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Record W4402624494 · doi:10.2196/48439

Association Between Internet Gaming Disorder and Suicidal Ideation Mediated by Psychosocial Resources and Psychosocial Problems Among Adolescent Internet Gamers in China: Cross-Sectional Study

2024· article· en· W4402624494 on OpenAlexvenueno aff
Yanqiu Yu, Anise M. S. Wu, Vivian W. I. Fong, Jianxin Zhang, Jibin Li, Joseph T. F. Lau

Bibliographic record

VenueJMIR Serious Games · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsSuicidal ideationPsychosocialClinical psychologyPsychologyPsychiatryMedicinePoison controlSuicide preventionMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Adolescent internet gaming disorder (IGD) was associated with severe harm, including suicidal ideation. While suicidal ideation was predictive of completed suicides, further research is required to clarify the association between IGD and suicidal ideation among adolescents, as well as the mechanisms involved. OBJECTIVE: This study aimed to investigate the understudied association between IGD and suicidal ideation, as well as novel mechanisms associated with it, among Chinese adolescent internet gamers through psychosocial coping resources and psychosocial problems. METHODS: An anonymous, self-administered, cross-sectional survey was conducted among secondary school students who had played internet games in the past year in Guangzhou and Chengdu, China (from October 2019 to January 2020). In total, 1693 adolescent internet gamers were included in this study; the mean age was 13.48 (SD 0.80) years, and 60% (n=1016) were males. IGD was assessed by the 9-item Internet Gaming Disorder Checklist of the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]), while a single item assessed suicidal ideation: "Have you ever considered committing suicide in the past 12 months?" Univariate and multivariate logistic regression associations were conducted to test the significance and directions of the potential factors for suicidal ideation. The mediation mechanism was examined by structural equation modeling. RESULTS: Among all participants, the prevalence of IGD and suicidal ideation was 16.95% (287/1693) and 43.06% (729/1693), respectively. IGD cases were 2.42 times more likely than non-IGD cases to report suicidal ideation (adjusted odds ratio [OR] 2.42, 95% CI 1.73-3.37). Other significant factors of suicidal ideation included psychosocial coping resources (resilience and social support, both adjusted OR 0.97, 95% CI 0.96-0.98) and psychosocial problems (social anxiety: adjusted OR 1.07, 95% CI 1.05-1.09; loneliness, adjusted OR 1.13, 95% CI 1.10-1.16). The association between IGD and suicidal ideation was partially mediated by 3 indirect paths, including (1) the 2-step path that IGD reduced psychosocial coping resources, which in turn increased suicidal ideation; (2) the 2-step path that IGD increased psychosocial problems, which in turn increased suicidal ideation; and (3) the 3-step path that IGD reduced psychosocial coping resources which then increased psychosocial problems, which in turn increased suicidal ideation, with effect sizes of 10.7% (indirect effect/total effect: 0.016/0.15), 30.0% (0.05/0.15), and 13.3% (0.02/0.15), respectively. The direct path remained statistically significant. CONCLUSIONS: IGD and suicidal ideation were alarmingly prevalent. Evidently and importantly, IGD was a significant risk factor for suicidal ideation. The association was partially explained by psychosocial coping resources of resilience and social support and psychosocial problems of social anxiety and loneliness. Longitudinal studies are needed to confirm the findings. Pilot randomized controlled trials are recommended to evaluate the effectiveness of interventions in reducing suicidal ideation by reducing IGD, improving psychosocial coping resources, and reducing psychosocial problems investigated in this study.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.317
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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