Internet Gaming Disorder: An Emergent Health Issue for Men
Bibliographic record
Abstract
Internet gaming is a legitimate leisure activity worldwide; however, there are emerging concerns that vast numbers of gamers are becoming addicted. In 2013, the American Psychiatric Association (APA) classified Internet Gaming Disorder (IGD) as a condition warranting more clinical research ahead of formalizing it as a mental disorder. Proposed as a behavioral addiction, IGD shares many similarities in both physical and psychosocial manifestations with substance use disorder, including cerebral changes on functional magnetic resonance imaging (fMRI). Among the gaming population, compared to females, adolescent and adult males demonstrate far more addictive internet gaming use in terms of screen hours, craving, and negative impacts on health, which have, in isolated incidents, also caused death. The current article draws findings from a scoping review of literature related to IGD as a means to raising awareness about an emergent men's health issue. Included are three themes: (a) unveiling the nature, impacts and symptoms of IGD; (b) conceptualizing IGD through neuroscience; and (c) treatment approaches to IGD. Afforded by these themes is an overview and synthesis of the existing literature regarding IGD as a means of providing direction for much needed research on gaming addiction and orientating primary care providers (PCPs) to the specificities of IGD in men's health. The findings are applied to a discussion of the connections between IGD and masculinity and the importance of recognizing how behaviors such as social isolation and game immersion can be maladaptive coping strategies for males.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".