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The gender discrepancy in high-risk behaviour outcomes in adolescents who have experienced cyberbullying in Indonesia
Accepted Manuscript Title: The gender discrepancy in high-risk behaviour outcomes in adolescents who have experienced cyberbullying in Indonesia Authors: Tjhin Wiguna, R. Irawati Ismail, Rini Sekartini, Noorhana Setyawati Winarsih Rahardjo, Fransiska Kaligis, Albert Limawan Prabowo, Rananda Hendarmo PII: DOI: Reference:
S1876-2018(18)30604-X https://doi.org/10.1016/j.ajp.2018.08.021 AJP 1526
To appear in: Received date: Revised date: Accepted date:
27-6-2018 26-7-2018 26-8-2018
Please cite this article as: Wiguna T, Irawati Ismail R, Sekartini R, Setyawati Winarsih Rahardjo N, Kaligis F, Prabowo AL, Hendarmo R, The gender discrepancy in high-risk behaviour outcomes in adolescents who have experienced cyberbullying in Indonesia, Asian Journal of Psychiatry (2018), https://doi.org/10.1016/j.ajp.2018.08.021 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The gender discrepancy in high-risk behaviour outcomes in adolescents who have experienced cyberbullying in Indonesia List of Authors Dr. dr. Tjhin Wiguna, SpKJ(K)1 Prof. Dr. dr. R. Irawati Ismail, SpKJ(K), MEpid1 Prof Dr. dr. Rini Sekartini, SpA(K)2 dr. Noorhana Setyawati Winarsih Rahardjo, SpKJ(K)1 dr. Fransiska Kaligis, SpKJ(K)1 Albert Limawan Prabowo3 Rananda Hendarmo3 Child and Adolescent Psychiatry Division. Department of Psychiatry, dr. Cipto Mangunkusumo General Hospital – Faculty of Medicine Universitas Indonesia. Jakarta – Indonesia 2. Department of Child Health. Department of Psychiatry, dr. Cipto Mangunkusumo General Hospital – Faculty of Medicine Universitas Indonesia. Jakarta – Indonesia 3. Faculty of Medicine Universitas Indonesia. Jakarta – Indonesia
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1. 2. 3. 4. 5. 6. 7. 1.
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Corresponding Dr.dr.Tjhin Wiguna, SpKJ(K) Child and Adolescent Division, Department of Psychiatry dr. Cipto Mangunkusumo General Hospital - Faculty of Medicine Universitas Indonesia, Jakarta - Indonesoa
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Highlights
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The study discuss: The high-risk behavior outcomes among adolescents with cyberbullying experienced. Discussion is done based on gender differences. Results of this study were very important especially for stakeholder and other professionals that work with adolescent, especially to plan mental health promotion and prevention programs that should consider the gender differences. For example: Male adolescents that being perpetrators had higher risk for tobacco smoking; meanwhile being solely as victim or victim/perpetrator, they were at risk for alcohol drinking. On the other hand, female adolescents that experienced cyberbullying victimization had higher risk for self-hurting behavior; but who acted as victim/perpetrator tended to show suicide attempt and thought.
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Abstract Objective: Adolescent cyberbullying and high-risk behaviour outcomes has been a major concern in the last few years. Gender discrepancy is an important component that predicts the outcomes. This study aimed to elaborate and identify the association between cyberbullying experience and high-risk behaviour outcomes based on gender differences among adolescents in Indonesia. Method: A cross sectional study that involved junior and senior high schools in Jakarta. There were 2917 adolescents who took part in this study. The cyberbullying questionnaire was used to identify the cyberbullying experienced. High-risk behaviour included in this study was tobacco smoking, alcohol consumption, and self-harm behaviour. Chi-Square test and odds ratio analysis were applied through SPSS for Mac. Results: The highest proportion in this study was composed of adolescents of both genders who acted as
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victim/perpetrator (52.25%). Adolescent males who indulged in cyberbullying showed an increased risk for cigarette smoking (OR=2.97); male adolescents who were victims and victim/perpetrator of cyberbullying had a higher risk to consume alcohol (OR=2.96 & OR=6.93). Meanwhile, the risk of self-hurting behaviour increased for both female and male with cyberbullying victimization (OR=3.68 & OR=2.97). Female adolescents who acted as victim/perpetrator had a higher risk of suicidal thoughts and attempting suicide (OR=1.90 & OR=2.11); and they were also at risk of consuming alcohol (OR=2.84). Conclusion: Cyberbullying returned negative impacts on both genders of adolescents. Boys showed a greater tendency to externalize while girls showed a greater tendency to internalize. Mental health promotion that is designed specifically for both genders might address the adolescents’ needs.
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Keywords: Adolescents, cyberbullying, high-risk behaviour outcomes, Indonesia
Introduction
Bullying is a major concern in societies all over the world, especially at school and other
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educational institutions. School bullying is described as hostile behaviour performed by students who possess and/or try to maintain a dominant position over others. In several
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community beliefs, school bullying is part of disciplinary training of students and is seen as a
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benign and “normal” part of the child’s and/or adolescent’s experience. Nevertheless, it is
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school violence that affects child and adolescent mental wellbeing (Kim et al., 2005). In addition, Chang et al. (2013) described school bullying as “an aggressive behavior among
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students that is characterized by three defining conditions: (a) negative or malicious behavior intended to harm or distress; (b) behavior repeated over a period; and (c) a relationship in which there is an imbalance in strength or power between parties involved.” Nowadays, the
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growth of electronic, internet-based communications in Indonesia has markedly changed social interactions among students at school. A study by Unicef-Indonesia (2014) showed
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that 80% of adolescents accessed the internet frequently for their school assignments, engaging with their friends online through social media platforms (70%), listening to music (65%), or watching videos online (39%). The study also revealed that 89% of children and
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adolescents communicated online with their friends, 56% with their family, 35% with their teacher, and 24% connected with people they did not know. Therefore, it is a common condition these days that many children and adolescents use mobile phones, smartphones, or laptops to connect with their surroundings, especially at school. Connections built in the digital world have similar social consequences as the physical world, such as abuse, violence, trafficking, and it also includes bullying in the digital world (cyberbullying). By using such technologies such as the ones mentioned above, the aggressors could annoy other students
without any face-to-face interaction but rather through interfaces such as personal computers mobile phones, and smartphones. Cyberbullying is a type of bullying performed through computers or cellular phones over the Internet and social media applications (Pachin and Hinduja, 2006). It can be experienced either directly or indirectly, depending on whether the post is private or public. Langos (2012) explained that sending private text messages from cellular phones, through social
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media applications such as Whatsapp, Path, and Line, or email, could be categorized as direct cyberbullying. Posting a negative content through public social media applications that humiliates others can be considered indirect cyberbullying (Snakenborg et al., 2011).
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Therefore, nowadays, adolescents at school are at a higher potential risk of experiencing cyberbullying. The prevalence of cyberbullying among adolescents at school ranged from 20–40% (Tokunaga, 2010). Studies aimed at secondary high-school students revealed that 5–
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25% of them disclosed that they have been victims or perpetrators of cyberbullying (Balding, 2005; Beran and Li, 2005; Smith et al., 2008). A study conducted in Taiwan (Chang et al.,
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2013) revealed that 18.4% of high-school students have been cyberbullied, 5.8% have bullied
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others, and 11.2% both bullied and were bullied. Beran and Li (2007) also indicated that
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students who were being cyberbullied also acted as cyberbullies towards other students while being bullied at school. Previous research has indicated that both victims and perpetrators of
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cyberbullying had increased risk of a variety of mental and somatic health problems (Sourander et al., 2010). Adolescents who experienced cyberbullying at school explicitly were at more risk of developing serious psychological distress compared to non-victims.
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Also, it is stated that adult support from schools might decrease the distress but could not inhibit the psychological consequences (Zhang et al., 2016). Other related studies revealed
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that children and adolescents who got bullied frequently sought more mental health services compared to others who had never been bullied (Evans-Lacko et al., 2017). Moreover, adolescence is a period of turmoil. The youths seek out their peers who help them
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define their identity and adapt while entering adulthood. Being an adolescent is complicated; they need to fit in their environment and define their role optimally as part of their identity development. Berg (2010) stated that during their development, adolescent males and females have different coping styles. For example, adolescent males exhibit a lower range of emotional relationships, tend to be more self-assured, and are willing to explore their surrounding more deeply and confidently; on the other hand, adolescent females show more neuroticism, more friendliness, and try to be more pleasurable. On the contrary, both genders
show concern about their self-image, particularly when it comes to the issue of peer acceptance and relationships, but girls are more concerned about being lean/tender while boys are concerned about being muscular (Pauletti and Perry, 2011). A sense of belongingness is also critical during this early adolescence period; therefore, it is often detected that teenagers, both male and female, take to and indulge themselves in high-risk behaviours, especially when their peers encourage and support these actions (Guerra et al., 2012). Therefore, performing a risky behaviour is more common among adolescents,
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especially due to either lack of rational decision-making, the feeling of inferiority towards others, increasing self-conscious, the complicated needs of peer acceptance and low
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understanding of one’s environment (Steinberg et al., 2007; Nixon, 2014; Lakon et al., 2015). As a result, it makes it easier for them to join in delinquency and risk-taking acts that are potentially harmful, such as self-hurting, suicidal thoughts and attempt, alcohol consumption,
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and cigarette smoking.
