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Canadian Journal of Public Health

, Volume 108, Issue 5–6, pp e475–e481 | Cite as

Cybervictimization among preadolescents in a community-based sample in Canada: Prevalence and predictors

  • Ahmad Mobin
  • Cindy Xin FengEmail author
  • Cory Neudorf
Quantitative Research
  • 3 Downloads

Abstract

OBJECTIVES: To examine the prevalence and predictors associated with cybervictimization among preadolescents in a community-based sample from Canada.

METHODS: Data were drawn from a cohort of 5783 students of grades 5–8, aged 9–14 from 109 elementary schools at the Saskatoon Health Region, Saskatchewan of Canada based on the Student Health Survey in the year of 2010–2011. Multivariate logistic regression with the generalized estimating equation was used to determine the individual and contextual factors associated with self-reported cybervictimization.

RESULTS: Of the 5783 school children, 5611 (97.0%) responded to the question regarding cybervictimization. Among those respondents, 572 (10.2%) reported being cyberbullied at least once in the past four weeks. The students most likely to be victimized by cyberbullying were girls, students in grades 7 and 8 compared with grade 5, Aboriginal students compared to non-Aboriginal students, those who had lived part of their life outside of Canada compared with those who lived all of their life in Canada, those who reported drinking alcohol in the past, those who reported very elevated depressive symptoms, those who were traditionally bullied, those who had low self-esteem, and those who had a poor relationship with their parents. School-level variation in cyberbullying victimization is negligible. School neighbour-level deprivation is not significant after adjusting for individual-level characteristics and parent-child relationship.

CONCLUSION: Our findings identified important characteristics of préadolescents with higher susceptibility to cybervictimization in a Canadian setting, which can be used to develop intervention strategies for mitigating cybervictimization among the study population.

Key words

Cyberbullying victimization ecological systems theory psychological factors traditional bullying 

Résumé

OBJECTIFS: Examiner la prévalence et les variables prédictives associées à la cybervictimisation chez les préadolescents dans un échantillon communautaire au Canada.

MÉTHODE: Les données provenaient d’une cohorte de 5 783 élèves de la 5e à la 8e année âgés de 9 à 14 ans et fréquentant 109 écoles primaires de la Région sanitaire de Saskatoon (en Saskatchewan, au Canada) d’après une enquête sur la santé des élèves (Student Health Survey) menée en 2010–2011. Une analyse de régression logistique multivariée avec équation d’estimation généralisée a servi à déterminer les facteurs individuels et contextuels associés à la cybervictimisation autodéclarée.

RÉSULTATS: Sur 5 783 enfants d’âge scolaire, 5 611 (97 %) ont répondu à la question sur la cybervictimisation. De ces répondants, 572 (10,2 %) ont déclaré avoir été victimes de cyberintimidation au moins une fois au cours des quatre semaines précédentes. Les élèves les plus susceptibles d’avoir été victimes de cyberintimidation étaient les filles, les élèves de 7e et de 8e année (par opposition aux élèves de 5e année), les élèves autochtones (par opposition aux élèves non autochtones), les élèves ayant vécu une partie de leur vie hors du Canada (par opposition à ceux ayant vécu au Canada toute leur vie), les élèves ayant déclaré avoir bu de l’alcool par le passé, ceux ayant déclaré des symptômes dépressifs très élevés, ceux ayant été victimes de brimades classiques, ceux qui avaient une faible estime de soi, et ceux qui étaient en mauvais termes avec leurs parents. Les écarts d’une école à l’autre en matière de cyberintimidation étaient négligeables. La défavorisation des écoles selon le quartier n’était pas un facteur significatif après élimination des effets des caractéristiques individuelles et de la qualité de la relation parent-enfant.

CONCLUSION: Nos résultats ont mis en lumière d’importantes caractéristiques chez les préadolescents les plus susceptibles d’être victimes de cyberintimidation dans un milieu canadien; ils peuvent servir à élaborer des stratégies d’intervention pour atténuer la cybervictimisation dans la population étudiée.

