Session Information
08 SES 04 A, Perspectives on School Bullying, Cyberbullying and Teacher Victimisation
Paper Session
Contribution
Bullying can be defined as a specific form of aggressive behavior exhibited by an individual or a group towards another person, characterized by a perceived or observed power imbalance and persistence over time (Hellström, Thornberg & Espelage, 2021). Cyberbullying, as consensus suggests, refers to bullying occurring through online platforms or mobile devices (Campbell & Bauman, 2018), and we will adhere to this interpretation. Research indicates that exposure to bullying significantly contributes to children's mental health issues independent of other factors (Arsenault, 2018; Arseneault et al., 2010). Even though bullying is not satisfying the A-criterion, symptoms following exposure align with symptoms of post-traumatic stress disorder (PTSD) (APA, 2013). Among the symptom groups highlighted in the DSM-5, the re-experience of the traumatic event, persistent avoidance of trauma-related stimuli, and ongoing symptoms of heightened arousal are often emphasized.
While evidence linking school bullying directly to causing PTSD is limited (Nielsen et al., 2015), a clear association between bullying and PTSD symptoms has been established. To further comprehend the relationship between school bullying and PTSD symptoms and/or diagnosis, our current systematic review and meta-analysis aim to investigate the extent of this association. This study serves as an update to the meta-analysis conducted by Nielsen et al. (2015), with specific modifications. Unlike Nielsen et al.'s study, our focus is solely on school bullying concerning the diagnosis of PTSD and/or PTSD symptoms. Additionally, we conducted a more comprehensive and systematic search of published peer-reviewed studies, without any time constraints.
Our primary research questions are as follows:
a) What is the degree of association between school bullying and PTSD symptoms among children and youth in primary and secondary schools?
b) Does the diagnosis of PTSD apply to the health consequences observed among individuals targeted by school bullying?
Method
In order to answer our research questions, we conducted a systematic review and meta-analysis, employing a meticulously designed review protocol registered in the Open Science Framework prior to commencing the review. First, a priori inclusion /exclusion criteria were determined as follows: Studies need to: a) be empirical original study with a quantitative design, b) focus on the association of bullying at school with the diagnosis of PTSD, and/or symptoms of PTSD, c) include validated questionnaires to assess posttraumatic stress, d) have a sample of students in primary or secondary education, e) report uncorrected bivariate correlations (or other statistical estimates that can be transformed to bivariate correlations) between school bullying and symptoms of PTSD, f) written in English, and g) published in a peer-reviewed journal. Hence, the studies were excluded based on: a) topic (i.e., a lack of a focus on the association of school bullying with symptoms pf PTSD and/or diagnosis of PTSD); b) target group (i.e., a different target group such as higher education students); c) outcome (i.e., non-validated measure of PTSD); d) study type (i.e., theoretical, and conceptual articles or other papers not reporting primary empirical quantitative research); e) language (i.e., not written in English), and f) insufficient information (i.e., information required to compute an effect size is either unavailable in the full-text or via direct requests from the corresponding author). Then a comprehensive literature search was carried out in seven databases: Academic Search Ultimate, ERIC, ISI Web of Science, Medline, ProQuest, PsycINFO, and SCOPUS. The identified studies were screened for their eligibility in a two-stage independent double screening process (i.e., screening on title and abstract and screening on full-text) using EPPI software. Detailed data were extracted for the eligible studies and authors who did not provide necessary information to calculate effect sizes and/or information on potential moderators were also contacted via email. Study quality was assessed using the AXIS tool (Downes, Brennan, Williams, & Dean, 2016).
Expected Outcomes
Through the comprehensive literature search, 2953 studies were identified and after the removal of duplicates, 906 studies were screened independently by two authors. After the two-stage abstract and full text screening, 38 studies were selected as eligible in line with the a priori defined inclusion criteria. Preliminary descriptive analyses showed that there were 15 studies conducted before 2015 (range 2000-2014), while there were 23 studies conducted in and after 2015 (range 2015-2023), showing an increase in the number of studies examining the association between school bullying and symptoms of PTSD. There is one study which also established the diagnosis of PTSD as a consequence of bullying. Studies were coming from more than 20 countries, and mainly from USA (7 studies), China (5 studies), South Africa (3 studies), and Italy (3 studies). Majority of the studies (35 studies) employed a cross-sectional design, while there were only three studies with longitudinal design. While six studies had samples of students in primary school, 25 had in secondary schools, and three had both in primary and secondary schools. Educational level was not reported in the four remaining studies. Currently, we are in the process of data synthesis using a correlated and hierarchical effect size model with robust variance estimation (Pustejovsky & Tipton, 2021) using the programs metafor (Viechtbauer, 2010) and clubSandwich (Pustejovsky, 2019) in R. The presentation will focus on our findings of overall effect sizes estimated separately for each symptom of PTSD and bullying as well as total PTSD symptom score and bullying. We will also present the moderator analyses. We anticipate that our results will contribute to the development of interventions against bullying and trauma-specific treatment procedures following instances of bullying. These insights can be utilized to mitigate the potential traumatic consequences of systematic and persistent harm caused by bullying.
References
APA. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Association. Arseneault, L. (2018). Annual research review: the persistent and pervasive impact of being bullied in childhood and adolescence: implications for policy and practice. Journal of Child Psychology and Psychiatry, 59(4), 405-421. https://doi.org/10.1111/jcpp.12841 Arseneault, L., Bowes, L., & Shakoor, S. (2010). Bullying victimization in youths and mental health problems: ‘Much ado about nothing’?. Psychological Medicine, 40(5), 717-729. doi:10.1017/S0033291709991383 Campbell, M., & Bauman, S. (2018). Cyberbullying: definition, consequences, prevalence. In M. A., Campbell, & S., Bauman (Eds.), Reducing Cyberbullying in Schools: International Evidence-based Best Practices (pp. 3-16). Elsevier. Hellström, L., Thornberg, R., & Espelage, D. L. (2021). Definitions of bullying. In P. K. Smith & J. O’Higgins Norman (Eds.), The Wiley-Blackwell Handbook of Bullying (Vol. 1, pp. 4-21). Wiley-Blackwell. Downes, M. J., Brennan, M. L., Williams, H. C., & Dean, R. S. (2016). Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open, 6(12). http://dx.doi.org/10.1136/bmjopen-2016-011458 Nielsen, M. B., Tangen, T., Idsoe, T., Matthiesen, S. B., & Magerøy, N. (2015). Post-traumatic stress disorder as a consequence of bullying at work and at school. A literature review and meta-analysis. Aggression and Violent Behavior, 21, 17-24. Pustejovsky, J. (2019). clubSandwich (0.3.3) [Computer software]. https://cran.r-project.org/package=clubSandwich Pustejovsky, J. E., & Tipton, E. (2021). Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models. Prevention Science. https://doi.org/10.1007/s11121-021-01246-3 Viechtbauer, W. (2010). Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36(1), Article 1. https://doi.org/10.18637/jss.v036.i03
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