There are several discrepancies regarding gender differences in cyberbullying studies and its
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psychosocial relatedness problems; some results show that girls are more likely to be
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cyberbullied, while other studies show no gender differences in this regard (Sourender, et al.,
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2010; Navarro et al., 2011; Navarro and Jaskinki, 2014). A study in Malawi in Southern Africa showed that male adolescents who smoked cigarettes were three times more likely to
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report being bullied compared to female smokers and non-smokers (Kubwalo et al., 2013). Meanwhile, a study among college students found cyberbullying victims, especially females, had an increased risk of performing self-harm behaviour, while male bullies had an increased
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risk of alcohol consumption (Selkie et al., 2015). Another study explained that cyberbullying was also linked to suicidal ideation and attempted suicide; the study found that being
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cyberbullied or being a cyberbully elevated the risk of suicidal thoughts, suicide attempts, and completed suicide equally for both female and male subjects (Hinduja and Patchin, 2010; Navarro et al., 2011; Navarro and Jaskinki, 2014). Therefore, this study aimed to identify the high-risk behaviour outcomes in adolescents with cyberbullying experienced at school in
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Jakarta, Indonesia, and to elaborate the results based on gender differences. The results of this study might be used to illustrate the current magnitude of adolescent mental health problems and to advocate people to take action in promoting adolescent mental wellbeing among urban populations. 1. Method
2.1 Study design This was a cross-sectional study and part of a more extensive study on ‘Adolescent Mental Health, Wellbeing and Bullying Behaviour’ in Indonesia during the period of 2016–2017. The study included 2917 junior-high and senior-high school students in five junior-high and four senior-high schools in Jakarta, which comprised four government schools, four private schools, and one religious-based school. Prior to the study, 15 junior- and senior- high
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schools were invited to participate but only nine schools consented; therefore, school selection was based on their willingness to be involved in this study. Research subjects gave
their written consent to participate in this study. The Health Research Ethic Committee –
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Faculty of Medicine, Universitas Indonesia, has approved the protocol of this study. 2.2 Instruments
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Cyberbullying experience was collected based on the questions from previous works of Patchin and Hinduja (2008) and Sourander et al. (2010). The first part of the questionnaire
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includes two general questions on cyberbullying: “During the past six months, how often
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have you been cyberbullied?” and “During the past six months, how often have you
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cyberbullied others?”. The second part consists of eight items that ask about the manner in which one has been cyberbullied (for example, ignored by others, disrespected by others, threatened, e-mail bombed, and so on). Responses to all the items are given on four-point
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Likert-scales, ranging from “Never” to “Almost daily.” The above questionnaire has been translated and validated into the Indonesian language during the study and has the
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Cronbach’s ∂ = 0.72.
High-risk behaviour that was considered in this study included cigarette smoking, alcohol
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consumption, and self-harm behaviour. Cigarette smoking and alcohol consumption experience was obtained by a self-rating questionnaire that included two questions: (1) How often do you smoke a cigarette/consume alcohol? (2) How many cigars do you smoke? Likert
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scale was employed to rate the three questions. Also, the self-harm behaviour questionnaire consisting of three items (self-hurting, seriously thinking of suicide, and attempted suicide, answered as ‘Yes’ or ‘No’) was used to identify the self-harm experienced among the adolescents. Additionally, the demographic characteristics were also brought together in the questionnaire. 2.3 Data analysis
All data were analysed using statistical SPSS for Mac version 21. The statistical significance is a set of 0.05 for all analysis. Chi-Square test and the odds ratio were also counted respectively to find out the association of those factors. 2. Results The adolescents’ age ranged between 11–18. This study obtained that 5.14% of adolescents disclosed that they were victims of cyberbullying, 2.43% revealed that they were the
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perpetrators. Meanwhile, 52.25% acted as victims/perpetrators respectively and most of them
were in the late adolescence period (Table 1). The male– female ratio was quite equal (Table
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1). Male and female adolescents aged 12–14 had a higher risk of being victims or being cyberbullies; meanwhile, older males and females (15- to 17-year-olds) were more likely to act as victim/perpetrator (Table 1). Both male and female victimization revealed that the most frequent form of cyberbullying experienced was ‘became afraid due to safety threat’ (34% vs.
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63.33%), followed by ‘threatened by others (33.3% vs. 63.33%) and ‘spammed by email by
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others’ (32% vs. 65.33%) (Table 2).
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Table 1
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Table 2
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The total number of adolescents who smoked cigarettes in this study was found to be 254 (8.7%). The highest proportion of smokers was found among adolescents above the age of 12 (94.6%). Male students were more likely to be smoking cigarettes. This study found that
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5.93% adolescents who had experienced cyberbullying either as victim, perpetrator, or victim/perpetrator smoked cigarettes (Table 3). The odds ratio of male cyberbullying
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perpetrators who did cigarette smoking was 2.97 (OR=2.97; 95% of Confidence Interval 1.49–5.99; p<0.05); and it also was higher compared to male adolescents that acted as victim/perpetrator (OR=1.54; 95% of Confidence Interval 1.12–2.13; p<0.05) (Table 4).
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This study revealed that 8.7% of total adolescents consumed alcohol; the higher proportion was found among male students. The higher proportion of alcohol drinking was obtained from adolescents who acted as victim/perpetrator (13.39%) (Table 3). Interestingly, male adolescents who were categorized as victim/perpetrator of cyberbullying showed an increased risk of consuming alcohol (OR=6.93, 95% of Confidence Interval 4.23–11.35, p<0.05) when compared to males who have never experienced any cyberbullying; in addition, this study showed that female adolescents who were victims/perpetrators of cyberbullying were also at
risk of alcohol consumption (OR=2.84, 95% of Confidence Interval 1.67–4.84, p<0.05) (Table 4). Table 3 Table 4 From this study, it was revealed that 6.38% of the total number of adolescents hurt
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themselves, 6.82% had suicidal ideation, and 2.47% attempted suicide. Meanwhile, this study also presented that a higher proportion of both male and female adolescents who were cyberbullying victims/perpetrators disclosed that they had ever hurt themselves, had suicide
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ideation, and attempted suicide (Table 3). The risk of self-hurting behaviour increased in both female and male cyberbullying victimization (OR=3.68, 95% Confidence Interval 1.81–7.46, p<0.05 and OR= 2.97, 95% Confidence Interval 1.15–7.68, p<0.05) (Table 4). In addition,
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the risk of suicide ideation increased among the female victim/perpetrator group (OR=1.90, 95% of Confidence Interval 1.24–2.89, p<0.05), while adolescent females who were
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3. Discussion
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of Confidence Interval 1.01–4.38, p<0.05).
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categorized as victim/perpetrator were at a higher risk of attempting suicide (OR=2.11, 95%
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This study found that the proportion of cyberbullying victimization, cyberbullies and highrisk behaviour among adolescents in both genders were quite similar compared to other worldwide studies cited above. However, the number of subjects who were cyberbullied and
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perpetrators of cyberbullying was quite high in both genders. It is said that being a victim of cyberbullying might intensify emotional volatility and have adverse outcomes among
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adolescents (Zhang et al., 2016). Geen (1998) explained that adolescents who are bullied possibly strike back against the aggressor and turn out to be the perpetrator by returning irritated statements (anger) and sending harassing messages to others. It is also reasonable to assume that children who annoy others using cyber media are targeted by other children,
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particularly to protect each other. Therefore, each incident might then increase further cyberbullying acts by students who are directly involved as well by their peers. When students feel upset for being wrongfully assaulted, they may respond aggressively. Further explanation comes from Beattie (2005), who mentioned that retaliation against bullying is so intense that the revenge may serve to protect the targeted student from feelings of
embarrassment, sadness, and powerlessness. In other words, adolescents who are being bullied may ‘bully back’ to counteract negative emotions after having been bullied. Cyberbullying and high-risk behaviour among adolescents is a major public health concern in most countries, including Indonesia. Several factors are said to be associated with it, such as the complex relationship between the period of teenage, developmental milestones, and the style of living. Adolescence is the period when one searches for self-identity. Therefore, the
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need for rational thinking is very important. During this critical period, adolescents are quickly involved in cyberbullying and other potentially harmful behaviour such as smoking,
drinking alcohol, and self-harm, mainly when their peer groups encourage and support these
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actions; it is a way to connect with, or fit in and gain acceptance among, peer groups. In
addition, the immaturity development of cognitive-control network might influence adolescents to engage in risk-taking behaviour, whether of their own will or through peer
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pressure. A constant drive of needing to fit in and specific peer influences can be seen in male adolescent behaviours, especially in delinquent and experimental high-risk acts. Two
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networks are involved in these processes: first, the socio-emotional network that is sensitive
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to emotional stimuli and reacts to the reward processing part of the brain, and second, the
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cognitive control network that mediates planning, rational thinking, and self-regulation. The socio-emotional network becomes very dominant among adolescents and lessens the
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cognitive control network due to the immaturity of the prefrontal cortex (Steinberg et al., 2007; Weiss et al., 2011). Therefore, adolescents are more easily involved in risk-taking,
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impulsive, and hostile behaviour (Wunderlich et al., 2001). This study showed that male adolescents that acted as cyberbullies or were victims/ perpetrators had a higher risk of externalizing behaviour through cigarette smoking, alcohol
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consumption, and self-harm. Male cyberbullies were students who showed a hostile behaviour towards certain students not on a traditional basis of bullying but through the specific digital media. The characteristics of hostile behaviours are pessimism, scepticism,
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and sarcasm. They also tend to be more selectively negative towards others. Male adolescents with hostile behaviours might be associated with tension and stress susceptibility, unsuccessful coping, and externalizing acts, and specifically, high-risk behaviour (Whalen et al., 2001; Hampson et al., 2007). The previous study found that male adolescents with hostile behaviours were independently related to unhealthy behaviours such as cigarette smoking, alcohol consumption, or even self-harm (Unger et al., 2004; Helstrom et al., 2004; Romer et al., 2007). Another related study assumed that smoking, alcohol consumption, or self-harm
among male cyberbullies was directed towards reducing tension and distress, and gaining acceptance among peers (Kovall et al., 2000). Therefore, cyberbullying itself might influence the odds of cigarette smoking, alcohol consumption, and self-harm among high school students as compared to traditional bullying that occurred at school only (Case et al., 2016). This study found that smoking was more prevalent among male adolescents over 12 years of age; this finding was similar to the study of Weiss et al. (2011) that reported that smoking prevalence was low among students under 12years of age compared to students in the middle
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stage of adolescence. Nicotine and alcohol are substance that can reduce anxiety and tension
because they activate several neurotransmitters, such as dopamine, serotonin, and beta-
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endorphin; therefore, it is assumed that early adolescence is a critical period that could trigger
anxiety, especially among males who have experienced cyberbullying. Thus, nicotine intake and alcohol consumption might repress the feeling of anxiety, hostile behaviour, and guilt induced by the cyberbullying – this is true for victim, perpetrator, and victim/perpetrator
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(Benowitz, 2010; Buckner and Vinci, 2013; Lakon et al., 2015).
