This article is a correction to:
A systematic review and meta-analysis of interventions to decrease cyberbullying perpetration and victimization: An in-depth analysis within the Asia Pacific region
Ida Khairina Kamaruddin
Aini Marina Ma'rof
Ahmad Iqmer Nashriq Mohd Nazan
Habibah Ab Jalil- 1Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Malaysia
- 2Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia
by Kamaruddin, I. K., Ma'rof, A. M., Mohd Nazan, A. I. N., and Ab Jalil, H. (2023). Front. Psychiatry 14:1014258. doi: 10.3389/fpsyt.2023.1014258
In the published article, there was an error in the Data availability statement. The error in the data availability statement in the original article is that PROSPERO determined that this review does not meet the eligibility criteria for inclusion in their database which is limited to systematic reviews with at least one direct health outcome. The correct Data availability statement appears below.
Data availability statement
The data utilized in this review are derived from previously published studies and are available upon request from the corresponding author.
In the published article, some errors were made where the percentage of teens accessing online content from a mobile device was inaccurately quoted, and overlapping texts with other articles were erroneously included. We would like to rectify these errors by providing the following correction:
A correction has been made to 1. Introduction, the first paragraph. This sentence previously stated:
“Before the pandemic caused by COVID-19, survey research indicated that 73% of teens aged 13–17 had smartphones and 91% reported accessing online content from a mobile device (1). Given the access to information and communication technology, it is not surprising that in the same survey, four out of five teens reported using the Internet “almost constantly” or “several times a day.” Throughout the pandemic, and once the pandemic subsides, youth and teens will continue to use technology regularly for school, extracurricular activities, and to engage with friends (2). One of the unfortunate consequences of the pervasive and prolonged use of technology is the cyberbullying phenomenon. Cyberbullying perpetration is the act of inflicting or receiving negative, damaging, or abusive language or harassment through information and communications technology (3). Over the past decade, prevalence rates for cyberbullying involvement among youth between the ages of 10 and 17 years (as a victim, bully, or bully-victim) have been reported to be between 14 and 21% (4–6). Meta-analytic findings revealed that approximately 15% of US students reported being victims or perpetrators of cyberbullying in the past 30 days (7). Prevalence rates vary widely in other countries, from a low 5.0% in Australia to a high 23.8% in Canada (8). A recent small-scale survey further suggests cyberbullying perpetration and victimization may have increased following the pandemic, perhaps due to students' increased technology use (9).”
The corrected sentence appears below:
“Surveys have revealed that 93% of teenagers owned smartphones and mobile devices were used by more than 90% of them to access online information before the COVID-19 outbreak (1). Technology will remain an essential part of their lives throughout the pandemic as well as after it (2). This heavy reliance on technology has resulted in an increase in cyberbullying. Cyberbullying perpetration is the act of sending, posting, or sharing negative, harmful, false, or mean content about someone else through various forms of digital technology (3). Cyberbullying has affected ~14–21% of youths over the past decade (either as a victim, a bully, or a bully-victim) (4–6). According to research conducted in the US, ~15% of students have experienced or perpetrated cyberbullying in the past 30 days (7). In other countries varying prevalence rates were reported, such as Australia at 5.0% and Canada at 23.8% (8). It appears that cyberbullying increased during the epidemic, perhaps due to students' intensive use of technology (9).”
In the published article, some errors were made where the overlapping texts with other articles were erroneously included. We would like to rectify these errors by providing the following correction:
A correction has been made to 1. Introduction, “1.2. Objectives,” the first and second paragraphs. This sub-section was previously stated:
“This study aimed to conduct a comprehensive systematic review and meta-analysis of studies that measured the impacts of school violence, bullying, and targeted cyberbullying prevention programming on cyberbullying perpetration and victimization outcomes. Researchers have increased the implementation of interventions to target cyberbullying, and the results have been varied. We believe, despite several extensive systematic reviews and meta-analyses (for example, (2, 34, 35), that resynthesizing the various primary research findings is paramount in providing an appropriate, specific, and concrete response to cyber violence in policy and practice, particularly in the Asia Pacific region. For this project, we built upon credible previous meta-analytic work to conduct an updated systematic review and meta-analysis using comprehensive literature searches, thorough coding practices, and state-of-the-art meta-analysis techniques.
