Problem gaming and adolescents’ health and well-being: Evidence from a large nationally representative sample in Italy
Bibliographic record
Abstract
Playing video games is a common leisure activity for adolescents, but a minority can develop maladaptive gaming patterns and experience impairments in various health domains. Most research has been conducted within the dichotomy of “non-problematic gaming” and “problematic gaming” with convenience and unrepresentative samples, necessitating further investigation to provide more robust and generalizable evidence. In this study, we examined the impact of gaming on different groups of gamers with distinct degrees of gaming involvement in relation to various psychological and physical health outcomes and behaviours. Data included a nationally representative sample of 89321 adolescents (11-17 years) from the 2022 Italian Health Behaviour in School-aged Children study. We compared groups of gamers (low risk, high risk, and problematic) with non-gamers concerning their (mental) health, nutrition, physical activity, sleep, and social well-being. Logistic regressions were used to estimate the odds ratios (adjusted for gender, age, material deprivation, and family structure). Compared with non-gamers (33.7% of the sample), low-risk gamers (51.6%) reported better health-related outcomes (lower risk of depression, lower stress, fewer psychological and somatic symptoms). High-risk (11.6%) and problematic gamers (3.1%) showed significantly higher impairments in all health-related outcomes than non-gamers did, the associations being especially pronounced in the problematic gaming group. Video games are not inherently harmful, and adolescents who reported a low risk of gaming problems showed slightly better health-related outcomes than non-gamers did. However, a minority of vulnerable users engaged in problematic use associated with negative consequences, functional impairment (e.g., sleep interference), and various unhealthy behaviours. • Low-risk gamers show slightly better health-related outcomes than non-gamers do • High-risk and problematic gamers report worse health/well-being than non-gamers do • The associations are especially pronounced in the problematic gaming group • Problematic gamers are most likely to experience sleep problems • Results call for the implementation of tailored interventions and preventive actions
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".