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However, this study showed that female cyberbullying victimization had a higher risk to
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result in internalizing behaviours, such as suicide ideation or suicide attempt. The female
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gender-role usually demands more affective dependence on human relations, especially towards peer groups or partner relationships; therefore, females with cyberbullying
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victimization are assumed to be less affectionate by their peer groups and turn to be more internalized and depressed. A study that was done in South Korea showed that female students respond to school bullying with suicidal ideations that are more acute as compared to
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male students. On the other hand, when school bullying persisted for a period of time, suicidal thoughts became equal between males and females, thus making the differences
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based on gender vanish over time (Kim et al., 2005). Nevertheless, this study did not measure the onset of cyberbullying; the present results could not support the above explanation any further. Another study explained that females were more vulnerable to the negative mental health effects of cyberbullying, such as depression, self-harm and suicidal ideas. It was
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hypothesized that participants who had experienced cyberbullying in their later years had also experienced cyberbullying or other kinds of school bullying in their earlier years (Kraft and Wang, 2010). A longitudinal study has shown that involvement in bullying in childhood can contribute to and manifest as low self-esteem, aggressive behaviour towards one’s own self (self-harm), and alcohol consumption, especially in young female students in middle and high school(Sourander et al., 2007; Due et al., 2010; Selkie et al., 2015).
This was, to our understanding, the first study in Indonesia that included a high number of subjects at school and elaborated the high-risk behaviour outcomes in adolescents who have experienced cyberbullying. This study found a remarkable data based on the association between adolescents who experienced cyberbullying and high-risk behaviour outcomes that differed in terms of gender. Therefore, cyberbullying at school should be given more attention and given serious consideration, since gender is a significant implication and a determinant factor for high-risk behaviour outcomes. Several findings from the current
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studies revealed the urgency of teachers, counsellors, and mental health professionals to
address cyberbullying and its high-risk behaviour outcomes when assessing adolescents’
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health (specifically their mental health); further, it was suggested that the gender discrepancy be elaborated more deeply to provide a better understanding of their needs. The finding of this study make it quite clear that adolescents who had experiences relating to cyberbullying required mental health support and health support in general. However, evidence from several
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clinical studies showed majority of adolescents did not seek help when experiencing
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cyberbullying or performing high-risk behaviours due to the cyberbullying experienced.
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Therefore, mental health professionals from different professions should take proactive approaches to serve these young people. This can be done through routine screening resulting
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in early detection at school, and it can be applied by using simple questionnaires or direct observations of their emotional or behavioural appearance. Consequently, and especially to
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solve this problem comprehensively, relevant approaches that include gender-related issues should be redesigned, such as comprehensive school-based intervention and prevention
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programs that can be incorporated into school curricula and the community in general – for example, engaging adolescents at school in scholarly and community discussions related to empathy, social skills, legislation, and other gender specific issues. In addition, mental health
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promotion and prevention programs for children and adolescents at school, such as life-skill programs, could be actively achieved by both genders – it may include ways of handling peer pressure, strengthening the means of conflict resolution, dealing with emotion, overcoming
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stress, and enhancing self-esteem. There were several limitations to this study. The focus was exclusively on adolescents at school, and the results might be different among adolescents outside schools. The using of a self-rating questionnaire that might be perceived subjectively would consequently increase subjective bias; to minimize the bias, brief seminars were conducted for teachers and students, addressing bullying and protocol of the research prior to the studies, including each
definition. Therefore, teachers and students had a similar perception about the aims of the study. Another limitation was the small number of school that participated in this study – this might also affect the external validation. This is not to mention that there was no data that showed the duration of cyberbullying experienced; further, it did not include other risk factors such as mental illness (depression, anxiety disorder, psychotic disorder, bipolar disorder, and substance abuse), personality types (borderline personality disorder or severe introvert or extrovert traits), environmental factors (domestic violence, parental separation,
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and parental mental illness), and socio-economic factors (poverty) (Gould et al., 2003; Kim et
al., 2005). To conclude, these findings are not only very important but also provide insight
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into the adolescent mental health and wellbeing in these days, especially as it is related to
high-risk behaviour outcomes in adolescents with experience of cyberbullying through gender discrepancy.
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Acknowledge
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We would like to thank you all junior- and senior-high schools that took part in this study.
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International Collaboration Research Grant from Universitas Indonesia funded this study on
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2015. References
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Balding J., 2005. Young People in 2004: The health-related behavior questionnaire results for 40,430 young people between the ages of 10 and 15. Exeter, England: Schools Health Education Unit.
PT
Benowitz, N.L., 2010. Nicotine: Addiction. N. Engl. J. Med. 362, 2295 – 2303
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Beran T., Li Q., 2005. Cyber-harassment: A study of a new method for an old behavior. J of Educational Computing Research. 32, 265 – 77 Beran T., Li Q., 2007. The relationship between cyberbullying and school bullying. Journal of Student Wellbeing. 1, 15 – 33
A
Berk, L.E., (2010). Development through the Lifespan, Fifth Edition. Pearson Education, United States. ISBN 9870205687930 Buckner, J.D., Vinci, C., 2013. Smoking and social anxiety: The roles of gender and smoking motives. Addict. Behav. 38, 2388 – 2391 Case, K.R., Cooper, M., Creamer, M., Mantey, D., Kelder, S., 2016. Victims of bullying and tobacco use behavior in adolescents: Differences between bullied at school, electronically, or both. J. Sch. Health. 86, 832 – 840
Chang, F.C., Ching, M.L., Chung, H.C., Wen, Y.H., Tzu, F.H., Yun, C.P., 2013. Relationships among cyberbullying, school bullying, and mental health in Taiwanese adolescents. J. Sch. Health. 83, 454 – 462 Due P., Damsgaard M.T., Lund R., et al., (2009). Is bullying equally harmful for rich and poor children? A study of bullying and depression from age 15 to 27. European Journal of Public Health.19, 464 – 469
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Evans-Lacko, S., Takizawa, R,, Brimblecombe, N., King, D., Knapp, M., Maughan, B., et al., 2017. Childhood bullying victimization is associated with use of mental health services over five decades: a longitudinal nationally representative cohort study. Psychol. Med. 47, 127 – 135
SC R
Gould M.S., Greenberg T., Velting D.M., Shaffer D., 2003. Youth suicide risk and preventive interventions: a review of the past 10 years. J Am Acad Child Adolesc Psychiatry.42, 386 – 405
N
U
Guerra, N.G., Williamson, A.A., Lucas-Molina, B., 2012. Normal development: Infancy, childhood, and adolescence. In: Rey JM (ed), IACAPAP e-Textbook of Child and Adolescent Mental Health. Geneva: International Association for Child and Adolescent Psychiatry and Allied Professions.
M
A
Hampson, S.E., Andrews, J.A., Barckley, M., 2007. Predictors of the development of elementaryschool children's intention to smoke cigarettes: hostility, prototypes, and subjective norms. Nicotine Tob. Res. 9, 751–760 Helstrom, A.W., Bryan, A.D., Hutchison, K.E., et al., 2004. Tobacco and alcohol use as an explanation for the association between externalizing behavior and illicit drug use among delinquent adolescents. Prev. Sci. 5, 267–277
ED
Hinduja, S., Patchin, J.W., 2009. Bullying beyond the schoolyard: Preventing and responding to cyberbullying. Thousand Oaks, CA: Corwin Press.
PT
Hinduja, S., Patchin, J. W., 2010. Bullying, cyberbullying, and suicide. Arch. of Suicide Resch. 14, 206 – 221
CC E
Kim Y.S., Koh Y.J., Leventhal B., 2005. School bullying and suicidal risk in Korean middle school students. Pediatrics. 115, 357 – 363
A
Kovall, J.J., Pederson, L.L., Mills, C.A., et al., 2000. Models of the relationship of stress, depression, and other psychological factors to smoking behavior: A comparison of a cohort of students in grades 6 and 8. Prev. Med. 30, 463 – 477
Kraft E.M., Wang J., (2010). An exploratory study of the cyberbullying and cyberstalking experiences and factors related to victimization of students at a public liberal arts college. International Journal of Technoethics. 1, 74 – 91 Kubwalo, H.W., Muula, A.S., Siziya, S., Pasupulati, S., Rudatsikira, E., 2013. Prevalence and correlates of being bullied among in-school adolescents in Malawi: results from the 2009 Global School-Based Health Survey. Malawi Med. J. 25,12 – 14
Langos, C., 2012. Cyberbullying: The challenge to define. Cyber-psychology, Behavior, and Social Networking. 15, 285 – 289 Lakon, C.M., Hipp, J.R., Cheng, W., Butts, C.T., Jose, R., 2015. Simulating Dynamic Network Models and Adolescent Smoking: The Impact of Varying Peer Influence and Peer Selection. Am. J. Public Health. 105, 2438 – 2448 Navarro, R., Larrañaga, E., Yubero, S., 2011. Bullying-victimization problems and aggressive tendencies in Spanish secondary schools students: the role of gender stereotypical traits. Social Psychology of Education. 14, 457 – 473
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Navarro, J. N., Jasinski, J. L., 2013. Why girls? Using routine activities theory to predict cyberbullying experiences between girls and boys. Women and Criminal Justice. 23, 286 – 303.
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Nixon C.L., (2014). Current perpectives: The impact of cyberbullying on adolescent health. Adolesc Health Med Ther. 5, 143 – 158
Pauletti, R.E., Perry, D.G., (2011). Gender and Adolescent Development. Journal of Research on Adolescence. 21, 61 – 74
N
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Razak N., 2014. Study: Most children in Indonesia are online now, but many are not aware of potential risks. Unicef-Indonesia. (Accessed from Unicef.org, 7 April 2018)
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Romer, D.P.S., 2007. Associations between smoking and depression in adolescence: an integrative review. Taehan Kanho Hakhoe Chi. 37, 227–241
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Sansone, R.A., Sansone, L.A., 2008. Bully victims: Psychological and somatic aftermaths. Psychiatry (Edgmont). 5, 62 – 64
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Selkie E.M., Kota R., Chan Y.F., Moreno M., 2015. Cyberbullying, depression and problem alcohol use in female college students: A multisite study. Cyber-psychology, Behavior and Social Networking. 18, 79 – 86
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Smith P.K.,.Mahdavi J., Carvalho M., Fisher S., Russell S., Tippett N., 2008. Cyberbullying: Its nature and impact in secondary school pupils. J Child Psychol Psychiatry. 49, 376 – 385
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Snakenborg, J., Van Acker, R., & Gable, R. A., 2011. Cyberbullying: Prevention and intervention to protect our children and youth. Preventing School Failure: Alternative Education for Children and Youth. 55, 88 – 95
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Sourander, A., Sourander A., Jensen P., Ronning J.A., et al., (2007). What is the early adulthood outcome of boys who bully or are bullied in childhood? The Finnish ‘‘From a Boy to a Man’’ study. Pediatrics.120, 397 – 404
Sourander, A., Brunstein Klomek, A., Ikonen, M., Lindross, J., Luntamo, T., Koskelainen, M., et al., 2010. Psychosocial risk factors associated with cyberbullying among adolescents: a population-based study. Arch. Gen. Psychiatry. 67, 720 – 728 Steinberg, L., 2007. Risk Taking in adolescence; New perspective from brain and behavioral Science. Association for Psychological Science, Philadelphia.
