Cyber victimization, cyber aggression, and adolescent alcohol use: Short‐term prospective and reciprocal associations<sup>⋆</sup><sup>,</sup><sup>⋆⋆</sup>
Bibliographic record
Abstract
INTRODUCTION: Cyber victimization is a significant public health concern. We examined prospective and reciprocal associations between cyber victimization, cyber aggression, and adolescents' drinking and binge drinking. Gender, Hispanic ethnicity, and age were examined as moderators. METHODS: Adolescents (N = 1140; 58% girls; 13-19 years; 80% Hispanic) from two US high schools completed the Cyber Peer Experiences Questionnaire and alcohol use items from the Youth Risk Behavior Survey at two time points, three months apart. Perceived social support was assessed at Time 1 and controlled for. Cross-lagged panel analyses using structural equation modeling were conducted, using zero-inflated negative binomial regressions for alcohol use outcomes. RESULTS: Adolescents who experienced more cyber victimization were more likely to abstain from drinking over time; however, they reported more frequent drinking if they were a drinker, a relationship that was stronger for older adolescents. Cyber victimization was unrelated to binge drinking, and alcohol use was unrelated to cyber victimization over time. Adolescents who engaged in more cyber aggression were more likely to use alcohol over time; conversely, adolescents who used alcohol more frequently engaged in more cyber aggression over time. Gender and ethnicity did not moderate these associations. CONCLUSIONS: A complex relationship between cyber victimization and alcohol use emerged; findings support the self-medication hypothesis among drinkers only and especially older adolescents. Reciprocal associations between cyber aggression and alcohol use fit with problem behavior theory Adolescent alcohol use prevention programs might play an important role in addressing cyber aggression. Drinking behaviors may be important to target in anti-cyberbullying interventions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".