Who Is at Risk for Problematic Video Gaming? Risk Factors in Problematic Video Gaming in Clinically Referred Canadian Children and Adolescents
Bibliographic record
Abstract
Both Internet and offline video gaming have become a normal aspect of child development, with estimates of children playing video games ranging from 90% to 97%. Research on problematic video gaming (PVG) has grown substantially in the last decade. Much of that research has focused on community samples, while research on clinically referred children and youth is lacking. The present study includes 5820 clinically referred children and youth across 44 mental health agencies, assessed using the interRAI Child and Youth Mental Health Assessment. Logistic regression analyses revealed that older age, male sex, extreme shyness, internalizing symptoms, externalizing symptoms, and poor relational strengths are all significant predictors of problematic video gaming (PVG). Further analyses suggested that, out of the internalizing symptoms, anhedonia was predictive of PVG in both males and females, but depressive symptoms and anxiety were not predictive of PVG when controlling for other variables in the model. Moreover, proactive aggression and extreme shyness were predictive of PVG in males, but not in females. The implications of these findings are discussed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".