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      Traditional bullying and cyberbullying in the digital age and its associated mental health problems in children and adolescents: a meta-analysis

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          Abstract

          Bullying is a risk factor for the physical and mental health of adolescents. The advent of new technologies has resulted in a brand-new type of bullying, cyberbullying (CB). The co-occurring effects of cyberbullying and traditional bullying(TB) forms of bullying on adolescent mental health are unclear. We performed a meta-analysis to explore the unique and combined effects of CB and TB on adverse psychological outcomes in victims by conducting a joint study of both types of bullying. By doing so, we provide the basis for a comprehensive community bullying prevention program. The database PubMed, PsyclNFO, and Web of Science were searched for studies from 2010 to 2021. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline was followed for data abstraction, and the NIH tool was used to evaluate study-level risk of bias. 42 studies with 266,888 participants were identified. Random-Effect models were used for our study. The moderator analysis was used to explore the moderator of prevalence. Studies with three groups of victims (TB only, CB only, and Both) and two groups of victims (TB and CB) were compared in subgroup analysis. The mean victimization rate was 24.32% (95% CI 20.32–28.83%) for TB and 11.10% (95% CI 9.12–13.44%) for CB. Roughly one-third of TB victims were also victimized by CB. Conversely, only about one-third of CB victims were free from TB. The estimated ORs for depression, suicidal ideation, suicide attempts, and self-harm in the three-group (TB only, CB only and Both) analysis were: depression [TB only: 3.33 (2.22–5.00); CB only: 3.38 (2.57–4.46); Both: 5.30 (2.43–11.56)]; suicidal ideations [TB only: 3.08 (2.12–4.46); CB only: 3.52 (2.38–5.20); Both: 6.64 (4.14–10.64)]; self-harm [TB only: 2.70 (1.86–3.91); CB only: 3.57 (3.20–3.98); Both: 5.57 (2.11–16.00)]; and suicide attempts: [TB only: 2.61 (1.50–4.55); CB only: 3.52 (2.50–4.98); Both: 7.82 (3.83–15.93)]. TB and CB victimization among youth are a matter of public health concern. Victimization appears to be a marker of greater psychopathological severity, particularly suicide-related issues.

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Bias in meta-analysis detected by a simple, graphical test

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              Quantifying heterogeneity in a meta-analysis.

              The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                European Child & Adolescent Psychiatry
                Eur Child Adolesc Psychiatry
                Springer Science and Business Media LLC
                1018-8827
                1435-165X
                December 31 2022
                Article
                10.1007/s00787-022-02128-x
                5f9676c4-7626-48b5-a77e-2375e68f3194
                © 2022

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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