Internet Addiction and Its Relationship With Suicidal Behaviors
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review and meta-analysis of observational studies that investigated the putative association between internet addiction and suicidality. DATA SOURCES: Major electronic databases (PubMed, Embase, ClinicalKey, Cochrane Library, ProQuest, Science Direct, and ClinicalTrials.gov) were searched using the following keywords (internet addiction OR internet gaming disorder OR internet use disorder OR pathological internet use OR compulsive internet use OR problematic internet use) AND (suicide OR depression) to identify observational studies from inception to October 31, 2017. STUDY SELECTION: We included 23 cross-sectional studies (n = 270,596) and 2 prospective studies (n = 1,180) that investigated the relationship between suicide and internet addiction. DATA EXTRACTION: We extracted the rates of suicidal ideation, planning, and attempts in individuals with internet addiction and controls. RESULTS: The individuals with internet addiction had significantly higher rates of suicidal ideation (odds ratio [OR] = 2.952), planning (OR = 3.172), and attempts (OR = 2.811) and higher severity of suicidal ideation (Hedges g = 0.723). When restricted to adjusted ORs for demographic data and depression, the odds of suicidal ideation and attempts were still significantly higher in the individuals with internet addiction (ideation: pooled adjusted OR = 1.490; attempts: pooled adjusted OR = 1.559). In subgroup analysis, there was a significantly higher prevalence rate of suicidal ideation in children (age less than 18 years) than in adults (OR = 3.771 and OR = 1.955, respectively). CONCLUSIONS: This meta-analysis provides evidence that internet addiction is associated with increased suicidality even after adjusting for potential confounding variables including depression. However, the evidence was derived mostly from cross-sectional studies. Future prospective studies are necessary to confirm these findings.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".