To address the research gap within the Asia-Pacific region on online user rights and protection concerning cyberbullying victims and perpetrators, this study aimed to provide further valuable empirical evidence by extending the work of the most recent large-scale systematic review and meta-analysis study on interventions to decrease cyberbullying perpetration and victimization (2) by expanding the age-range beyond school-aged settings.”
The corrected sentence appears below:
“This study seeks to comprehensively analyze studies examining interventions' effects on cyberbullying perpetration and victimization outcomes. Despite a number of extensive systematic reviews and meta-analyses [e.g., (2, 34, 35)], we consider it is vital to resynthesize the various primary research findings to provide a concrete and appropriate response to cyber violence in policy and practice, particularly in Asia Pacific region. To address the research gap within the Asia-Pacific region on online user rights and protection concerning cyberbullying victims and perpetrators, this study aimed to provide further valuable empirical evidence by extending the work of the most recent large-scale systematic review and meta-analysis study on interventions to decrease cyberbullying perpetration and victimization (2, 36) by expanding the age-range beyond school-aged settings.”
In the published article, we made an error regarding overlapping texts with other articles. The error we made was to directly reproduce the methods section from Polanin et al. (2021) without proper attribution under the wrongful assumption that replicating methods in scientific studies based on existing research is acceptable.
A correction has been made to the entire section 2. Methods, “2.1. Data Collection.” This sub-section previously stated:
“Intervention Studies: Eligible studies must have tested the effects of an intervention to decrease cyberbullying perpetration and victimization. Studies were not excluded based on the type of intervention tested; that is, a wide range of interventions and programs were included, which provides a robust database of studies. Studies on direct interventions were included in which study authors implemented cyberbullying intervention programs specifically intended to reduce cyberbullying perpetration and victimization. We also included interventions such as general violence prevention programs, physical aggression, and bullying prevention programs, and school climate models.
Comparison Group: The study must have included an eligible comparison group to be included in the review. Several eligible comparison groups may have been used, such as those that received no intervention, treatment as usual, or minimal or proven-to-be ineffective treatment. For the comparison group to be eligible, the study must have demonstrated that the minimal treatment was ineffective.
Research Design: We included studies that randomly assigned participants to a condition (randomized controlled trials) and studies that non-randomly assigned participants (quasi-experimental designs). In light of the number of studies that assign classrooms and schools to conditions, we did not exclude any studies based on the level of assignment. Hence, we included studies that may have randomly or non-randomly assigned classrooms, schools, or school districts to conditions.
Primary Outcome Measures: If primary studies did not implement a direct cyberbullying intervention, they had to have measured a cyberbullying perpetration or victimization outcome variable to be included in the review. If the authors implemented a general violence or bullying prevention program but did not include a cyberbullying measure, we did not immediately exclude it. This procedure and the reasoning behind it have been explained by Polanin et al. (37). They found that excluding the identified studies would change some substantive conclusions in their meta-analysis. Another rationale behind this is that recent meta-analytic research indicated that traditional in-person bullying perpetration and victimization and cyberbullying perpetration and victimization are correlated (26).
Timeframe: We expected that the vast majority of studies would have been published on or after 2003 because that was the earliest date for consistent mentioning of the terms electronic bullying, computer bullying, and cyberbullying in the literature. To ensure we synthesized all studies, we included any studies published on or after 1995.
Publication Status: We included all types of study reports, published or unpublished, to ensure that every available study report would be included in the review and decreased the well-known upward bias of studies published in peer-reviewed journals (38). We comprehensively searched for and attempted to locate all unpublished datasets that included cyberbullying perpetration and victimization measures.
Language and Country of Origin: Studies must have been published in English or Bahasa Melayu, which represented the native languages of our team members. We did not exclude studies based on country of origin (i.e., where a study's sample originated).”
The corrected sentence appears below:
“Intervention Studies. Studies on interventions designed to reduce cyberbullying perpetration and victimization were included in this review, regardless of the type of intervention. This gave us a wide range of studies to draw from, including those focusing on direct interventions, as well as those exploring broader violence prevention initiatives and anti-bullying programs.
Comparison Group. For the study to be considered for the review, it was required to contain a comparison group that met specific eligibility criteria. The comparison group could have been composed of individuals who did not receive any form of intervention, those who underwent treatment as per usual practice, or those who received a treatment that was either minimal or demonstrated to be ineffective. The comparison group was necessary to provide a point of reference for evaluating the effectiveness of the intervention being studied. Without such a group, it would be difficult to draw any meaningful conclusions about the efficacy of the intervention in question.
