Alexithymia and bullying behavior in students
Bibliographic record
Abstract
Background: Bullying behavior among students has become a severe problem that affects their mental and physical well-being. The factors that cause bullying behavior are very complex and involve various aspects. One factor that is receiving increasing attention is alexithymia.Purpose: This study explores the relationship between alexithymia and student bullying behavior.Methods: The research used a quantitative design with a cross-sectional study approach. The research sample consisted of 120 class X students at Senior High School X in Bandung, selected using a total sampling technique. Data was collected by distributing online questionnaires, including the Toronto Alexithymia Scale-20 (TAS-20) to measure alexithymia and the Olweus Bully/Victim Questionnaire for bullying behavior. Data were analyzed using descriptive statistics and the Spearman correlation test.Results: The data obtained show that less than half of the respondents (36.7%) experienced high levels of alexithymia, 38.3% likely experienced possible alexithymia, and 25% did not experience alexithymia, while the majority of respondents exhibited high bullying behavior (43.5%). This study shows a positive relationship between alexithymia and student bullying behavior (p <0.0001; r: 0.309).Conclusion: Alexithymia can increase bullying behavior in school children. The higher the level of alexithymia, the greater the tendency to engage in bullying behavior.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".