Associations between relative deprivation and problematic gaming in adolescence: evidence from an Italian representative sample
Bibliographic record
Abstract
BACKGROUND: Problematic gaming is a known risk factor for adolescent well-being. Yet, socioeconomic factors that might contribute to problematic gaming in adolescence have not been fully explored. This study examined the association between relative deprivation, defined as an individual's financial gap relative to their classmates, and problematic gaming in a representative sample of Italian adolescents. METHODS: We analysed data on family material assets and self-reported symptoms of problematic gaming from 58 881 participants in the 2021/2022 Italian Health Behaviour in school-aged children study. Relative deprivation was measured using the Yitzhaki index, with classmates as a social reference group. Associations with problematic gaming were tested using a two-level multiple logistic regression model while accounting for the effects of economic, sociodemographic and psychological factors. RESULTS: Adolescents reporting higher relative deprivation were eight times more likely to be classified as problematic gamers compared with their better-off peers, after controlling for individual and class-level deprivation, and self-efficacy beliefs. Males, younger adolescents and adolescents with lower self-efficacy were more at risk of being problematic gamers. CONCLUSION: The current study expands the existing literature on the detrimental impact of inequalities on adolescent behaviours by highlighting the association between relative deprivation and problematic gaming. Policymakers are advised to implement measures to reduce inequalities and mitigate maladaptive gaming patterns among adolescents.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".