Mental Health Symptomatology, Internet Gaming Disorder and Gaming-Related Harms
Bibliographic record
Abstract
Internet gaming disorder (IGD) affects approximately 3-5% of the population worldwide and is listed in the DSM-5 as a condition for further study. Gamers, irrespective of IGD status, may also report gaming-related harms, including work or study or health harms. A relationship has been established between IGD and mental health symptoms. However, there is a lack of research that has examined the relationship between mental health symptoms and gaming-related harms. The present study utilized a cross-sectional online survey to assess the association between mental health symptoms and the prevalence of IGD and gaming-related harms. A total of N = 738 adult participants who resided in the United States or Canada and had played any video games within the past month were retained in the sample. Meeting the cut-off for any mental health symptoms was associated with an increased prevalence of IGD (3-14-fold) and all gaming-related harms (1-5-fold). Gamers, including individuals who do not meet the cut-off for IGD but still experience harms associated with their video gaming, may need to be closely monitored for mental health comorbidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".