Social networking sites and mental health problems in adolescents: The mediating role of cyberbullying victimization
Bibliographic record
Abstract
BACKGROUND: Previous research has suggested an association between the use of social networking sites (SNSs) and mental health problems such as psychological distress, suicidal ideation and attempts in adolescents. However, little is known about the factors that might mediate these relationships. The present study examined the link between the use of social networking sites and psychological distress, suicidal ideation and suicide attempts, and tested the mediating role of cyberbullying victimization on these associations in adolescents. METHODS: The sample consisted of a group of 11-to-20-year-old individuals (n=5126, 48% females; mean±SD age: 15.2±1.9 years) who completed the mental health portion of the Ontario Student Drug Use and Health Survey (OSDUHS) in 2013. Multiple logistic regression analyses were used to test the mediation models. RESULTS: After adjustment for age, sex, ethnicity, subjective socioeconomic status (SES), and parental education, use of SNSs was associated with psychological distress (adjusted odds ratio, 95% confidence interval=2.03, 1.22-3.37), suicidal ideation (3.44, 1.54-7.66) and attempts (5.10, 1.45-17.88). Cyberbullying victimization was found to fully mediate the relationships between the use of SNSs with psychological distress and attempts; whereas, it partially mediated the link between the use of SNSs and suicidal ideation. CONCLUSION: Findings provide supporting evidence that addressing cyberbullying victimization and the use of SNSs among adolescents may help reduce the risk of mental health problems.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".