Tokunaga, R.S., 2010. Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior. 26, 277 – 287 Unger, J.B., Chou, C.P., Palmer, P.H., et al., 2004. Project FLAVOR: One-year outcomes of a multicultural, school-based smoking prevention curriculum for adolescents. Am. J. Public Health. 94, 263 – 265.
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US Centre for Disease Control. Youth and tobacco use. Available at: http://www.cdc.gov/tobacco/data_statistics/fact_sheets/youth_data/tobacco_use/index.htm#estima tes. Accessed 7 November 2017
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Weiss, J.W., Cen, S., Mouttapa, M., Johnson, A.C., Unger, J., 2011. Longitudinal effects of hostility, depression, and bullying on adolescent smoking initiation. J. Adolesc. Health. 48, 591 – 96. Whalen, C., Whalen, C., Jamner, L., Henker, B., et al., 2001. Smoking and moods in adolescents with depressive and aggressive dispositions: Evidence from surveys and electronic diaries. Health Psychol. 20, 99 – 111
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Wolfer, R., Scheithauer, H., 2014. Social influence and bullying behavior: interventionbased network dynamics of the fair-player manual bullying prevention program. Aggress. Behav. 40, 309 – 19
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Wunderlich U., Bronisch T., Wittchen H.-U., Carter R., 2001. Gender differences in adolescents and young adults with suicidal behaviour. Acta Psychiatr Scand. 104, 332 – 339
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Zhang, X., Ra, C.K., Zhang, D., Zhang, Y., MacLeod, K.E., 2016. The impact of school social support and bullying victimization on psychological distress among California adolescents. Calif. J. Health Promot.14, 56 – 67
Table 1. Age characteristic of the research subjects Bullying experienced (n=2917; 100%)
30 (1.03)
15
0 (0.00) 5 (0.17)
16
1 (0.03) 0 (0.00)
17
0 (0.00) 0 (0.00)
18
0 (0.00) 0 (0.00)
Total
51 (1.75)
CC E A
0 (0.00)
4 (0.14) 127 14 (0.48) (4.35) 205 37 (1.27) (7.03) 154 86 (2.95) (5.28) 203 42 (1.44) (6.96) 255 5 (8.74) (0.17) 234 0 (8.02) (0.00) 0 4 (0.14) (0.00) 834 537 (28.59) (18.41) 1 (0.03)
Female (%)
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0 (0.00) 7 6 (0.21) (0.24) 39 12 (0.41) (1.34) 85 7 (0.24) (2.91) 160 2 (0.07) (5.48) 190 0 (0.00) (6.51) 197 0 (0.00) (6.75) 12 0 (0.00) (0.41) 27 690 (0.93) (23.65)
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99 (3.39)
1 (0.03) 5 (0.17) 16 (0.55) 13 (0.46) 9 (0.31) 0 (0.00) 0 (0.00) 0 (0.00) 44 (1.51)
Female (%)
Total (%)
11 (0.38) 19 (0.65) 345 (11.83) 602 239 (8.19) (20.64) 602 207 (7.10) (20.64) 450 29 (0.99) (15.43) 148 (5.07)
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14
38 (1.30)
Male (%)
Female (%)
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13
25 (0.86)
Male (%)
Male (%)
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12
1 (0.03)
Non-victim/ Non-perpetrator (n=1172; 40.18)
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1 (0.03) 13 (0.46) 16 (0.55) 20 (0.69)
11
Female (%)
Victim/ Perpetrator (n=1524; 52.25%)
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Male (%)
Perpetrator (n=71; 2.43%)
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Age (Years)
Victim (n=150; 5.14%)
1 (0.03) 452 (15.49) 0 (0.00) 431 (14.77) 0 (0.00) 16 (0.55) 635 (21.77)
2917 (100)
Table 2. The most frequent forms of cyberbullying among adolescents with cyberbullying experienced
Victim (n=150)
Forms of cyberbullying
Victim/Perpetrator (n=1523) Female
Ignored by others
47 (31.33%)
90 (60%)
599 (39.33%)
686 (45.04%)
Not appreciated by others
44 (29.33%)
86 (57.33%)
569 (37.36%)
671 (44.06%)
Called using names by others
43 (28.67%)
85 (56.67%)
Gossip spread by others
47 (31.33%)
84 (56%)
Threatened by others
50 (33.33%)
95 (63.33%)
Spammed by e-mail by others
48 (32%)
Offended by others
47 (31,33%)
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707 (46.42%)
581 (38.15%)
654 (42.94%)
658 (43.20%)
804 (52.79%)
83 (53.33%)
541 (35.52%)
589 (39.67%)
46 (30.67%)
86 (57.33%)
586 (38.48%)
734 (48.19%)
51 (34%)
95 (63.33%)
659 (43.27%)
800 (52.53%)
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98 (65.33%)
A
PT CC E A
556 (36.51%)
791 (51.94%)
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Became afraid due to safety threat
Female
627 (41.17%)
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Mocked
Male
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Male
Table 3. High-risk behaviour among adolescents with cyberbullying experienced based on the gender differences Adolescents with cyberbullying experienced Victim (n=150; 5.14%)
Perpetrator (n=71; 2.43%)
Victim/ Perpetrator (n=1524;52.25%)
Non-victim/ Non-perpetrator (n=1172;40.18%)
Male (%)
Male (%)
Male (%)
Female (%)
Male (%)
567 (19.44) 123 (4.22)
813 (27.87)
470 (16.11) 66 21 (0.72) (2.27)
618 (21.19)
550 (18.85) 140 (4.80)
770 (26.40)
517 (17.72)
615 (21.08)
Female (%)
Female (%)
Never Yes
43 (1.47)
94 (3.22)
31 (1.06) 13 8 (0.27) 5 (0.17) (0.45)
24 (0.82) 3 (0.10)
Alcohol drinking 95 (3.25)
5 (0.17) 4 (0.14)
41 (1.41)
26 (0.89)
3 (0.10) 1 (0.03)
86 (2.95)
No
50 (1.71) 1 (0.03)
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Yes
763 (26.16)
513 (17.59) 23 63 (2.16) (0.79)
608 (20.84)
593 (20.33) 51 (1.75)
748 (25.64)
516 (17.69) 20 78 (2.67) (0.69)
600 (20.57)
27 (0.93) 623 (21.36) 0 (0.00) 21 0 (0.00) (0.72)
799 (27.39)
623 (21.36)
ED 41 (1.41)
26 (0.89)
10 (3.43) 3 (0.10) 1 (0.03)
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Attempted suicide
89 (3.05)
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Yes
48 (1.65) 3 (0.10)
529 (18.13) 52 (1.78)
27 (0.93)
13 (0.46) 4 (0.14) 0 (0.00)
Suicide idea No
40 (1.37)
M
Yes
45 (1.54) 6 (0.20)
A
Self-hurting behavior: No
2660 (91.19) 254 64 (2.19) 19 (0.65) 18 (0.62) (8.70)
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Yes
46 (1.58)
2660 (91.19) 254 15 (0.51) (8.70)
N
Never
Female (%)
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Cigarette Smoking
Total (%)
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Charateristics
96 (3.29) 3 (0.10)
44 (1.51)
526 (18.03) 10 27 (0.93) (3.43)
2674 (91.67) 186 25 (0.86) (6.38)
2661 (91.22) 199 33 (1.13) (6.82)
2788 (95.58) 72 10 (3.43) (2.47)
Table 4. Association between cyberbullying experienced and high-risk behaviour outcomes based on the gender differences Male adolescent Odds Ratio 95% CI (OR) –
1.00 2.19
0.78 – 6.17
–
3.43
0.74 – 15.87
–
1.06
0.54 – 2.08
1.06 8.29 0.57 7.01 4.23 11.35
–
1.00 1.44 1.31
0.17 – 10.22
2.84*
1.67 – 4.84
–
1.00 3.68*
1.81 – 7.46
–
0.43
0.02 – 7.31
–
2.01*
1.25 – 3.23
0.46 5.62 0.54 6.62 0.46 5.62
–
1.00 2.04
0.97 – 4.23
–
0.70
0.09 – 5.31
–
1.90*
1.24 – 2.89
0.13 8.39 Perpetrator 0.56 0.03 9.77 Victim/Perpetrator 1.77 0.82 3.80 *p<0.05; odds ratio (OR) adjusted from socio-economic factor
–
1.00 1.95
0.53 – 7.20
–
1.08
0.06 – 18.90
–
2.11*
1.01 – 4.38
Perpetrator
2.97*
Victim/Perpetrator
1.54*
Alcohol drinking (n=2914) Non-victim/Non-perpetrator Victim
1.00 2.96*
Perpetrator
1.99
Victim/Perpetrator
6.93*
Victim/Perpetrator
2.19*
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Victim/Perpetrator
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Perpetrator
Attempted suicide (n=2860) Non-victim/Non-perpetrator Victim
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1.00 1.61 1.89 1.61
1.00 1.05
1.15 7.68 0.73 6.76 1.32 3.63
–
N
2.23
M
Perpetrator
Suicide idea (n=2860) Non-victim/Non-perpetrator Victim
A
1.00 2.97*
A
Self-hurting behavior (n=2860) Non-victim and Non-perpetrator Victim
–
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1.00 1.32
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0.59 2.94 1.49 5.99 1.12 2.13
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Cigarette smoking (n=2914) Non-victim/Non-perpetrator Victim
Female adolescent Odds Ratio 95% CI (OR)
0.48 – 4.34
S1876-2018(18)30604-X https://doi.org/10.1016/j.ajp.2018.08.021 AJP 1526
To appear in: Received date: Revised date: Accepted date:
27-6-2018 26-7-2018 26-8-2018
Please cite this article as: Wiguna T, Irawati Ismail R, Sekartini R, Setyawati Winarsih Rahardjo N, Kaligis F, Prabowo AL, Hendarmo R, The gender discrepancy in high-risk behaviour outcomes in adolescents who have experienced cyberbullying in Indonesia, Asian Journal of Psychiatry (2018), https://doi.org/10.1016/j.ajp.2018.08.021 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The gender discrepancy in high-risk behaviour outcomes in adolescents who have experienced cyberbullying in Indonesia List of Authors Dr. dr. Tjhin Wiguna, SpKJ(K)1 Prof. Dr. dr. R. Irawati Ismail, SpKJ(K), MEpid1 Prof Dr. dr. Rini Sekartini, SpA(K)2 dr. Noorhana Setyawati Winarsih Rahardjo, SpKJ(K)1 dr. Fransiska Kaligis, SpKJ(K)1 Albert Limawan Prabowo3 Rananda Hendarmo3 Child and Adolescent Psychiatry Division. Department of Psychiatry, dr. Cipto Mangunkusumo General Hospital – Faculty of Medicine Universitas Indonesia. Jakarta – Indonesia 2. Department of Child Health. Department of Psychiatry, dr. Cipto Mangunkusumo General Hospital – Faculty of Medicine Universitas Indonesia. Jakarta – Indonesia 3. Faculty of Medicine Universitas Indonesia. Jakarta – Indonesia
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1. 2. 3. 4. 5. 6. 7. 1.
