Situating Problematic Video Gaming and Psychotic-Like Experiences in the Adolescent Landscape of Affordances: A Cohort Study
Bibliographic record
Abstract
ABSTRACT Background and aims Problematic gaming has been linked to increased levels of psychotic-like experiences (PLEs) in youth, but the role of environmental factors remains unclear. Using affordance theory, this study aimed to examine the association of problematic gaming with PLEs and the role of environmental factors. Methods Participants were 6492 youth (39.2% female) who reported playing video games, from the Adolescent Brain Cognitive Development Study in the U.S. Measures included problematic gaming, peer environment (number of close friends), school environment (teachers, activities, etc.), family environment (parental monitoring), and PLEs. We examined whether the peer, school, and family environments at age 12 were associated with problematic gaming and moderated its association with PLEs at age 13. Results Higher protective scores for the school and family environments at age 12 were independently associated with lower levels of problematic gaming at age 12 (respectively B=−0.15; 95% CI: −0.21, −0.10 and B=−2.39; 95% CI: −2.71, −2.07) and age 13 (B=−0.05; 95% CI: −0.10, −0.00 and B=−0.79; 95% CI: −1.11, −0.47). The peer environment was not associated with problematic gaming. Higher levels of problematic gaming at age 12 were associated with higher levels of PLEs at age 13 (B=0.13; 95% CI: 0.09, 0.17), with no significant interaction with the environmental variables. Discussion and conclusions Positive school and family environments may be protective against problematic gaming in adolescence but do not appear to attenuate the putative effect of problematic gaming on PLEs. The results provide partial support to an affordance-based conceptualization of problematic gaming.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".