Empathic Skills Training As a Means of Reducing Cyberbullying among Adolescents: An Empirical Evaluation
Bibliographic record
Abstract
Cyberbullying is a form of aggression in which electronic communication such as e-mails, mobile phone calls, text messages, instant messenger contacts, photos, social networking sites and personal webpages are used to threaten or intimidate individuals. Cognitive-behavioral therapy (CBT) counselling based on empathic training may reduce cyberbullying among adolescents. The present study investigated the impact of developing empathy skills in reducing cyberbullying among a sample of adolescents using two groups (i.e., an experimental group and control group). The experimental group received counselling intervention based on CBT with special focus on improving empathy whereas the control group received CBT general counselling. The participants comprised 217 adolescents (experimental group = 98 adolescents, control group = 119 adolescents) with a mean age of 15.1 years (SD ± 1.5). The measures included the Toronto Empathy Questionnaire (TEQ) and the Bullying, Cyberbullying Scale for Adolescents (BCS-A). Results showed that there were statistically significant differences on TEQ scores and BCS-A scores in the experimental and control groups after the intervention but more so in favor of the experimental group in terms of reduced levels of cyberbullying (both victimization and perpetration). Positive gains among the experimental group in both empathy and reduced cyberbullying remained at two-month follow-up. It is recommended that teachers and school counselors tackling cyberbullying should use empathy training as part of their cyberbullying prevention programs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".