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Corresponding Dr.dr.Tjhin Wiguna, SpKJ(K) Child and Adolescent Division, Department of Psychiatry dr. Cipto Mangunkusumo General Hospital - Faculty of Medicine Universitas Indonesia, Jakarta - Indonesoa
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Highlights
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The study discuss: The high-risk behavior outcomes among adolescents with cyberbullying experienced. Discussion is done based on gender differences. Results of this study were very important especially for stakeholder and other professionals that work with adolescent, especially to plan mental health promotion and prevention programs that should consider the gender differences. For example: Male adolescents that being perpetrators had higher risk for tobacco smoking; meanwhile being solely as victim or victim/perpetrator, they were at risk for alcohol drinking. On the other hand, female adolescents that experienced cyberbullying victimization had higher risk for self-hurting behavior; but who acted as victim/perpetrator tended to show suicide attempt and thought.
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Abstract Objective: Adolescent cyberbullying and high-risk behaviour outcomes has been a major concern in the last few years. Gender discrepancy is an important component that predicts the outcomes. This study aimed to elaborate and identify the association between cyberbullying experience and high-risk behaviour outcomes based on gender differences among adolescents in Indonesia. Method: A cross sectional study that involved junior and senior high schools in Jakarta. There were 2917 adolescents who took part in this study. The cyberbullying questionnaire was used to identify the cyberbullying experienced. High-risk behaviour included in this study was tobacco smoking, alcohol consumption, and self-harm behaviour. Chi-Square test and odds ratio analysis were applied through SPSS for Mac. Results: The highest proportion in this study was composed of adolescents of both genders who acted as
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victim/perpetrator (52.25%). Adolescent males who indulged in cyberbullying showed an increased risk for cigarette smoking (OR=2.97); male adolescents who were victims and victim/perpetrator of cyberbullying had a higher risk to consume alcohol (OR=2.96 & OR=6.93). Meanwhile, the risk of self-hurting behaviour increased for both female and male with cyberbullying victimization (OR=3.68 & OR=2.97). Female adolescents who acted as victim/perpetrator had a higher risk of suicidal thoughts and attempting suicide (OR=1.90 & OR=2.11); and they were also at risk of consuming alcohol (OR=2.84). Conclusion: Cyberbullying returned negative impacts on both genders of adolescents. Boys showed a greater tendency to externalize while girls showed a greater tendency to internalize. Mental health promotion that is designed specifically for both genders might address the adolescents’ needs.
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Keywords: Adolescents, cyberbullying, high-risk behaviour outcomes, Indonesia
Introduction
Bullying is a major concern in societies all over the world, especially at school and other
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educational institutions. School bullying is described as hostile behaviour performed by students who possess and/or try to maintain a dominant position over others. In several
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community beliefs, school bullying is part of disciplinary training of students and is seen as a
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benign and “normal” part of the child’s and/or adolescent’s experience. Nevertheless, it is
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school violence that affects child and adolescent mental wellbeing (Kim et al., 2005). In addition, Chang et al. (2013) described school bullying as “an aggressive behavior among
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students that is characterized by three defining conditions: (a) negative or malicious behavior intended to harm or distress; (b) behavior repeated over a period; and (c) a relationship in which there is an imbalance in strength or power between parties involved.” Nowadays, the
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growth of electronic, internet-based communications in Indonesia has markedly changed social interactions among students at school. A study by Unicef-Indonesia (2014) showed
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that 80% of adolescents accessed the internet frequently for their school assignments, engaging with their friends online through social media platforms (70%), listening to music (65%), or watching videos online (39%). The study also revealed that 89% of children and
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adolescents communicated online with their friends, 56% with their family, 35% with their teacher, and 24% connected with people they did not know. Therefore, it is a common condition these days that many children and adolescents use mobile phones, smartphones, or laptops to connect with their surroundings, especially at school. Connections built in the digital world have similar social consequences as the physical world, such as abuse, violence, trafficking, and it also includes bullying in the digital world (cyberbullying). By using such technologies such as the ones mentioned above, the aggressors could annoy other students
without any face-to-face interaction but rather through interfaces such as personal computers mobile phones, and smartphones. Cyberbullying is a type of bullying performed through computers or cellular phones over the Internet and social media applications (Pachin and Hinduja, 2006). It can be experienced either directly or indirectly, depending on whether the post is private or public. Langos (2012) explained that sending private text messages from cellular phones, through social
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media applications such as Whatsapp, Path, and Line, or email, could be categorized as direct cyberbullying. Posting a negative content through public social media applications that humiliates others can be considered indirect cyberbullying (Snakenborg et al., 2011).
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Therefore, nowadays, adolescents at school are at a higher potential risk of experiencing cyberbullying. The prevalence of cyberbullying among adolescents at school ranged from 20–40% (Tokunaga, 2010). Studies aimed at secondary high-school students revealed that 5–
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25% of them disclosed that they have been victims or perpetrators of cyberbullying (Balding, 2005; Beran and Li, 2005; Smith et al., 2008). A study conducted in Taiwan (Chang et al.,
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2013) revealed that 18.4% of high-school students have been cyberbullied, 5.8% have bullied
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others, and 11.2% both bullied and were bullied. Beran and Li (2007) also indicated that
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students who were being cyberbullied also acted as cyberbullies towards other students while being bullied at school. Previous research has indicated that both victims and perpetrators of
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cyberbullying had increased risk of a variety of mental and somatic health problems (Sourander et al., 2010). Adolescents who experienced cyberbullying at school explicitly were at more risk of developing serious psychological distress compared to non-victims.
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Also, it is stated that adult support from schools might decrease the distress but could not inhibit the psychological consequences (Zhang et al., 2016). Other related studies revealed
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that children and adolescents who got bullied frequently sought more mental health services compared to others who had never been bullied (Evans-Lacko et al., 2017). Moreover, adolescence is a period of turmoil. The youths seek out their peers who help them
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define their identity and adapt while entering adulthood. Being an adolescent is complicated; they need to fit in their environment and define their role optimally as part of their identity development. Berg (2010) stated that during their development, adolescent males and females have different coping styles. For example, adolescent males exhibit a lower range of emotional relationships, tend to be more self-assured, and are willing to explore their surrounding more deeply and confidently; on the other hand, adolescent females show more neuroticism, more friendliness, and try to be more pleasurable. On the contrary, both genders
show concern about their self-image, particularly when it comes to the issue of peer acceptance and relationships, but girls are more concerned about being lean/tender while boys are concerned about being muscular (Pauletti and Perry, 2011). A sense of belongingness is also critical during this early adolescence period; therefore, it is often detected that teenagers, both male and female, take to and indulge themselves in high-risk behaviours, especially when their peers encourage and support these actions (Guerra et al., 2012). Therefore, performing a risky behaviour is more common among adolescents,
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especially due to either lack of rational decision-making, the feeling of inferiority towards others, increasing self-conscious, the complicated needs of peer acceptance and low
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understanding of one’s environment (Steinberg et al., 2007; Nixon, 2014; Lakon et al., 2015). As a result, it makes it easier for them to join in delinquency and risk-taking acts that are potentially harmful, such as self-hurting, suicidal thoughts and attempt, alcohol consumption,
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and cigarette smoking.
