The Relationship Between Epidemic Perception and Cyberbullying Behaviors of Chinese Adolescents During the COVID-19 Pandemic: Cross-Sectional Study
Bibliographic record
Abstract
Background: In response to the COVID-19 outbreak, the government initiated measures for social distancing, leading to a gradual transition of adolescents' social interactions toward web-based platforms. Consequently, web-based behaviors, particularly cyberbullying, have become a prominent concern. Considering that adolescents experience more intense feelings, the widely increased negative emotions and strains perceived from the COVID-19 pandemic may end up engaging in cyberbullying behaviors. In addition, during the COVID-19 pandemic, adolescents experiencing insomnia and negative affect are more prone to diminished self-control, which is associated with cyberbullying behaviors. Objective: This study aims to investigate the relationship between epidemic perception and cyberbullying behaviors, while also examining the serial mediating roles of insomnia and negative affect on the relationship between epidemic perception and cyberbullying behaviors. Methods: This study presents a large-scale web-based survey conducted during the period of concentrated COVID-19 outbreaks, encompassing 20,000 Chinese adolescents. A total of 274 submitted questionnaires were discarded because of high levels of missing data or their answers were clearly fictitious or inconsistent. The final count of valid participants amounted to 19,726 (10,371 boys, age range: 12-18 years; mean 14.80, SD 1.63 years). The Perceptions of COVID-19 Scale, Negative Affect Scale, Insomnia Scale, and Cyberbullying Behavior Scale were used to assess participants' responses on the Questionnaire Star platform. Results: The results show that epidemic perception is positively correlated with cyberbullying behaviors (r=0.13; P<.001), insomnia (r=0.19; P<.001), and negative affect (r=0.25; P<.001). Insomnia is positively correlated with negative affect (r=0.44; P<.001) and cyberbullying behaviors (r=0.30; P<.001). Negative affect is positively correlated with cyberbullying behaviors (r=0.25; P<.001). And insomnia and negative affect play independent mediating and serial mediating roles in epidemic perception and cyberbullying behaviors. Conclusions: This study provides additional empirical evidence on the relationship between the perception of COVID-19 pandemic and cyberbullying in adolescents. In addition, the study offers recommendations for implementing interventions targeted at mitigating cyberbullying in adolescents during the COVID-19 pandemic.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".