The Effect of Self-esteem and Empathic Tendency on the Violence Tendency in Adolescents: A Multivariable Analysis
Bibliographic record
Abstract
Introduction: Personal, familial, and environmental factors affect the tendency to violence in people. This study was carried out to determine the effects of socio-demographic characteristics, empathic tendencies, and self-esteem in adolescents on the levels of their tendency towards violence. Method: This research is a retrospective record review. In the research, data were obtained from student information forms in high school students’ records (n = 982), Violence Tendency Scale (VTS), Rosenberg Self-Esteem Scale (RSES), and Empathic Tendency Scale (ETS). The data were analysed by multivariable Binary Logistic Regression (BLR) and Linear Regression (LR). Results: Among the adolescents, 48.2% were male, and 38.8% stated that they used social media for 2 hours or more per day. The mean scores of adolescents obtained from the VTS, ETS, and RSES was 34.8, 66.2, and 2.2, respectively, and 25.6% were prone to violence. The factors affecting violence tendency were, in order of importance, low empathic tendency, being male, long social media usage time, low perception of success by the family, father’s profession, and not getting prepared for university entrance exams, which were found to be statistically significant. The correlation between self-esteem and violence tendency was not found to be significant in LR and BLR analyses (p > 0.05). Conclusions: One-quarter of adolescents were prone to violence. The empathic tendency, gender, social media use, and familial characteristics were found to influence their tendency towards violence.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".