Research Design. We included randomized controlled trials, quasi-experimental studies, and studies that may have assigned groups in a randomized or non-randomized way to conditions without any exclusion based on the level of assignment.
Primary Outcome Measures. This review included studies that assessed cyberbullying perpetration or victimization as the primary outcome measure and did not exclude those that utilized a broader program to prevent violence or bullying instead of a specific cyberbullying intervention. This procedure and the reasoning behind it have been explained by Polanin et al. (38). The exclusion of certain studies was found to alter significant conclusions in their meta-analysis. Another rationale for this is previous meta-analytic studies' finding that the perpetration and victimization of conventional bullying and cyberbullying are connected (26).
Time range. Although cyberbullying-related terms started appearing in literature around 2003, studies published since 1995 were also included to ensure comprehensive coverage of research.
Publication Status. To minimize publication bias, we searched for relevant information on cyberbullying, including published and unpublished research reports and available data sets (39) with cyberbullying perpetration and victimization measures.
Language. Publications must be in English or Bahasa Melayu, regardless of the country of origin, were included in our review.”
In the published article, we made an error regarding overlapping texts with other articles. The error we made was to directly reproduce the methods section from Polanin et al. (2021) without proper attribution under the wrongful assumption that replicating methods in scientific studies based on existing research is acceptable.
A correction has been made to Methods, “2.2. Literature Search and Screening.” This sub-section previously stated:
“We used several complementary approaches, including searches of the traditional and gray works of literature, forward and backward reference harvesting, and hand searching of targeted journals. First, we conducted an electronic bibliographic literature search to identify qualifying studies. We then searched the following online databases, which included both published and unpublished studies, using search terms tailored to each database available through our University's library services: Cambridge Journal Online, EBSCOHOST, ERIC, IEEE XPLORE, Oxford Journal Online, ProQuest Dissertations and Theses, PubMed (Medline), Science Direct, Scopus, and Springerlink.”
The corrected sentence appears below:
“We employed multiple methods to identify qualifying studies, including electronic bibliographic searches and forward and backward reference harvesting. Our search included published and unpublished works within the traditional and gray literature. We used tailored search terms for each database and the following online databases available through our University's library services: Cambridge Journal Online, EBSCOhost, ERIC, IEEE XPLORE, Oxford Journal Online, ProQuest Dissertations and Theses, PubMed (Medline), Science Direct, Scopus, and SpringerLink.”
In the published article, we made an error regarding overlapping texts with other articles. The error we made was to directly reproduce the methods section from Polanin et al. (2021) without proper attribution under the wrongful assumption that replicating methods in scientific studies based on existing research is acceptable.
A correction has been made to 2. Methods, “2.3. PRISMA Flowchart.” This sub-section previously stated:
“2.3.1. Abstract screening
We used an exhaustive methodology to screen the large number of studies identified in this round (detailed in 2). We developed an abstract screening guide (see Supplementary Data Sheet 2) and screened the abstracts (Supplementary Table 1) using the free Rayyan software (39), which provides open-source web-based abstract screening. All review team members screened abstracts.
2.3.2. Full-text retrieval
Team members located full-text PDFs for all abstracts that were screened during the first round of screening in preparation for a second round using a full-text screening tool.
2.3.3. Full-text screening
We organized the results from all phases of the project (i.e., search results, abstract screening, full-text screening, and data extraction), and team members entered full-text screening responses into an “eligibility screen.” we ensured the accuracy of the screening process as all the “keep” or “drop” results were validated by the leading research members (i.e., the principal investigator or the lead statistician). As with abstract screening, team members conducted in-house training led by the lead statistician, after which we conducted the pilot screening.
2.3.4. Data extraction
A codebook detailed all information extracted from each study, and we further developed the relational database in Excel. We extracted study-level information such as details on the sample demographics and how the individuals were placed in groups, characteristics of the intervention and comparison conditions (including who developed and implemented the intervention and information on implementation fidelity), construct-level information (such as how the predictor and outcome variables were measured), and the summary data that could be used to estimate effect sizes (such as semi-partial correlations and/or adjusted-odds ratios derived from a regression model). Coders extracted information about each study and entered it into Excel coding screens dedicated to that information (e.g., samples, conditions, constructs, and effect sizes).
