Use of Social Networking Sites and Risk of Cyberbullying Victimization: A Population-Level Study of Adolescents
Bibliographic record
Abstract
Social networking sites (SNSs) have gained considerable popularity among youth in recent years; however, there is a noticeable paucity of research examining the association between the use of these web-based platforms and cyberbullying victimization at the population level. This study examines the association between the use of SNSs and cyberbullying victimization using a large-scale survey of Canadian middle and high school students. Data on 5,329 students aged 11-20 years were derived from the 2013 Ontario Student Drug Use and Health Survey. Logistic regression was used to examine the relationship between the use of SNSs and cyberbullying victimization while adjusting for covariates. Overall, 19 percent of adolescents were cyberbullied in the past 12 months. Adolescents who were female, younger, of lower socioeconomic status, and who used alcohol or tobacco were at greater odds of being cyberbullied. The use of SNSs was associated with an increased risk of cyberbullying victimization in a dose-response manner (p-trend <0.001). Gender was not a significant moderator of the association between use of SNSs and being cyberbullied. Results from this study underscore the need for raising awareness and educating adolescents on effective strategies to prevent cyberbullying victimization.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".