Parental Factors Associated With Internet Gaming Disorder Among First-Year High School Students: Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Parents play central roles in adolescents' socialization, behavioral development, and health, including the development of internet gaming disorder (IGD). However, longitudinal research on the parental predictors of adolescent IGD is limited. OBJECTIVE: This study aimed to investigate the reciprocal associations between various parental factors and adolescent IGD using 2-wave cross-lagged models. METHODS: A sample of 1200 year-one high school students in central China completed a baseline assessment in 2018 (mean age 15.6 years; 633/1200, 52.8% male) and a follow-up survey in 2019. IGD was measured using the 9-item DSM-5 IGD Symptoms checklist. Perceptions related to parental variables, including psychological control, parental abuse, parental support, and the parent-child relationship, were also collected from the adolescents. RESULTS: Of all the participants, 12.4% (148/1200) and 11.7% (140/1200) were classified as having IGD at baseline (T1) and follow-up (T2), respectively. All 4 cross-lagged models fit the data well (range for the comparative fit index .91-.95; range for the standardized root mean square residual .05-.06). Parental support (β=-.06, P=.02) and parental abuse (β=.08, P=.002) at T1 predicted IGD symptoms at T2, while parental psychological control (β=.03, P=.25) and a positive relationship with parents (β=-.05, P=.07) at T1 had nonsignificant effects on IGD symptoms at T2, when controlling for background variables. In addition, IGD symptoms at T1 did not predict parental factors at T2. CONCLUSIONS: The findings suggest that parental factors may be significant predictors of adolescent IGD. Health interventions should consider involving parents to increase the effectiveness of treatment to prevent and reduce adolescent IGD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".