You may even see someone on screen that you, a modern woman in a multicultural world, can identify with! Which, of course, is the point. In short, the ad features Normal Women™ doing Normal Things™: the sort of people you see on the train on your way to work, the sort of people you are friends with– and the sort of people who shop at H&M. Table 2 Factors associated with bullying amongst the adolescent, per WHO region.Action shots of women’s wobbly bits wobblingĪ thin woman eating french fries without a side of guiltĪn ethnically ambiguous high-powered female business executive
The lowest prevalence was observed in Tajikistan for males (7♰%) and females (8♰%) (see Supplementary Table 3). The highest prevalence of bullying was observed in Samoa for both males (79♰%) and females (70♰%). A large variation in prevalence amongst both male and female adolescents was observed ( Fig. Nearly one-third (33♰%, 32♲–33♲%) of male adolescents were bullied at least once at school within the past 30 days prior to the survey, whereas the prevalence was lower (28♲% 28♰–29♰%) amongst female adolescents. According to the country income classification, pooled prevalence amongst the adolescents was lower in HICs (20♰%, 19♰–20♴%) and the highest in the upper-middle-income group of LMICs (40♴%, 40♰–41♱% Fig. The country-specific prevalence ranged from 7♰% in Tajikistan to 75♰% in Samoa (Supplementary Table 3). The highest pooled prevalence was observed in the Eastern Mediterranean Region (45♱%, 44♳–46♰%) and the lowest was in the European region with 8♴% (8♰–9♰% Fig. The pooled prevalence of bullying victimisation was 30♵% (95% CI: 30♲–31♰%) in LMICs-HICs. Variations in errors due to complex sample design were controlled in all the analysis by using “svy” command in STATA (version 14). We then fitted a two multiple regression models i) one by including all the population level variables (Model 2) and ii) another by including population as well as country level variables (GDP and government expenditure on education) in Model 3 to explore independent factors associated with bullying victimisation. First we conducted a simple logistic regression analysis (Model 1) by only adjusting for survey year in order to select variables which had a bivariate association with bullying. These included survey year, age, gender, socioeconomic status, peer and parental support, GDP per capita and expenditure on education. We considered a set of independent variables in the regression model. In binary logistic regression models, we considered bulling victimization (a binary variable coded as 0 if not victimized and 1 if victimized) as a dependant variable. We conducted binary logistic regression analysis to examine the factors associated with bullying victimisation. Bivariate analysis was performed to calculate the prevalence of bullying victimization over background characteristics at the global and regional level.
Weighted prevalence estimates were calculated for individual countries to allow cross-country comparisons, and by gender across countries to understand gender disparities within countries. We used sample weights to calculate weighted prevalence or mean estimates (with corresponding 95% confidence intervals, CIs). This included using strata and primary sampling units at the country level. The data were weighted to allow the samples to be nationally representative.
The second study used the Global School-based Student Health Survey (GSHS) focusing on South East Asia, and provided an overview of the prevalence of bullying victimisation experiences amongst the adolescents in South East Asia. We identified only three publications, the first study conducted in 2001/2 used the Health Behaviour in School-aged Children survey (HBSC) and the Global School-based Students Health Survey (GSHS) data from 66 countries. The literature search was conducted up to June 25, 2019. The key words used in the search (“bullying” OR “bullying victimisation”) and (“adolescents” OR “child*” OR “teenager” OR “youth”) and (“developing country” OR “low socioeconomic status” OR “low income country” OR “middle income country” OR “low- and middle-income country” OR “ high income country” OR “developed country” OR “high socioeconomic status” OR “low and middle income to high income countries” OR “LMIC HICs” OR “LMICs”). We systematically searched PubMed, EMBASE, PsycNIFO with a combination of MeSH heading terms and keywords. The Lancet Regional Health – Western Pacific.The Lancet Regional Health – Southeast Asia.The Lancet Gastroenterology & Hepatology.
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