Assessment of School Anti-Bullying Interventions

Abstract

IMPORTANCEBullying is a prevalent and modifiable risk factor for mental health disorders. Although previous studies have supported the effectiveness of anti-bullying programs; their population impact and the association of specific moderators with outcomes are still unclear.OBJECTIVE To assess the effectiveness of school anti-bullying interventions, their population impact, and the association between moderator variables and outcomes.DATA SOURCES A search of Ovid MEDLINE, ERIC, and PsycInfo databases was conducted using 3 sets of search terms to identify randomized clinical trials (RCTs) assessing anti-bullying interventions published from database inception through February 2020. A manual search of reference lists of articles included in previous systematic reviews and meta-analyses was also performed. STUDY SELECTIONThe initial literature search yielded 34 798 studies. Included in the study were articles that (1) assessed bullying at school; (2) assessed the effectiveness of an anti-bullying program; (3) had an RCT design; (4) reported results; and (5) were published in English. Of 16 707 studies identified, 371 met the criteria for review of full-text articles; 77 RCTs were identified that reported data allowing calculation of effect sizes (ESs). Of these, 69 independent trials were included in the final meta-analysis database.DATA EXTRACTION AND SYNTHESIS Random-effects and meta-regression models were used to derive Cohen d values with pooled 95% CIs as estimates of ES and to test associations between moderator variables and ES estimates. Population impact number (PIN), defined as the number of children in the total population for whom 1 event may be prevented by an intervention, was used as an estimate of the population impact of universal interventions targeting all students, regardless of individual risk. MAIN OUTCOMES AND MEASURESThe main outcomes are the effectiveness (measured by ES) and the population impact (measured by the PIN) of anti-bullying interventions on the following 8 variable categories: overall bullying, bullying perpetration, bullying exposure, cyberbullying, attitudes that discourage bullying, attitudes that encourage bullying, mental health problems (eg, anxiety and depression), and school climate as well as the assessment of potential assocations between trial or intervention characteristics and outcomes. RESULTSThis study included 77 samples from 69 RCTs (111 659 participants [56 511 in the intervention group and 55 148 in the control group]). The weighted mean (range) age of participants in the intervention group was 11.1 (4-17) years and 10.8 (4-17) years in the control group. The weighted mean (range) proportion of female participants in the intervention group was 49.9% (0%-100%) and 50.5% (0%-100%) in the control group. Anti-bullying interventions were efficacious in reducing bullying (ES, −0.150; 95% CI, −0.191 to −0.109) and improving mental health problems (ES, −0.205; 95% CI, −0.277 to −0.133) at study end point, with PINs for universal interventions that target the total stud...

Design and Analysis of a Smart Sensor-Based Early Warning Intervention Network for School Sports Bullying among Left-Behind Children

“…The result is converted into the probability of showing bullying emotion and nonbullying emotion in speech samples, and the conversion process increases the computation of the algorithm and reduces the recognition efficiency of the classification algorithm [27]. Therefore, in this paper, we design a speech emotion 2-classification neural network model based on the speech emotion 6-classification neural network model, and the output of this model represents the probability of showing bullying emotion and nonbullying emotion in speech samples without the conversion process of recognition results.…”

Pelajaran Yang Didapat Dari Olweus Bullying Prevention Program dan KiVa: Review Naratif

“…The best way to conduct an evaluation is to use an RCT, in which schools and classes are randomly selected as the treated group (intervention) and the untreated group (control) (P. K. Smith, 2016). RCT is the most powerful research method for determining the causes and effects of interventions (Bhide et al, 2018;Deaton & Cartwright, 2018;Fraguas et al, 2021;Kaul et al, 2021), so it can clearly explain the differences that emerge in the two groups (Smith, 2016). However, RCT requires schools or classes to follow all the procedures designed for the program.…”

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“…A web-enabled, school-based, preventive intervention targeting bullying and promoting respect for diversity will be associated with a reduction in bullying prevalence and improved mental health and quality of life in children and adolescents receiving the intervention relative to the control group. Based on evidence from previous studies ( 19 ), we hypothesize that these effects will be sustained over follow-up.…”

“…For instance, bullying victims show increased risk for anxiety, depressive, and psychotic disorders, poorer physical health, and suicidality both in the short-and longterm, along with poorer educational and vocational outcomes in adulthood (10)(11)(12)(13)(14)(15). Several school-based interventions have shown effectiveness in reducing bullying rates by about 20% (16)(17)(18)(19). Although individual effect sizes are in the small to moderate range, considering the global prevalence of bullying, the population impact number of these interventions seems compelling (19).…”

“…Several school-based interventions have shown effectiveness in reducing bullying rates by about 20% (16)(17)(18)(19). Although individual effect sizes are in the small to moderate range, considering the global prevalence of bullying, the population impact number of these interventions seems compelling (19).…”