There are several discrepancies regarding gender differences in cyberbullying studies and its
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psychosocial relatedness problems; some results show that girls are more likely to be
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cyberbullied, while other studies show no gender differences in this regard (Sourender, et al.,
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2010; Navarro et al., 2011; Navarro and Jaskinki, 2014). A study in Malawi in Southern Africa showed that male adolescents who smoked cigarettes were three times more likely to
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report being bullied compared to female smokers and non-smokers (Kubwalo et al., 2013). Meanwhile, a study among college students found cyberbullying victims, especially females, had an increased risk of performing self-harm behaviour, while male bullies had an increased
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risk of alcohol consumption (Selkie et al., 2015). Another study explained that cyberbullying was also linked to suicidal ideation and attempted suicide; the study found that being
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cyberbullied or being a cyberbully elevated the risk of suicidal thoughts, suicide attempts, and completed suicide equally for both female and male subjects (Hinduja and Patchin, 2010; Navarro et al., 2011; Navarro and Jaskinki, 2014). Therefore, this study aimed to identify the high-risk behaviour outcomes in adolescents with cyberbullying experienced at school in
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Jakarta, Indonesia, and to elaborate the results based on gender differences. The results of this study might be used to illustrate the current magnitude of adolescent mental health problems and to advocate people to take action in promoting adolescent mental wellbeing among urban populations. 1. Method
2.1 Study design This was a cross-sectional study and part of a more extensive study on ‘Adolescent Mental Health, Wellbeing and Bullying Behaviour’ in Indonesia during the period of 2016–2017. The study included 2917 junior-high and senior-high school students in five junior-high and four senior-high schools in Jakarta, which comprised four government schools, four private schools, and one religious-based school. Prior to the study, 15 junior- and senior- high
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schools were invited to participate but only nine schools consented; therefore, school selection was based on their willingness to be involved in this study. Research subjects gave
their written consent to participate in this study. The Health Research Ethic Committee –
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Faculty of Medicine, Universitas Indonesia, has approved the protocol of this study. 2.2 Instruments
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Cyberbullying experience was collected based on the questions from previous works of Patchin and Hinduja (2008) and Sourander et al. (2010). The first part of the questionnaire
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includes two general questions on cyberbullying: “During the past six months, how often
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have you been cyberbullied?” and “During the past six months, how often have you
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cyberbullied others?”. The second part consists of eight items that ask about the manner in which one has been cyberbullied (for example, ignored by others, disrespected by others, threatened, e-mail bombed, and so on). Responses to all the items are given on four-point
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Likert-scales, ranging from “Never” to “Almost daily.” The above questionnaire has been translated and validated into the Indonesian language during the study and has the
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Cronbach’s ∂ = 0.72.
High-risk behaviour that was considered in this study included cigarette smoking, alcohol
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consumption, and self-harm behaviour. Cigarette smoking and alcohol consumption experience was obtained by a self-rating questionnaire that included two questions: (1) How often do you smoke a cigarette/consume alcohol? (2) How many cigars do you smoke? Likert
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scale was employed to rate the three questions. Also, the self-harm behaviour questionnaire consisting of three items (self-hurting, seriously thinking of suicide, and attempted suicide, answered as ‘Yes’ or ‘No’) was used to identify the self-harm experienced among the adolescents. Additionally, the demographic characteristics were also brought together in the questionnaire. 2.3 Data analysis
All data were analysed using statistical SPSS for Mac version 21. The statistical significance is a set of 0.05 for all analysis. Chi-Square test and the odds ratio were also counted respectively to find out the association of those factors. 2. Results The adolescents’ age ranged between 11–18. This study obtained that 5.14% of adolescents disclosed that they were victims of cyberbullying, 2.43% revealed that they were the
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perpetrators. Meanwhile, 52.25% acted as victims/perpetrators respectively and most of them
were in the late adolescence period (Table 1). The male– female ratio was quite equal (Table
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1). Male and female adolescents aged 12–14 had a higher risk of being victims or being cyberbullies; meanwhile, older males and females (15- to 17-year-olds) were more likely to act as victim/perpetrator (Table 1). Both male and female victimization revealed that the most frequent form of cyberbullying experienced was ‘became afraid due to safety threat’ (34% vs.
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63.33%), followed by ‘threatened by others (33.3% vs. 63.33%) and ‘spammed by email by
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others’ (32% vs. 65.33%) (Table 2).
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Table 1
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Table 2
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The total number of adolescents who smoked cigarettes in this study was found to be 254 (8.7%). The highest proportion of smokers was found among adolescents above the age of 12 (94.6%). Male students were more likely to be smoking cigarettes. This study found that
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5.93% adolescents who had experienced cyberbullying either as victim, perpetrator, or victim/perpetrator smoked cigarettes (Table 3). The odds ratio of male cyberbullying
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perpetrators who did cigarette smoking was 2.97 (OR=2.97; 95% of Confidence Interval 1.49–5.99; p<0.05); and it also was higher compared to male adolescents that acted as victim/perpetrator (OR=1.54; 95% of Confidence Interval 1.12–2.13; p<0.05) (Table 4).
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This study revealed that 8.7% of total adolescents consumed alcohol; the higher proportion was found among male students. The higher proportion of alcohol drinking was obtained from adolescents who acted as victim/perpetrator (13.39%) (Table 3). Interestingly, male adolescents who were categorized as victim/perpetrator of cyberbullying showed an increased risk of consuming alcohol (OR=6.93, 95% of Confidence Interval 4.23–11.35, p<0.05) when compared to males who have never experienced any cyberbullying; in addition, this study showed that female adolescents who were victims/perpetrators of cyberbullying were also at
risk of alcohol consumption (OR=2.84, 95% of Confidence Interval 1.67–4.84, p<0.05) (Table 4). Table 3 Table 4 From this study, it was revealed that 6.38% of the total number of adolescents hurt
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themselves, 6.82% had suicidal ideation, and 2.47% attempted suicide. Meanwhile, this study also presented that a higher proportion of both male and female adolescents who were cyberbullying victims/perpetrators disclosed that they had ever hurt themselves, had suicide
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ideation, and attempted suicide (Table 3). The risk of self-hurting behaviour increased in both female and male cyberbullying victimization (OR=3.68, 95% Confidence Interval 1.81–7.46, p<0.05 and OR= 2.97, 95% Confidence Interval 1.15–7.68, p<0.05) (Table 4). In addition,
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the risk of suicide ideation increased among the female victim/perpetrator group (OR=1.90, 95% of Confidence Interval 1.24–2.89, p<0.05), while adolescent females who were
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3. Discussion
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of Confidence Interval 1.01–4.38, p<0.05).
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categorized as victim/perpetrator were at a higher risk of attempting suicide (OR=2.11, 95%
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This study found that the proportion of cyberbullying victimization, cyberbullies and highrisk behaviour among adolescents in both genders were quite similar compared to other worldwide studies cited above. However, the number of subjects who were cyberbullied and
PT
perpetrators of cyberbullying was quite high in both genders. It is said that being a victim of cyberbullying might intensify emotional volatility and have adverse outcomes among
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adolescents (Zhang et al., 2016). Geen (1998) explained that adolescents who are bullied possibly strike back against the aggressor and turn out to be the perpetrator by returning irritated statements (anger) and sending harassing messages to others. It is also reasonable to assume that children who annoy others using cyber media are targeted by other children,
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particularly to protect each other. Therefore, each incident might then increase further cyberbullying acts by students who are directly involved as well by their peers. When students feel upset for being wrongfully assaulted, they may respond aggressively. Further explanation comes from Beattie (2005), who mentioned that retaliation against bullying is so intense that the revenge may serve to protect the targeted student from feelings of
embarrassment, sadness, and powerlessness. In other words, adolescents who are being bullied may ‘bully back’ to counteract negative emotions after having been bullied. Cyberbullying and high-risk behaviour among adolescents is a major public health concern in most countries, including Indonesia. Several factors are said to be associated with it, such as the complex relationship between the period of teenage, developmental milestones, and the style of living. Adolescence is the period when one searches for self-identity. Therefore, the
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need for rational thinking is very important. During this critical period, adolescents are quickly involved in cyberbullying and other potentially harmful behaviour such as smoking,
drinking alcohol, and self-harm, mainly when their peer groups encourage and support these
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actions; it is a way to connect with, or fit in and gain acceptance among, peer groups. In
addition, the immaturity development of cognitive-control network might influence adolescents to engage in risk-taking behaviour, whether of their own will or through peer
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pressure. A constant drive of needing to fit in and specific peer influences can be seen in male adolescent behaviours, especially in delinquent and experimental high-risk acts. Two
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networks are involved in these processes: first, the socio-emotional network that is sensitive
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to emotional stimuli and reacts to the reward processing part of the brain, and second, the
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cognitive control network that mediates planning, rational thinking, and self-regulation. The socio-emotional network becomes very dominant among adolescents and lessens the
ED
cognitive control network due to the immaturity of the prefrontal cortex (Steinberg et al., 2007; Weiss et al., 2011). Therefore, adolescents are more easily involved in risk-taking,
PT
impulsive, and hostile behaviour (Wunderlich et al., 2001). This study showed that male adolescents that acted as cyberbullies or were victims/ perpetrators had a higher risk of externalizing behaviour through cigarette smoking, alcohol
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consumption, and self-harm. Male cyberbullies were students who showed a hostile behaviour towards certain students not on a traditional basis of bullying but through the specific digital media. The characteristics of hostile behaviours are pessimism, scepticism,
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and sarcasm. They also tend to be more selectively negative towards others. Male adolescents with hostile behaviours might be associated with tension and stress susceptibility, unsuccessful coping, and externalizing acts, and specifically, high-risk behaviour (Whalen et al., 2001; Hampson et al., 2007). The previous study found that male adolescents with hostile behaviours were independently related to unhealthy behaviours such as cigarette smoking, alcohol consumption, or even self-harm (Unger et al., 2004; Helstrom et al., 2004; Romer et al., 2007). Another related study assumed that smoking, alcohol consumption, or self-harm
among male cyberbullies was directed towards reducing tension and distress, and gaining acceptance among peers (Kovall et al., 2000). Therefore, cyberbullying itself might influence the odds of cigarette smoking, alcohol consumption, and self-harm among high school students as compared to traditional bullying that occurred at school only (Case et al., 2016). This study found that smoking was more prevalent among male adolescents over 12 years of age; this finding was similar to the study of Weiss et al. (2011) that reported that smoking prevalence was low among students under 12years of age compared to students in the middle
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stage of adolescence. Nicotine and alcohol are substance that can reduce anxiety and tension
because they activate several neurotransmitters, such as dopamine, serotonin, and beta-
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endorphin; therefore, it is assumed that early adolescence is a critical period that could trigger
anxiety, especially among males who have experienced cyberbullying. Thus, nicotine intake and alcohol consumption might repress the feeling of anxiety, hostile behaviour, and guilt induced by the cyberbullying – this is true for victim, perpetrator, and victim/perpetrator
U
(Benowitz, 2010; Buckner and Vinci, 2013; Lakon et al., 2015).