The corrected sentence appears below:
“2.3.1. Abstract Screening
We employed a comprehensive approach to review the numerous studies located during this round of research (as outlined in 2 and 36). All members of the review team assessed the abstracts. We developed a screening guide for abstracts (see Supplementary material) and utilized the free Rayyan software (40) for web-based abstract screening.
2.3.2. Full-Article Retrieval
To prepare for the next screening stage, the team members retrieved the complete article PDFs of the previously screened titles and abstracts.
2.3.3. Full-Article Screening
The team carried out a thorough screening process for eligibility by entering responses into a designated tool, followed by review and validation by the principal investigator and lead statistician, and after a training session, a pilot screening was conducted.
2.3.4. Data Extraction
We created a codebook to document all data that was extracted from each study. The data comprised demographics of the sample, characteristics of the intervention and comparison conditions, and summary statistics useful for effect size estimation. An Excel-based relational database was designed to structure the information. To maintain accuracy and consistency, coders used dedicated coding screens in Excel for each category of extracted data.”
In the published article, we made an error regarding overlapping texts with other articles. The error we made was to directly reproduce the methods section from Polanin et al. (2021) without proper attribution under the wrongful assumption that replicating methods in scientific studies based on existing research is acceptable.
A correction has been made to Methods, “2.4. Data Analysis.” This sub-section previously stated:
“We conducted separate analyses for each outcome variable category: (1) cyberbullying perpetration and (2) cyberbullying victimization. We reported summary statistics for the included studies, e.g., publication status, program target, research design, and location. We also planned to perform a sub-analysis looking further into the potential differentiated effects of gender, randomized controlled trial versus non-randomized control trial designs, whether or not the studies were theory-based or non-theory-based, and geographical locations with a specific focus on Asia-Pacific regions, and studies that also covers the age-range beyond K-12.
2.4.1. Meta-analyses
First, we estimated separate meta-analytic models that predict the two primary outcome variable categories. We used a random-effects model with robust variance estimation (40), which weights each effect size by the inverse of its variance (41) to produce a weighted average of the effect sizes.
Next, we planned to conduct two confirmatory meta-regression analyses predicting each behavioral outcome variable (i.e., cyberbullying perpetration and cyberbullying victimization). The meta-regression could be conducted using the following predictor variables: (1) country of origin (i.e., Asia-Pacific versus non-Asia Pacific); (2) program target (i.e., specifically targeted cyberbullying versus did not specifically target cyberbullying); (3) timepoint of second measurement (i.e., posttest versus follow up); (4) effect size type (i.e., dichotomous versus continuous), (5) percentage of males, and (6) the percentage of ethnic minority participants. Sub-analyses would be conducted to evaluate the potential moderating effects of gender, comparing randomized controlled trial study designs versus nonrandomized control trial designs, whether or not the studies were theory-based or non-theory-based, taking geographical locations of subjects into consideration, and expanding the age range beyond school-aged settings.
2.4.2. Exploratory analysis
Finally, we analyzed the overall effect sizes for each of the named programs identified through our systematic review.”
The corrected sentence appears below:
“We conducted separate analyses for each outcome variable category: (1) cyberbullying perpetration and (2) cyberbullying victimization. The characteristics of the studies included in the analysis were documented, including the publication status, the target of the program, the type of research design, and geographical location. We also planned to perform a sub-analysis looking further into the potential differentiated effects of gender, randomized controlled trial vs. non-randomized control trial designs, whether or not the studies were theory-based or non-theory-based, and geographical locations with a specific focus on Asia-Pacific regions, and studies that also covers the age-range beyond K-12.
2.4.1. Meta-Analyses
We analyzed two primary outcome variables using meta-analytic models, with separate analyses using a random-effects model and robust variance estimation method (41). The random-effects model considered both within-study and between-study variations, making it suitable for studies with diverse populations and designs. The robust variance estimation method was used to estimate standard errors of effect size estimates and to adjust for potential biases due to small sample sizes or heterogeneity. Each effect size estimate was weighted by its inverse variance to calculate the average effect size (42). The model assigned greater importance to effect sizes with smaller variances, resulting in a more accurate estimation of the overall effect size.