Mots clés

cybervictimisation théorie des systèmes écologiques facteurs psychologiques brimades classiques 

References

  1. 1.
    Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N. Cyberbullying: Its nature and impact in secondary school pupils. J Child Psychol Psychiatry 2008;49(4):376–85. PMID: 18363945. doi: 10.1111/j.1469-7610.2007.01846.x.CrossRefGoogle Scholar
  2. 2.
    Vreeman RC, Carroll AE. A systematic review of school-based interventions to prevent bullying. Arch Pediatr Adolesc Med 2007;161(1):78–88. PMID: 17199071. doi: 10.1001/archpedi.l61.1.78.CrossRefGoogle Scholar
  3. 3.
    Cross D, Lester L, Barnes A. A longitudinal study of the social and emotional predictors and consequences of cyber and traditional bullying victimisation. Int J Public Health 2015;60(2):207–17. PMID: 25645100. doi: 10.1007/s00038-015-0655-1.CrossRefGoogle Scholar
  4. 4.
    Mishna F, Cook C, Gadalla T, Daciuk J, Solomon S. Cyber bullying behaviors among middle and high school students. Am J Orthopsychiatry 2010; 80(3):362–74. PMID: 20636942. doi: 10.1111/j.1939-0025.2010.01040.X.CrossRefGoogle Scholar
  5. 5.
    Deschamps R, McNutt K. Cyberbullying: What’s the problem? Can Public Admin 2016;59(1):45–71. doi: 10.1111/capa.l2159.CrossRefGoogle Scholar
  6. 6.
    Bronfenbrenner U, Morris PA. The ecology of developmental processes. In: Lerner RM (Ed.), Theoretical Models of Human Development, 5th ed. Handbook of Child Psychology; Vol. 1. New York, NY: Wiley, 1998; 993–1028.Google Scholar
  7. 7.
    Slonje R, Smith PK, Frisén A. The nature of cyberbullying, and strategies for prevention. Comput Human Behav 2013;29(1):26–32. doi: 10.1016/j.chb.2012. 05.024.CrossRefGoogle Scholar
  8. 8.
    Moore PM, Huebner ES, Hills KJ. Electronic bullying and victimization and life satisfaction in middle school students. Soc Indic Res 2012;107(3):429–47. doi: 10.1007/S11205-011-9856-Z.CrossRefGoogle Scholar
  9. 9.
    Hinduja S, Patchin JW. Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behav 2008;29(2):129–56. doi: 10.1080/01639620701457816.CrossRefGoogle Scholar
  10. 10.
    Vieno A, Gini G, Santinello M. Different forms of bullying and their association to smoking and drinking behavior in Italian adolescents. J Sch Health 2011;81(7):393–99. PMID: 21668879. doi: 10.1111/j.1746-1561.2011. 00607.x.CrossRefGoogle Scholar
  11. 11.
    Lemstra ME, Nielsen G, Rogers MR, Thompson AT, Moraros JS. Risk indicators and outcomes associated with bullying in youth aged 9–15 years. Can J Public Health 2012;103(1):9. PMID: 22338321.PubMedGoogle Scholar
  12. 12.
    Chen L, Ho SS, Lwin MO. A meta-analysis of factors predicting cyberbullying perpetration and victimization: From the social cognitive and media effects approach. New Media & Soc 2016;19:1194–213. doi: 10.1177/1461444816634037.CrossRefGoogle Scholar
  13. 13.
    Cappadocia MC, Craig WM, Pepler D. Cyberbullying. Can J Sch Psychol 2013; 28(2):171–92. doi: 10.1177/0829573513491212.CrossRefGoogle Scholar
  14. 14.
    Eslea M, Menesini E, Morita Y, O’Moore M, Mora-Merchán JA, Pereira B, et al. Friendship and loneliness among bullies and victims: Data from seven countries. Aggr Behav 2004;30(1): 71–83. doi: 10.1002/ab.20006.CrossRefGoogle Scholar
  15. 15.
    Khoury-Kassabri M, Benbenishty R, Astor RA, Zeira A. The contributions of community, family, and school variables to student victimization. Am J Community Psychol 2004;34(3-4):187–204. PMID: 15663206. doi: 10.1007/s10464-004-7414-4.CrossRefGoogle Scholar
  16. 16.
    Jansen PW, Verlinden M, Dommisse-van Berkel A, Mieloo C, van der Ende J, Veenstra R, et al. Prevalence of bullying and victimization among children in early elementary school: Do family and school neighbourhood socioeconomic status matter? BMC Public Health 2012;12(1):494. doi: 10. 1186/1471-2458-12-494.CrossRefGoogle Scholar
