Association of cyberbullying victimization and substance initiation: The Adolescent Brain Cognitive Development (ABCD) study
Bibliographic record
Abstract
BACKGROUND: Evidence shows that cyberbullying is an important risk factor for various adverse mental health outcomes, such as substance use. However, there is limited evidence from longitudinal studies that assessed whether cyberbullying victimization is associated with substance use initiation, especially among adolescent population. METHODS: Using data from the Adolescent Brain Cognitive Development Study, we assessed the association between cyberbullying victimization and substance use initiation among adolescents. In the cross-sectional analysis at year 2, multivariable logistic regressions were used to assess the association between cyberbullying victimization history and substance use initiation. Additionally, the association between year 2 cyberbullying victimization in the past 12 months/lifetime and year 3 substance use initiation was assessed using multivariable logistic regression. RESULTS: Adjusting for sociodemographic characteristics and the presence of depression/anxiety symptoms, lifetime history of cyberbullying victimization was significantly associated with substance use initiation (OR= 2.17, 95% CI: 1.68, 2.81). Recent cyberbullying victimization in the past 12 months was associated with two-times higher odds of initiating substances (OR= 2.31, 95% CI: 1.71, 3.12). In addition, both lifetime history of cyberbullying victimization and recent cyberbullying victimization at year 2 were associated with two times increased risk in substance use initiation at year 3 (OR = 2.22, 95% CI: 1.68, 2.93; OR = 2.34, 95% CI: 1.68, 3.26). CONCLUSION: There is a significant relationship between cyberbullying victimization and substance use initiation among adolescents. Cyberbullying victims are at an increased risk of initiating substance use later in life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".