The prospective associations between problematic gaming and phubbing among Chinese adolescents: Insights from a cross-lagged panel network model
Bibliographic record
Abstract
BACKGROUND AND AIMS: Previous studies are limited in addressing the directionality of temporal relationships between problematic gaming and phubbing symptoms by exploring cross-sectional studies. Therefore, we estimated the longitudinal relationships between individual behavioral addictive symptoms including problematic gaming and phubbing in adolescence, and explored potential sex differences in these relationships. METHODS: This study included 3296 participants in Shandong Province, China. Data were collected from November 2021 (mean [SD] age: 15.17 [1.44] years) to May 2023 (mean [SD] age: 17.50 [1.18] years), with females comprising 54.5 % of the sample. Problematic gaming and phubbing were assessed using validated scales at each wave. We construct cross-sectional networks and cross-lagged panel networks (CLPN) to explore the contemptuous and temporal relationships between problematic gaming and phubbing. RESULTS: Contemporaneous networks revealed significant differences in problematic gaming and phubbing networks between males and females. Additionally, temporal network analyses indicated that among male adolescents, feeling anxious when unable to play games was the most influential predictor of subsequent behavioral addictive symptoms. For female adolescents, fantasizing about gaming had the most significant associations with future addictive behaviors. The strongest bridge symptom linking problematic gaming and phubbing for both sexes was focusing on phones rather than engaging in conversation. DISCUSSION AND CONCLUSIONS: The study applied network modeling to panel data from a large, population-based cohort of adolescents, identifying unique longitudinal relationships between problematic gaming and phubbing across symptom domains. It provides valuable insights into the characterization of behavioral addictive symptoms among adolescents and the potential predictive relationships among these symptoms among different sexes, guiding sex-specific targeted interventions for 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".