Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescents
Background: Cyberbullying is a major health issue across the globe as it affects the mental health and well-being of the victims, especially children and adolescents, since there is a dearth of studies in the Indian setting. This study aimed to understand the predictors, patterns, prevalence, mental...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2023-07-01
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| Series: | Indian Journal of Psychiatry |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/indianjpsychiatry.indianjpsychiatry_313_23 |
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| _version_ | 1825206238555668480 |
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| author | Prabhu James Ranjith Mysore Narasimaha Vranda M. Thomas Kishore |
| author_facet | Prabhu James Ranjith Mysore Narasimaha Vranda M. Thomas Kishore |
| author_sort | Prabhu James Ranjith |
| collection | DOAJ |
| description | Background:
Cyberbullying is a major health issue across the globe as it affects the mental health and well-being of the victims, especially children and adolescents, since there is a dearth of studies in the Indian setting. This study aimed to understand the predictors, patterns, prevalence, mental health problems, and coping of cyberbullying among adolescents.
Methods:
The study adopted a cross-sectional explorative design with 484 adolescents studying in 8th to 12th standards recruited through convenient sampling. The Cyberbullying Online Aggression Survey Instrument (COASI), Strength and Difficulties Questionnaire (SDQ), Coping with Cyberbullying Questionnaire (CWCBQ), and Internet usage pattern were administered to collect the data. Multinomial logistic regression was used to find the predictors of the only cyber-victim, only cyber-offending, and both cyber-victim and cyber-offending.
Results:
The findings showed that 14.5% of teenagers were victims of cyberbullying, 5.8% were offenders, and 13.8% were both victims and offenders. The predictors for “cyber-victimization” were father’s education and religion. The predictors of “cyber-offenders” were grade, Internet usage, and father’s occupation. Adolescents identified as both cyber-victims and cyber-offenders were predicted by gender, grade, religion, and father’s employment.
Conclusion:
The study indicated a high prevalence of cyber-victimization and cyber-offending among adolescents with many psychosocial risk factors. The findings highlighted the need for a school-based cyberbullying intervention program to address the issues using a whole-school approach. |
| format | Article |
| id | doaj-art-776beef85be94dc586eb64b76d75fc0a |
| institution | Kabale University |
| issn | 0019-5545 1998-3794 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Indian Journal of Psychiatry |
| spelling | doaj-art-776beef85be94dc586eb64b76d75fc0a2025-02-07T11:21:18ZengWolters Kluwer Medknow PublicationsIndian Journal of Psychiatry0019-55451998-37942023-07-0165772072810.4103/indianjpsychiatry.indianjpsychiatry_313_23Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescentsPrabhu James RanjithMysore Narasimaha VrandaM. Thomas KishoreBackground: Cyberbullying is a major health issue across the globe as it affects the mental health and well-being of the victims, especially children and adolescents, since there is a dearth of studies in the Indian setting. This study aimed to understand the predictors, patterns, prevalence, mental health problems, and coping of cyberbullying among adolescents. Methods: The study adopted a cross-sectional explorative design with 484 adolescents studying in 8th to 12th standards recruited through convenient sampling. The Cyberbullying Online Aggression Survey Instrument (COASI), Strength and Difficulties Questionnaire (SDQ), Coping with Cyberbullying Questionnaire (CWCBQ), and Internet usage pattern were administered to collect the data. Multinomial logistic regression was used to find the predictors of the only cyber-victim, only cyber-offending, and both cyber-victim and cyber-offending. Results: The findings showed that 14.5% of teenagers were victims of cyberbullying, 5.8% were offenders, and 13.8% were both victims and offenders. The predictors for “cyber-victimization” were father’s education and religion. The predictors of “cyber-offenders” were grade, Internet usage, and father’s occupation. Adolescents identified as both cyber-victims and cyber-offenders were predicted by gender, grade, religion, and father’s employment. Conclusion: The study indicated a high prevalence of cyber-victimization and cyber-offending among adolescents with many psychosocial risk factors. The findings highlighted the need for a school-based cyberbullying intervention program to address the issues using a whole-school approach.https://journals.lww.com/10.4103/indianjpsychiatry.indianjpsychiatry_313_23adolescentscopingcyberbullyingcyber-victimizationmental healthpatternspredictorsprevalence |
| spellingShingle | Prabhu James Ranjith Mysore Narasimaha Vranda M. Thomas Kishore Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescents Indian Journal of Psychiatry adolescents coping cyberbullying cyber-victimization mental health patterns predictors prevalence |
| title | Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescents |
| title_full | Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescents |
| title_fullStr | Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescents |
| title_full_unstemmed | Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescents |
| title_short | Predictors, prevalence, and patterns of cyberbullying among school-going children and adolescents |
| title_sort | predictors prevalence and patterns of cyberbullying among school going children and adolescents |
| topic | adolescents coping cyberbullying cyber-victimization mental health patterns predictors prevalence |
| url | https://journals.lww.com/10.4103/indianjpsychiatry.indianjpsychiatry_313_23 |
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