  17. 17.
    Modupalli K, Cushon J, Neudorf C. 2010/2011 Student Health Survey: Evidence for Action. Saskatoon, SK: Saskatoon Health Region, 2013.Google Scholar
  18. 18.
    Wang J, Iannotti RJ, Nansel TR. School bullying among adolescents in the United States: Physical, verbal, relational, and cyber. J Adolesc Health 2009; 45(4):368–75. PMID: 19766941. doi: 10.1016/j.jadohealth.2009.03.021.CrossRefGoogle Scholar
  19. 19.
    Poulin C, Hand D, Boudreau B. Validity of a 12-item version of the CES-D used in the National Longitudinal Study of Children and Youth. Chronic Dis Can 2005;26(2-3):65–72. PMID: 16251012.PubMedGoogle Scholar
  20. 20.
    Pampalon R, Hamel D, Gamache P, Raymond G. A deprivation index for health planning in Canada. Chronic Dis Can 2009;29(4):178–91. PMID: 19804682.PubMedGoogle Scholar
  21. 21.
    Li Q. Cyberbullying in high schools: A study of students’ behaviors and beliefs about this new phenomenon. J Aggress Maltreat 2010;19(4):372–92. doi: 10. 1080/10926771003788979.CrossRefGoogle Scholar
  22. 22.
    Khurana A, Bleakley A, Jordan A, Romer D. The protective effects of parental monitoring and internet restriction on adolescents’ risk of online harassment. J Youth Adolesc 2015;44(5):1039–47. PMID: 25504217. doi: 10.1007/sl0964-014-0242-4.CrossRefGoogle Scholar
  23. 23.
    Brownlee K, Martin J, Rawana E, Harper J, Mercier M, Neckoway R, et al. Bullying behaviour and victimization among aboriginal students within northwestern Ontario. First Peoples Child Fam Rev 2014;9(1).Google Scholar
  24. 24.
    Sampasa-Kanyinga H, Hamilton HA. Use of social networking sites and risk of cyberbullying victimization: A population-level study of adolescents. Cyberpsychol Behav Soc Netw 2015;18(12):704. PMID: 26539738. doi: 10. 1089/cyber.2015.0145.CrossRefGoogle Scholar
  25. 25.
    Arseneault L, Bowes L, Shakoor S. Bullying victimization in youths and mental health problems: ‘Much ado about nothing’? Psychol Med 2010; 40(5):717–29. PMID: 19785920. doi: 10.1017/s0033291709991383.CrossRefGoogle Scholar
  26. 26.
    Korchmaros JD, Mitchell KJ, Ybarra ML. Technology-based interpersonal victimization: Predictors of patterns of victimization over time. J Interpers Violence 2014;29(7):1297–317. PMID: 24269990. doi: 10.1177/088626 0513506277.CrossRefGoogle Scholar
  27. 27.
    Kowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychol Bull 2014;140(4):1073–137. PMID: 24512111. doi: 10. 1037/a0035618.CrossRefGoogle Scholar
  28. 28.
    Mishna F, Khoury-Kassabri M, Gadalla T, Daciuk J. Risk factors for involvement in cyber bullying: Victims, bullies and bully-victims. Child Youth Serv Rev 2012;34(1):63–70. doi: 10.1016/j.childyouth.2011.08.032.CrossRefGoogle Scholar
  29. 29.
    Nansel TR, Overpeck M, Pilla RS, Ruan WJ, Simons-Morton B, Scheidt P. Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. JAMA 2001;285(16):2094–2100. PMID: 11311098. doi: 10.1001/jama.285.16.2094.CrossRefGoogle Scholar
  30. 30.
    Hemphill SA, Tollit M, Kotevski A, Heerde JA. Predictors of traditional and cyber-bullying victimization: A longitudinal study of Australian secondary school students. J Interpers Violence 2015;30(15):2567–90. PMID: 25315480. doi: 10.1177/0886260514553636.CrossRefGoogle Scholar

Copyright information

© The Canadian Public Health Association 2017

Authors and Affiliations

  1. 1.School of Public HealthUniversity of SaskatchewanSaskatoonCanada
  2. 2.Saskatoon Health Region, Community Health & EpidemiologyUniversity of SaskatchewanSaskatoonCanada

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