Cyberbullying victimisation and internalising and externalising problems among adolescents: the moderating role of parent–child relationship and child's sex
Bibliographic record
Abstract
AIMS: Previous research has found links between cyberbullying victimisation and internalising and externalising problems among adolescents. However, little is known about the factors that might moderate these relationships. Thus, the present study examined the relationships between cyberbullying victimisation and psychological distress, suicidality, self-rated poor mental health and substance use among adolescents, and tested whether parent-child relationship and child's sex would moderate these relationships. METHODS: Self-report data on experiences of cyberbullying victimisation, self-rated poor mental health, psychological distress, suicidality and substance use were derived from the 2013 Ontario Student Drug Use and Health Survey, a province-wide school-based survey of students in grades 7 through 12 aged 11-20 years (N = 5478). Logistic regression models adjusted for age, sex, ethnicity, subjective socioeconomic status and involvement in physical fighting, bullying victimisation and perpetration at school. RESULTS: Cyberbullying victimisation was associated with self-rated poor mental health (adjusted odds ratio (OR) 2.15; 95% confidence interval (CI) 1.64-2.81), psychological distress (OR 2.41; 95% CI 1.90-3.06), suicidal ideation (OR 2.38; 95% CI 1.83-3.08) and attempts (OR 2.07; 95% CI 1.27-3.38), smoking tobacco cigarette (OR 1.96; 95% CI 1.45-2.65), cannabis use (OR 1.82; 95% CI 1.32-2.51), and binge drinking (OR 1.44; 95% CI 1.03-2.02). The association between cyberbullying victimisation and psychological distress was modified by parent-child relationship and child's sex (three-way interaction term p < 0.05). The association between cyberbullying victimisation and psychological distress was much stronger among boys who have a negative relationship with their parents. CONCLUSIONS: Findings suggest that cyberbullying victimisation is strongly associated with psychological distress in most adolescents with the exception of males who get along well with their parents. Further research using a longitudinal design is necessary to disentangle the interrelationship among child's sex, parent-child relationship, cyberbullying victimisation and mental health outcomes among adolescents in order to improve ongoing mental health prevention efforts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.002 |
| 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".