Our original plan, which followed the preceding step, involved conducting two confirmatory meta-regression analyses to investigate the potential predictors contributing to cyberbullying perpetration and victimization. Our meta-regression approach would involve incorporating several pertinent variables. These variables would encompass the type of effect size, the objectives of the intervention, the native country of the study participants, the timing of the follow-up measurement, as well as the percentage of male participants and individuals from ethnic minority groups. Sub-analyses were also planned to examine potential moderating effects of gender, study design, theory-based or non-theory-based, geographical location, and age range. Sub-analyses would be conducted to evaluate the potential moderating effects of gender, comparing randomized controlled trial study designs vs. non-randomized control trial designs, whether or not the studies were theory-based or non-theory-based, taking geographical locations of subjects into consideration, and expanding the age range beyond school-aged settings.
2.4.2. Exploratory Analysis
Ultimately, we performed an exploratory analysis aimed at assessing the overall effect size of the specified interventions that were identified during our review process.”
In the published article, several errors were made regarding the PROSPERO registration. We have deleted the below sentences from the Abstract and Methods sections, respectively:
“Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42022313369.
The study was registered in the PROSPERO International Prospective Register of Systematic Reviews (registration number CRD42022313369), and the detailed prespecified protocol is available online.”
In the published article, there is an error in the reference list. The updated reference list incorporates a new reference entry identified as 36, which necessitates renumbering the preexisting reference labeled as 36 to be revised as 37. Consequently, all subsequent numerical references from 37 to 66 have been incremented by 1 in both the in-text citations and the reference list.
The authors apologize for the errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Lenhart A. Teens, Social Media & Technology Overview 2015. Washington DC: Pew Internet and American Life Project (2015).
2. Polanin JR, Espelage DL, Grotpeter JK, Ingram K, Michaelson L, Spinney E, et al. A systematic review and meta-analysis of interventions to decrease cyberbullying perpetration and victimization. Prev Sci. (2021) 23:1259. doi: 10.1007/s11121-021-01259-y
3. Pearce N, Cross D, Monks H, Waters S, Falconer S. Current evidence of best practice in whole-school bullying intervention and its potential to inform cyberbullying interventions. Austr J Guid Counsell. (2011) 21:1–21. doi: 10.1375/ajgc.21.1.1
4. Kowalski RM, Limber SP. Electronic bullying among middle school students. J Adolesc Health. (2007) 6:S22–30. doi: 10.1016/j.jadohealth.2007.08.017
5. Wang J, Iannotti RJ, Nansel TR. School bullying among adolescents in the united states: physical, verbal, relational, and cyber. J Adolesc Health. (2009) 45:368–75. doi: 10.1016/j.jadohealth.2009.03.021
6. Ybarra ML, Mitchell KJ. Online aggressor/targets, aggressors, and targets: a comparison of associated youth characteristics. J Child Psychol Psychiatry. (2004) 45:1308–16. doi: 10.1111/j.1469-7610.2004.00328.x
7. Modecki KL, Minchin J, Harbaugh AG, Guerra NG, Runions KC. Bullying prevalence across contexts: a meta-analysis measuring cyber and traditional bullying. J Adolesc Health. (2014) 55:602–11. doi: 10.1016/j.jadohealth.2014.06.007
8. Brochado S, Soares S, Fraga S. A scoping review on studies of cyberbullying prevalence among adolescents. Trauma Violence Abuse. (2016) 18:523–31. doi: 10.1177/1524838016641668
9. Jain O, Gupta M, Satam S, Panda S. Has the COVID-19 pandemic affected the susceptibility to cyberbullying in India? Comput Hum Behav Rep. (2020) 2:100029. doi: 10.1016/j.chbr.2020.100029
10. Menesini E, Nocentini A, Palladino BE. Empowering students against bullying and cyberbullying: evaluation of an Italian peer-led model. Int J Confl Violence. (2012) 6:313–20. doi: 10.4119/ijcv-2922
11. Fekkes M, Van de Sande MC, Gravesteijn JC, Pannebakker FD, Buijs GJ, Diekstra RF, et al. Effects of the Dutch Skills for Life program on the health behavior, bullying, and suicidal ideation of secondary school students. Health Educ. (2016) 116:2–15. doi: 10.1108/HE-05-2014-0068
13. UNICEF. UNICEF Poll: More than a Third of Young People in 30 Countries Report Being a Victim of Online Bullying. UNICEF (2019).
