School Climate and School Identification as Determinants of Internet Gaming Disorder Among Chinese Adolescent Internet Gamers: Cross-Sectional Mediation Study
Bibliographic record
Abstract
Background: School climate and school identification are important features of the school environment and potential determinants of adolescent internet gaming disorder (IGD). Objective: This novel study investigated their joint effects on IGD and related mediation mechanisms via the interpersonal factor of teacher-student relationship and the individual factors of academic stress and anxiety. Methods: A large-scale cross-sectional study was conducted among adolescent internet gamers of junior, senior, and vocational middle schools in Taizhou City, China, from February to March 2022 (N=5778). Participants self-administered an anonymous, structured questionnaire in classrooms. Adjusted logistic regression and structural equation modeling (SEM) were used for data analysis. Results: Among all participants, the prevalence of IGD was 8% (461/5778). The 4 school climate subscales (student-student relationship subscale: adjusted odds ratio [ORa] 0.88, 95% CI 0.85-0.91; student-staff relations subscale: ORa 0.87, 95% CI 0.84-0.90; academic emphasis subscale: ORa 0.88, 95% CI 0.85-0.91; shared values approach: ORa 0.88, 95% CI 0.85-0.90), the school identification subscale (ORa 0.85, 95% CI 0.83-0.88), and teacher-student relationship (ORa 0.80, 95% CI 0.76-0.84) were significant protective factors against IGD, while academic stress (ORa 1.18, 95% CI 1.14-1.23) and anxiety (ORa 1.16, 95% CI 1.14-1.18) were risk factors of IGD. The SEM showed that the negative associations between school climate and IGD and between school identification and IGD were mediated via (1) three 2-step paths, each involving a single mediator-teacher-student relationship, academic stress, and anxiety, respectively-and (2) two 3-step paths involving 2 mediators-teacher-student relationship and academic stress first, respectively, and then anxiety. The direct effect of school climate on IGD was statistically nonsignificant (ie, full mediation with effect size ranging from 4.2% to 20.4%), while that of school identification was statistically significant (ie, partial mediation with effect size ranging from 4.5% to 38.2%). Conclusions: The relatively high prevalence of IGD among Chinese adolescents may be reduced through school-based interventions to improve school climate and school identification. Such improvements may reduce the levels of risk factors of IGD (poor teacher-student relationship, academic stress, and anxiety) and hence the risk of IGD. Future longitudinal and intervention studies are needed to confirm the findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".