Cognitive Mechanisms Between Psychosocial Resources and the Behavioral Intention of Professional Help-Seeking for Internet Gaming Disorder Among Chinese Adolescent Gamers: Cross-Sectional Mediation Study
Bibliographic record
Abstract
Background: Internet gaming disorder (IGD) is a global public health concern for adolescents due to its potential severe negative consequences. Professional help-seeking is important for early screening, diagnosis, and treatment of IGD. However, research on the factors associated with professional help-seeking for IGD as well as relevant mediation mechanisms among adolescents is limited. Objective: Based on the stress coping theory, the conservation of resource theory, and behavioral change theories, this study investigated the prevalence and factors influencing the behavioral intention of professional help-seeking for internet gaming disorder (BI-PHSIGD). The research also explored the underlying mechanisms, including psychosocial resources like resilience and social support, perceived resource loss due to reduced gaming time, and self-efficacy, in professional help-seeking among adolescent internet gamers. Methods: A cross-sectional survey was conducted among secondary school students who were internet gamers in 2 Chinese cities from October 2019 to January 2020. Data from the full sample (N=1526) and a subsample of 256 IGD cases (according to the 9-item DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition] IGD Checklist) were analyzed. Multivariate logistic regression analysis was conducted to examine the factors of BI-PHSIGD, while structural equation modeling was performed to test the proposed mediation mechanisms. Results: The prevalence of BI-PHSIGD was 54.3% (829/1526) in the full sample and 40.6% (104/256) in the IGD subsample (vs 708/1239, 57.1% among non-IGD cases). In the full sample, psychosocial resources of resilience (adjusted odds ratio [aOR] 1.03, 95% CI 1.02-1.05) and social support (aOR 1.03, 95% CI 1.02-1.04) as well as self-efficacy in professional help-seeking (aOR 1.64, 95% CI 1.49-1.81) were positively associated with BI-PHSIGD, while perceived resource loss due to reduced gaming time was negatively associated with BI-PHSIGD (aOR 0.97, 95% CI 0.96-0.98); the positive association between psychosocial resources and BI-PHSIGD was fully mediated via 2 single-mediator indirect paths (via self-efficacy in professional help-seeking alone: effect size=53.4%; indirect effect/total effect=0.10/0.19 and via perceived resource loss due to reduced gaming time alone: effect size=17.8%; indirect effect/total effect=0.03/0.19) and one 2-mediator serial indirect path (first via perceived resource loss due to reduced gaming time then via self-efficacy in professional help-seeking: effect size=4.7%; indirect effect/total effect=0.009/0.19). In the IGD subgroup, a full mediation via self-efficacy in professional help-seeking alone but not the other 2 indirect paths was statistically significant. Conclusions: Many adolescent internet gamers, especially those with IGD, were unwilling to seek professional help; as a result, early treatment is often difficult to achieve. To increase BI-PHSIGD, enhancing psychosocial resources such as resilience and social support, perceived resource loss due to reduced gaming time, and self-efficacy in professional help-seeking may be effective. Future longitudinal and intervention studies are needed to confirm and extend the findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".