14. Zhu C, Huang S, Evans R, Zhang W. Cyberbullying among adolescents and children: a comprehensive review of the global situation, risk factors, and preventive measures. Front Public Health. (2021) 9:634909. doi: 10.3389/fpubh.2021.634909
15. Lin MT. Risk Factors Associated With Cyberbullying Victimization and Perpetration Among Taiwanese Children. (Dissertation on the Internet), The University of Texas School of Public Health, Dallas, TX, United States (2009). Available online at: https://digitalcommons.library.tmc.edu/dissertations/AAI10126751 (accessed October 10, 2022).
16. Beran T, Mishna F, McInroy LB, Shariff S. Children's experiences of cyberbullying: a Canadian National Study. Child Sch. (2015) 37:207–14. doi: 10.1093/cs/cdv024
17. Lee C, Shin N. Prevalence of cyberbullying and predictors of cyberbullying perpetration among Korean adolescents. Comput Human Behav. (2017) 68:352–8. doi: 10.1016/j.chb.2016.11.047
18. Marco JH, Tormo-Irun MP. Cyber victimization is associated with eating disorder psychopathology in adolescents. Front Psychol. (2018) 9:987. doi: 10.3389/fpsyg.2018.00987
19. Marret MJ, Choo WY. Factors associated with online victimisation among Malaysian adolescents who use social networking sites: a cross-sectional study. Br Med J Open. (2017) 7:e014959. doi: 10.1136/bmjopen-2016-014959
20. Olenik-Shemesh D, Heiman T. Cyberbullying victimization in adolescents as related to body esteem, social support, and social self-efficacy. J Genet Psychol. (2016) 178:28–43. doi: 10.1080/00221325.2016.1195331
21. Rao J, Wang H, Pang M, Yang J, Zhang J, Ye Y, et al. Cyberbullying perpetration and victimisation among junior and senior high school students in Guangzhou, China. Injury Prev. (2017) 25:13–9. doi: 10.1136/injuryprev-2016-042210
22. Mishna F, Cook C, Saini M, Wu M-J, MacFadden R. Interventions to prevent and reduce cyber abuse of youth: a systematic review. Res Soc Work Pract. (2010) 21:5–14. doi: 10.1177/1049731509351988
23. Gaffney H, Farrington DP, Espelage DL, Ttofi MM. Are cyberbullying intervention and prevention programs effective? A systematic and meta-analytical review. Aggress Violent Behav. (2019) 45:134–53. doi: 10.1016/j.avb.2018.07.002
24. Gardella JH, Fisher BW, Teurbe-Tolon AR. A systematic review and meta-analysis of cyber-victimization and educational outcomes for adolescents. Rev Educ Res. (2017) 87:283–308. doi: 10.3102/0034654316689136
25. Guo S. A meta-analysis of the predictors of cyberbullying perpetration and victimization. Psychol Sch. (2016) 53:432–53. doi: 10.1002/pits.21914
26. Marciano L, Schulz PJ, Camerini AL. Cyberbullying perpetration and victimization in youth: a meta-analysis of longitudinal studies. J Comput Mediat Commun. (2020) 25:163-81. doi: 10.1093/jcmc/zmz031
27. Zych I, Ortega-Ruiz R, Del Rey R. Systematic review of theoretical studies on bullying and cyberbullying: facts, knowledge, prevention, and intervention. Aggress Violent Behav. (2015) 23:1–21. doi: 10.1016/j.avb.2015.10.001
28. Park MA, Golden KJ, Vizcaino-Vickers S, Jidong D, Raj S. Sociocultural values, attitudes and risk factors associated with adolescent cyberbullying in East Asia: a systematic review. Cyberpsychology. (2021) 15:5. doi: 10.5817/CP2021-1-5
