Cyberbullying and Its Impact on Self-Esteem and Emotional and Behavioral Problems Among University Students in Kuwait – A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Cyberbullying is a modern phenomenon with public health implications due to the associated serious mental disorders, emotional distress, substance use, and suicidal behaviour. Young people are at a higher risk of cyberbullying and its complications. This study aimed to determine the prevalence of cyberbullying and its relationship with self-esteem and behavioural problems among Kuwait University Students. METHODS: A cross-sectional study was conducted in seven randomly selected colleges of Kuwait University using a self-administered questionnaire. The questionnaire included three sections: Cyberbullying questions, the Rosenberg Self-Esteem Scale, and the Strengths and Difficulties Questionnaire. RESULTS: A total of 1252 students were included with a mean age of 20.58 years. Most students were females (n=1049, 84.2%), single (n=1078, 86.4%) and Kuwaiti (89.3%). Of the cohort, 194 students (15.8%) have been cyberbullied in their lifetime and 4% (n=49) were cyberbullied in the last 30 days. Female students (OR =2.677, P<0.001) and students with divorced (OR=2.35, P<0.006) or separated (OR=3.730, P<0.006) parents had a higher risk of being affected by cyberbullying. In addition, participants who were dissatisfied with their financial situation were more likely to be affected by cyberbullying (OR=1.096, P=0.008). Emotional problems (P<0.001), conduct problems (P<0.001), hyperactivity problems (P=0.029), peer problems (P<0.001), externalizing problems (P<0.001) and internalizing problems (P<0.001) were higher among students who were exposed to cyberbullying in their lifetime compared to other students. CONCLUSION: This study revealed a relatively high prevalence of cyberbullying among college students in Kuwait. Since emotional problems and self-esteem are significantly related to cyberbullying, university-wide public health promotion campaigns are encouraged to address the negative consequences of cyberbullying on students’ psychological health.
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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 | 0.001 |
| 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 | 0.001 |
| 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".