N
However, this study showed that female cyberbullying victimization had a higher risk to
A
result in internalizing behaviours, such as suicide ideation or suicide attempt. The female
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gender-role usually demands more affective dependence on human relations, especially towards peer groups or partner relationships; therefore, females with cyberbullying
ED
victimization are assumed to be less affectionate by their peer groups and turn to be more internalized and depressed. A study that was done in South Korea showed that female students respond to school bullying with suicidal ideations that are more acute as compared to
PT
male students. On the other hand, when school bullying persisted for a period of time, suicidal thoughts became equal between males and females, thus making the differences
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based on gender vanish over time (Kim et al., 2005). Nevertheless, this study did not measure the onset of cyberbullying; the present results could not support the above explanation any further. Another study explained that females were more vulnerable to the negative mental health effects of cyberbullying, such as depression, self-harm and suicidal ideas. It was
A
hypothesized that participants who had experienced cyberbullying in their later years had also experienced cyberbullying or other kinds of school bullying in their earlier years (Kraft and Wang, 2010). A longitudinal study has shown that involvement in bullying in childhood can contribute to and manifest as low self-esteem, aggressive behaviour towards one’s own self (self-harm), and alcohol consumption, especially in young female students in middle and high school(Sourander et al., 2007; Due et al., 2010; Selkie et al., 2015).
This was, to our understanding, the first study in Indonesia that included a high number of subjects at school and elaborated the high-risk behaviour outcomes in adolescents who have experienced cyberbullying. This study found a remarkable data based on the association between adolescents who experienced cyberbullying and high-risk behaviour outcomes that differed in terms of gender. Therefore, cyberbullying at school should be given more attention and given serious consideration, since gender is a significant implication and a determinant factor for high-risk behaviour outcomes. Several findings from the current
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studies revealed the urgency of teachers, counsellors, and mental health professionals to
address cyberbullying and its high-risk behaviour outcomes when assessing adolescents’
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health (specifically their mental health); further, it was suggested that the gender discrepancy be elaborated more deeply to provide a better understanding of their needs. The finding of this study make it quite clear that adolescents who had experiences relating to cyberbullying required mental health support and health support in general. However, evidence from several
U
clinical studies showed majority of adolescents did not seek help when experiencing
N
cyberbullying or performing high-risk behaviours due to the cyberbullying experienced.
A
Therefore, mental health professionals from different professions should take proactive approaches to serve these young people. This can be done through routine screening resulting
M
in early detection at school, and it can be applied by using simple questionnaires or direct observations of their emotional or behavioural appearance. Consequently, and especially to
ED
solve this problem comprehensively, relevant approaches that include gender-related issues should be redesigned, such as comprehensive school-based intervention and prevention
PT
programs that can be incorporated into school curricula and the community in general – for example, engaging adolescents at school in scholarly and community discussions related to empathy, social skills, legislation, and other gender specific issues. In addition, mental health
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promotion and prevention programs for children and adolescents at school, such as life-skill programs, could be actively achieved by both genders – it may include ways of handling peer pressure, strengthening the means of conflict resolution, dealing with emotion, overcoming
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stress, and enhancing self-esteem. There were several limitations to this study. The focus was exclusively on adolescents at school, and the results might be different among adolescents outside schools. The using of a self-rating questionnaire that might be perceived subjectively would consequently increase subjective bias; to minimize the bias, brief seminars were conducted for teachers and students, addressing bullying and protocol of the research prior to the studies, including each
definition. Therefore, teachers and students had a similar perception about the aims of the study. Another limitation was the small number of school that participated in this study – this might also affect the external validation. This is not to mention that there was no data that showed the duration of cyberbullying experienced; further, it did not include other risk factors such as mental illness (depression, anxiety disorder, psychotic disorder, bipolar disorder, and substance abuse), personality types (borderline personality disorder or severe introvert or extrovert traits), environmental factors (domestic violence, parental separation,
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and parental mental illness), and socio-economic factors (poverty) (Gould et al., 2003; Kim et
al., 2005). To conclude, these findings are not only very important but also provide insight
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into the adolescent mental health and wellbeing in these days, especially as it is related to
high-risk behaviour outcomes in adolescents with experience of cyberbullying through gender discrepancy.
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Acknowledge
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We would like to thank you all junior- and senior-high schools that took part in this study.
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International Collaboration Research Grant from Universitas Indonesia funded this study on
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2015. References
ED
Balding J., 2005. Young People in 2004: The health-related behavior questionnaire results for 40,430 young people between the ages of 10 and 15. Exeter, England: Schools Health Education Unit.
PT
Benowitz, N.L., 2010. Nicotine: Addiction. N. Engl. J. Med. 362, 2295 – 2303
CC E
Beran T., Li Q., 2005. Cyber-harassment: A study of a new method for an old behavior. J of Educational Computing Research. 32, 265 – 77 Beran T., Li Q., 2007. The relationship between cyberbullying and school bullying. Journal of Student Wellbeing. 1, 15 – 33
A
Berk, L.E., (2010). Development through the Lifespan, Fifth Edition. Pearson Education, United States. ISBN 9870205687930 Buckner, J.D., Vinci, C., 2013. Smoking and social anxiety: The roles of gender and smoking motives. Addict. Behav. 38, 2388 – 2391 Case, K.R., Cooper, M., Creamer, M., Mantey, D., Kelder, S., 2016. Victims of bullying and tobacco use behavior in adolescents: Differences between bullied at school, electronically, or both. J. Sch. Health. 86, 832 – 840
Chang, F.C., Ching, M.L., Chung, H.C., Wen, Y.H., Tzu, F.H., Yun, C.P., 2013. Relationships among cyberbullying, school bullying, and mental health in Taiwanese adolescents. J. Sch. Health. 83, 454 – 462 Due P., Damsgaard M.T., Lund R., et al., (2009). Is bullying equally harmful for rich and poor children? A study of bullying and depression from age 15 to 27. European Journal of Public Health.19, 464 – 469
IP T
Evans-Lacko, S., Takizawa, R,, Brimblecombe, N., King, D., Knapp, M., Maughan, B., et al., 2017. Childhood bullying victimization is associated with use of mental health services over five decades: a longitudinal nationally representative cohort study. Psychol. Med. 47, 127 – 135
SC R
Gould M.S., Greenberg T., Velting D.M., Shaffer D., 2003. Youth suicide risk and preventive interventions: a review of the past 10 years. J Am Acad Child Adolesc Psychiatry.42, 386 – 405
N
U
Guerra, N.G., Williamson, A.A., Lucas-Molina, B., 2012. Normal development: Infancy, childhood, and adolescence. In: Rey JM (ed), IACAPAP e-Textbook of Child and Adolescent Mental Health. Geneva: International Association for Child and Adolescent Psychiatry and Allied Professions.
M
A
Hampson, S.E., Andrews, J.A., Barckley, M., 2007. Predictors of the development of elementaryschool children's intention to smoke cigarettes: hostility, prototypes, and subjective norms. Nicotine Tob. Res. 9, 751–760 Helstrom, A.W., Bryan, A.D., Hutchison, K.E., et al., 2004. Tobacco and alcohol use as an explanation for the association between externalizing behavior and illicit drug use among delinquent adolescents. Prev. Sci. 5, 267–277
ED
Hinduja, S., Patchin, J.W., 2009. Bullying beyond the schoolyard: Preventing and responding to cyberbullying. Thousand Oaks, CA: Corwin Press.
PT
Hinduja, S., Patchin, J. W., 2010. Bullying, cyberbullying, and suicide. Arch. of Suicide Resch. 14, 206 – 221
CC E
Kim Y.S., Koh Y.J., Leventhal B., 2005. School bullying and suicidal risk in Korean middle school students. Pediatrics. 115, 357 – 363
A
Kovall, J.J., Pederson, L.L., Mills, C.A., et al., 2000. Models of the relationship of stress, depression, and other psychological factors to smoking behavior: A comparison of a cohort of students in grades 6 and 8. Prev. Med. 30, 463 – 477
Kraft E.M., Wang J., (2010). An exploratory study of the cyberbullying and cyberstalking experiences and factors related to victimization of students at a public liberal arts college. International Journal of Technoethics. 1, 74 – 91 Kubwalo, H.W., Muula, A.S., Siziya, S., Pasupulati, S., Rudatsikira, E., 2013. Prevalence and correlates of being bullied among in-school adolescents in Malawi: results from the 2009 Global School-Based Health Survey. Malawi Med. J. 25,12 – 14
Langos, C., 2012. Cyberbullying: The challenge to define. Cyber-psychology, Behavior, and Social Networking. 15, 285 – 289 Lakon, C.M., Hipp, J.R., Cheng, W., Butts, C.T., Jose, R., 2015. Simulating Dynamic Network Models and Adolescent Smoking: The Impact of Varying Peer Influence and Peer Selection. Am. J. Public Health. 105, 2438 – 2448 Navarro, R., Larrañaga, E., Yubero, S., 2011. Bullying-victimization problems and aggressive tendencies in Spanish secondary schools students: the role of gender stereotypical traits. Social Psychology of Education. 14, 457 – 473
IP T
Navarro, J. N., Jasinski, J. L., 2013. Why girls? Using routine activities theory to predict cyberbullying experiences between girls and boys. Women and Criminal Justice. 23, 286 – 303.
SC R
Nixon C.L., (2014). Current perpectives: The impact of cyberbullying on adolescent health. Adolesc Health Med Ther. 5, 143 – 158
Pauletti, R.E., Perry, D.G., (2011). Gender and Adolescent Development. Journal of Research on Adolescence. 21, 61 – 74
N
U
Razak N., 2014. Study: Most children in Indonesia are online now, but many are not aware of potential risks. Unicef-Indonesia. (Accessed from Unicef.org, 7 April 2018)
A
Romer, D.P.S., 2007. Associations between smoking and depression in adolescence: an integrative review. Taehan Kanho Hakhoe Chi. 37, 227–241
M
Sansone, R.A., Sansone, L.A., 2008. Bully victims: Psychological and somatic aftermaths. Psychiatry (Edgmont). 5, 62 – 64
ED
Selkie E.M., Kota R., Chan Y.F., Moreno M., 2015. Cyberbullying, depression and problem alcohol use in female college students: A multisite study. Cyber-psychology, Behavior and Social Networking. 18, 79 – 86
PT
Smith P.K.,.Mahdavi J., Carvalho M., Fisher S., Russell S., Tippett N., 2008. Cyberbullying: Its nature and impact in secondary school pupils. J Child Psychol Psychiatry. 49, 376 – 385
CC E
Snakenborg, J., Van Acker, R., & Gable, R. A., 2011. Cyberbullying: Prevention and intervention to protect our children and youth. Preventing School Failure: Alternative Education for Children and Youth. 55, 88 – 95
A
Sourander, A., Sourander A., Jensen P., Ronning J.A., et al., (2007). What is the early adulthood outcome of boys who bully or are bullied in childhood? The Finnish ‘‘From a Boy to a Man’’ study. Pediatrics.120, 397 – 404
Sourander, A., Brunstein Klomek, A., Ikonen, M., Lindross, J., Luntamo, T., Koskelainen, M., et al., 2010. Psychosocial risk factors associated with cyberbullying among adolescents: a population-based study. Arch. Gen. Psychiatry. 67, 720 – 728 Steinberg, L., 2007. Risk Taking in adolescence; New perspective from brain and behavioral Science. Association for Psychological Science, Philadelphia.