29. Smith PK, Kwak K, Toda Y. School Bullying in Different Cultures. Cambridge: Cambridge University Press (2016).
30. Barlett CP, Gentile DA, Anderson CA, Suzuki K, Sakamoto A, Yamaoka A, et al. Cross-cultural differences in cyberbullying behavior: a short-term longitudinal study. J Cross Cult Psychol. (2014) 45:300–13. doi: 10.1177/0022022113504622
31. Han Y, Donnelly HK, Ma J, Song J, Hong H. Neighborhood predictors of bullying perpetration and victimization trajectories among South Korean adolescents. J Community Psychol. (2019) 47:1714–32. doi: 10.1002/jcop.22226
32. Hu Q, Bernardo AB, Lam SW, Cheang PK. Individualism-collectivism orientations and coping styles of cyberbullying victims in Chinese culture. Curr Psychol. (2018) 37:65–72. doi: 10.1007/s12144-016-9490-7
33. Huang YY, Chou C. An analysis of multiple factors of cyberbullying among junior high school students in Taiwan. Comput Human Behav. (2010) 26:1581–90. doi: 10.1016/j.chb.2010.06.005
34. Gaffney H, Ttofi MM, Farrington DP. Evaluating the effectiveness of school-bullying prevention programs: an updated meta-analytical review. Aggress Violent Behav. (2019) 45:111–33. doi: 10.1016/j.avb.2018.07.001
35. Pyzalski J, Poleszak W. 3.4 Peer violence and cyberbullying prevention programmes. In:Porzak R, , editor, Prevention in School: Current Situation and Future Prospects for Prevention in Poland. Lublin (PL): You Have a Chance Foundation (2019). p. 186–90.
36. Polanin J, Espelage D, Grotpeter J. Systematic Review Meta-Analysis of Interventions to Decrease Cyberbullying Perpetration and Victimization. (2021). Available online at: https://www.ojp.gov/pdffiles1/nij/grants/256015.pdf (accessed March 16, 2023).
37. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br Med J. (2021) 372:n71. doi: 10.1136/bmj.n71
38. Polanin JR, Espelage DL, Grotpeter JK, Valido A, Ingram KM, Torgal C, et al. Locating unregistered and unreported data for use in a social science systematic review and meta-analysis. Syst Rev. (2020) 9:1–9. doi: 10.1186/s13643-020-01376-9
39. Polanin JR, Tanner-Smith EE, Hennessy EA. Estimating the difference between published and unpublished effect sizes. Rev Educ Res. (2016) 86:207–36. doi: 10.3102/0034654315582067
40. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Systemat Rev. (2016) 5:4. doi: 10.1186/s13643-016-0384-4
41. Hedges LV, Tipton E, Johnson MC. Robust variance estimation in meta-regression with dependent effect size estimates. Res Synth Methods. (2010) (1):39–65. doi: 10.1002/jrsm.5
42. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. (2010) 1:97–111. doi: 10.1002/jrsm.12
43. McMaster Evidence Review & Synthesis Team. Effective Public Health Practice Project. (2010). Available online at: https://merst.healthsci.mcmaster.ca/ephpp/ (accessed August 31, 2022).
44. Ng JQ, Lee Z, Chee BCB. A Study of Brief Mindfulness Practice as an Intervention on the Relationship between Cyberbullying and Depressive Symptoms among Young Adults in Malaysia. (Malaysia): Universiti Tunku Abdul Rahman (2019).
45. Liau AK, Park Y, Gentile DA, Katna DP, Tan CHA, Khoo A, et al. Adventure: evaluating the effectiveness of a peer-mentoring and transmedia cyberwellness program for children. Psychol Popul Media Cult. (2017) 6:326–37. doi: 10.1037/ppm0000094
46. Wiretna CD, Saputra WNE, Muarifah A, Barida M. Effectiveness of solution-focused brief counseling to reduce online aggression of student. Univ J Educ Res. (2020) 8:1092–9. doi: 10.13189/ujer.2020.080344
47. Cross D, Shaw T, Hadwen K, Cardoso P, Slee P, Roberts C, et al. Longitudinal impact of the Cyber Friendly Schools program on adolescents' cyberbullying behavior. Aggress Behav. (2016) 42:166–80. doi: 10.1002/ab.21609
48. Tapingkae P, Panjaburee P, Hwang G-J, Srisawasdi N. Effects of a formative assessment-based contextual gaming approach on students' digital citizenship behaviours, learning motivations, and perceptions. Comput Educ. (2020) 159:103998. doi: 10.1016/j.compedu.2020.103998
