and social anxiety disorder symptoms. Conclusions: Binge gaming patterns, especially daily or almost daily binge gaming, are important to consider with regard to gaming disorder and mental health. Asking about binge gaming may be a promising screening question for gaming related problems. Encouraging regular breaks from gaming may be a valuable prevention strategy to reduce negative outcomes of gaming.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".