Gender and Geographic Predictors of Cyberbullying Victimization, Perpetration, and Coping Modalities Among Youth
Bibliographic record
Abstract
Cyberbullying has become an important public health issue due to documented associations among victimization, perpetration, and greater likelihood of depression, substance abuse, anxiety, insomnia, and school-related problems for adolescents. Less is known, however, about how youth cope with cyberbullying and the types of services and supports they are likely to use based on relevant socioeconomic, demographic and geographic factors. The objective of this project was to determine whether gender and geography, in combination with mental health and socioeconomic status, predicted cyberbullying victimization, perpetration, and patterns of coping and help seeking in a sample of youth aged 16 to 19 years ( N = 289). An anonymous online survey was used to gather information on cyberbullying victimization, perpetration, and methods for coping from youth from New Brunswick, Canada. The results of this study suggest that the likelihood of becoming a cyberbullying victim or perpetrator, as well as the coping modalities used to respond to bullying, are highly gendered and intersect with existing social and health inequities. Interventions aimed at bolstering resiliency should be developed in the context of the urban and rural school environments where coping skills are developed and refined.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".