49. Leung ANM, Wong N, Farver JM. Testing the effectiveness of an e-course to combat cyberbullying. Cyberpsychol Behav Soc Network. (2019) 22:569–77. doi: 10.1089/cyber.2018.0609
50. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. (2002) 21:1539–58. doi: 10.1002/sim.1186
51. Ryan W, Smith JD. Anti-bullying programs in schools: how effective are evaluation practices? Prev Sci. (2009) 10:248–59. doi: 10.1007/s11121-009-0128-y
52. Morgan PL, Farkas G, Hillemeier MM, Maczuga S. Are minority children disproportionately represented in early intervention and early childhood special education? Educ Researchr. (2012) 41:339–51. doi: 10.3102/0013189X12459678
53. Dwivedi YK, Hughes L, Baabdullah AM, Ribeiro-Navarrete S, Giannakis M, Al-Debei MM, et al. Metaverse beyond the hype: multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inform Manag. (2022) 66:102542. doi: 10.1016/j.ijinfomgt.2022.102542
54. Toombs E, Mushquash CJ, Mah L, Short K, Young NL, Cheng C, et al. Increased screen time for children and youth during the COVID-19 pandemic. Science briefs of the Ontario COVID-19. Sci Advisory Table. (2022) 3:1–19. doi: 10.47326/ocsat.2022.03.59.1.0
55. Hartshorne JK, Huang YT, Lucio Paredes PM, Oppenheimer K, Robbins PT, Velasco MD. Screen time as an index of family distress. Curr Res Behav Sci. (2021) 2:100023. doi: 10.1016/j.crbeha.2021.100023
56. Tanriverdi B, Apak O. Analysis of primary school curriculum of Turkey, Finland, and Ireland in terms of media literacy education. Educ Sci. (2010) 10:1187–213.
57. Arató N, Zsidó AN, Lénárd K, Lábadi B. Cybervictimization and cyberbullying: the role of socio-emotional skills. Front Psychiatry. (2020) 3:11. doi: 10.3389/fpsyt.2020.00248
58. den Hamer AH, Konijn EA. Can emotion regulation serve as a tool in combating cyberbullying? Pers Individ Dif. (2016) 102:1–6. doi: 10.1016/j.paid.2016.06.033
59. Del Rey R, Casas JA, Ortega R. Impact of the ConRed program on different cyberbulling roles. Aggress Behav. (2015) 42:123–35. doi: 10.1002/ab.21608
60. Brewer G, Kerslake J. Cyberbullying, self-esteem, empathy and loneliness. Comput Human Behav. (2015) 48:255–60. doi: 10.1016/j.chb.2015.01.073
61. Akhter S, Pauyo T, Khan M. What is the difference between a systematic review and a meta-analysis? In:Musahl V, Karlsson J, Hirschmann MT, Ayeni OR, Marx R, Koh JL, et al., , editors, What Is the Difference Between a Systematic Review and a Meta-analysis? Berlin: Springer (2019). p. 331–42. doi: 10.1007/978-3-662-58254-1_37
62. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Newark: John Wiley & Sons, Ltd (2019). doi: 10.1002/9781119536604
63. Mikolajewicz N, Komarova SV. Meta-analytic methodology for basic research: a practical guide. Front Physiol. (2019) 10:203. doi: 10.3389/fphys.2019.00203
65. Higgins S. Improving Learning: Meta-analysis of Intervention Research in Education. Cambridge: Cambridge University Press (2018). doi: 10.1017/9781139519618
66. Peng Z, Li L, Su X, Lu Y. A pilot intervention study on bullying prevention among junior high school students in Shantou, China. BMC Public Health. (2022) 22:12669. doi: 10.1186/s12889-022-12669-0
Keywords: cyberbullying perpetration, cyberbullying victimization, intervention, systematic review and meta-analysis, Asia-Pacific
Citation: Kamaruddin IK, Ma'rof AM, Mohd Nazan AIN and Ab Jalil H (2023) Corrigendum: A systematic review and meta-analysis of interventions to decrease cyberbullying perpetration and victimization: an in-depth analysis within the Asia Pacific region. Front. Psychiatry 14:1226698. doi: 10.3389/fpsyt.2023.1226698
Received: 22 May 2023; Accepted: 27 June 2023;
Published: 20 July 2023.
Edited and reviewed by: Wulf Rössler, Charité University Medicine Berlin, Germany
Copyright © 2023 Kamaruddin, Ma'rof, Mohd Nazan and Ab Jalil. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Aini Marina Ma'rof, ainimarina@upm.edu.my