Weak symptom overlap between cannabis use and internet gaming
Bibliographic record
Abstract
• Problematic cannabis and video game use are, at most, weakly associated with one another. • Failing to do as expected from cannabis use is linked to suffering consequences due to video game play. • Symptoms of problematic gaming are most associated with escape and competition as gaming motives. Previous research has suggested that substance use disorders and behavioural addictions tend to co-occur and have similar diagnostic criteria. This has prompted investigation into new diagnostic categories and conceptualizations of addiction. Network theory may provide a novel and useful framework for conceptualizing such psychological disorders. Given the ongoing debate surrounding internet gaming disorder, named in the DSM-5 as a condition for further study, alongside recent trends in the legalization of recreational cannabis, this investigation aimed to explore the interconnectedness of both via network analysis among a sample of young adult video game players. Across multiple network parameterizations, problematic cannabis and video game use were found to be – at most – weakly associated with one another. Further, problematic game play was found to be most associated with escape and competition motivations. These findings suggest that before a comprehensive understanding of a new diagnostic category can be established, more research should be conducted to determine if a comorbidity between cannabis use disorder and problematic internet gaming exists.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".