Tokunaga, R.S., 2010. Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior. 26, 277 – 287 Unger, J.B., Chou, C.P., Palmer, P.H., et al., 2004. Project FLAVOR: One-year outcomes of a multicultural, school-based smoking prevention curriculum for adolescents. Am. J. Public Health. 94, 263 – 265.
IP T
US Centre for Disease Control. Youth and tobacco use. Available at: http://www.cdc.gov/tobacco/data_statistics/fact_sheets/youth_data/tobacco_use/index.htm#estima tes. Accessed 7 November 2017
SC R
Weiss, J.W., Cen, S., Mouttapa, M., Johnson, A.C., Unger, J., 2011. Longitudinal effects of hostility, depression, and bullying on adolescent smoking initiation. J. Adolesc. Health. 48, 591 – 96. Whalen, C., Whalen, C., Jamner, L., Henker, B., et al., 2001. Smoking and moods in adolescents with depressive and aggressive dispositions: Evidence from surveys and electronic diaries. Health Psychol. 20, 99 – 111
N
U
Wolfer, R., Scheithauer, H., 2014. Social influence and bullying behavior: interventionbased network dynamics of the fair-player manual bullying prevention program. Aggress. Behav. 40, 309 – 19
M
A
Wunderlich U., Bronisch T., Wittchen H.-U., Carter R., 2001. Gender differences in adolescents and young adults with suicidal behaviour. Acta Psychiatr Scand. 104, 332 – 339
A
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PT
ED
Zhang, X., Ra, C.K., Zhang, D., Zhang, Y., MacLeod, K.E., 2016. The impact of school social support and bullying victimization on psychological distress among California adolescents. Calif. J. Health Promot.14, 56 – 67
Table 1. Age characteristic of the research subjects Bullying experienced (n=2917; 100%)
30 (1.03)
15
0 (0.00) 5 (0.17)
16
1 (0.03) 0 (0.00)
17
0 (0.00) 0 (0.00)
18
0 (0.00) 0 (0.00)
Total
51 (1.75)
CC E A
0 (0.00)
4 (0.14) 127 14 (0.48) (4.35) 205 37 (1.27) (7.03) 154 86 (2.95) (5.28) 203 42 (1.44) (6.96) 255 5 (8.74) (0.17) 234 0 (8.02) (0.00) 0 4 (0.14) (0.00) 834 537 (28.59) (18.41) 1 (0.03)
Female (%)
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0 (0.00) 7 6 (0.21) (0.24) 39 12 (0.41) (1.34) 85 7 (0.24) (2.91) 160 2 (0.07) (5.48) 190 0 (0.00) (6.51) 197 0 (0.00) (6.75) 12 0 (0.00) (0.41) 27 690 (0.93) (23.65)
PT
99 (3.39)
1 (0.03) 5 (0.17) 16 (0.55) 13 (0.46) 9 (0.31) 0 (0.00) 0 (0.00) 0 (0.00) 44 (1.51)
Female (%)
Total (%)
11 (0.38) 19 (0.65) 345 (11.83) 602 239 (8.19) (20.64) 602 207 (7.10) (20.64) 450 29 (0.99) (15.43) 148 (5.07)
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14
38 (1.30)
Male (%)
Female (%)
U
13
25 (0.86)
Male (%)
Male (%)
N
12
1 (0.03)
Non-victim/ Non-perpetrator (n=1172; 40.18)
A
1 (0.03) 13 (0.46) 16 (0.55) 20 (0.69)
11
Female (%)
Victim/ Perpetrator (n=1524; 52.25%)
M
Male (%)
Perpetrator (n=71; 2.43%)
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Age (Years)
Victim (n=150; 5.14%)
1 (0.03) 452 (15.49) 0 (0.00) 431 (14.77) 0 (0.00) 16 (0.55) 635 (21.77)
2917 (100)
Table 2. The most frequent forms of cyberbullying among adolescents with cyberbullying experienced
Victim (n=150)
Forms of cyberbullying
Victim/Perpetrator (n=1523) Female
Ignored by others
47 (31.33%)
90 (60%)
599 (39.33%)
686 (45.04%)
Not appreciated by others
44 (29.33%)
86 (57.33%)
569 (37.36%)
671 (44.06%)
Called using names by others
43 (28.67%)
85 (56.67%)
Gossip spread by others
47 (31.33%)
84 (56%)
Threatened by others
50 (33.33%)
95 (63.33%)
Spammed by e-mail by others
48 (32%)
Offended by others
47 (31,33%)
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707 (46.42%)
581 (38.15%)
654 (42.94%)
658 (43.20%)
804 (52.79%)
83 (53.33%)
541 (35.52%)
589 (39.67%)
46 (30.67%)
86 (57.33%)
586 (38.48%)
734 (48.19%)
51 (34%)
95 (63.33%)
659 (43.27%)
800 (52.53%)
N
U
98 (65.33%)
A
PT CC E A
556 (36.51%)
791 (51.94%)
ED
Became afraid due to safety threat
Female
627 (41.17%)
M
Mocked
Male
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Male
Table 3. High-risk behaviour among adolescents with cyberbullying experienced based on the gender differences Adolescents with cyberbullying experienced Victim (n=150; 5.14%)
Perpetrator (n=71; 2.43%)
Victim/ Perpetrator (n=1524;52.25%)
Non-victim/ Non-perpetrator (n=1172;40.18%)
Male (%)
Male (%)
Male (%)
Female (%)
Male (%)
567 (19.44) 123 (4.22)
813 (27.87)
470 (16.11) 66 21 (0.72) (2.27)
618 (21.19)
550 (18.85) 140 (4.80)
770 (26.40)
517 (17.72)
615 (21.08)
Female (%)
Female (%)
Never Yes
43 (1.47)
94 (3.22)
31 (1.06) 13 8 (0.27) 5 (0.17) (0.45)
24 (0.82) 3 (0.10)
Alcohol drinking 95 (3.25)
5 (0.17) 4 (0.14)
41 (1.41)
26 (0.89)
3 (0.10) 1 (0.03)
86 (2.95)
No
50 (1.71) 1 (0.03)
A
Yes
763 (26.16)
513 (17.59) 23 63 (2.16) (0.79)
608 (20.84)
593 (20.33) 51 (1.75)
748 (25.64)
516 (17.69) 20 78 (2.67) (0.69)
600 (20.57)
27 (0.93) 623 (21.36) 0 (0.00) 21 0 (0.00) (0.72)
799 (27.39)
623 (21.36)
ED 41 (1.41)
26 (0.89)
10 (3.43) 3 (0.10) 1 (0.03)
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Attempted suicide
89 (3.05)
PT
Yes
48 (1.65) 3 (0.10)
529 (18.13) 52 (1.78)
27 (0.93)
13 (0.46) 4 (0.14) 0 (0.00)
Suicide idea No
40 (1.37)
M
Yes
45 (1.54) 6 (0.20)
A
Self-hurting behavior: No
2660 (91.19) 254 64 (2.19) 19 (0.65) 18 (0.62) (8.70)
U
Yes
46 (1.58)
2660 (91.19) 254 15 (0.51) (8.70)
N
Never
Female (%)
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Cigarette Smoking
Total (%)
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Charateristics
96 (3.29) 3 (0.10)
44 (1.51)
526 (18.03) 10 27 (0.93) (3.43)
2674 (91.67) 186 25 (0.86) (6.38)
2661 (91.22) 199 33 (1.13) (6.82)
2788 (95.58) 72 10 (3.43) (2.47)
Table 4. Association between cyberbullying experienced and high-risk behaviour outcomes based on the gender differences Male adolescent Odds Ratio 95% CI (OR) –
1.00 2.19
0.78 – 6.17
–
3.43
0.74 – 15.87
–
1.06
0.54 – 2.08
1.06 8.29 0.57 7.01 4.23 11.35
–
1.00 1.44 1.31
0.17 – 10.22
2.84*
1.67 – 4.84
–
1.00 3.68*
1.81 – 7.46
–
0.43
0.02 – 7.31
–
2.01*
1.25 – 3.23
0.46 5.62 0.54 6.62 0.46 5.62
–
1.00 2.04
0.97 – 4.23
–
0.70
0.09 – 5.31
–
1.90*
1.24 – 2.89
0.13 8.39 Perpetrator 0.56 0.03 9.77 Victim/Perpetrator 1.77 0.82 3.80 *p<0.05; odds ratio (OR) adjusted from socio-economic factor
–
1.00 1.95
0.53 – 7.20
–
1.08
0.06 – 18.90
–
2.11*
1.01 – 4.38
Perpetrator
2.97*
Victim/Perpetrator
1.54*
Alcohol drinking (n=2914) Non-victim/Non-perpetrator Victim
1.00 2.96*
Perpetrator
1.99
Victim/Perpetrator
6.93*
Victim/Perpetrator
2.19*
PT
Victim/Perpetrator
ED
Perpetrator
Attempted suicide (n=2860) Non-victim/Non-perpetrator Victim
CC E
1.00 1.61 1.89 1.61
1.00 1.05
1.15 7.68 0.73 6.76 1.32 3.63
–
N
2.23
M
Perpetrator
Suicide idea (n=2860) Non-victim/Non-perpetrator Victim
A
1.00 2.97*
A
Self-hurting behavior (n=2860) Non-victim and Non-perpetrator Victim
–
U
1.00 1.32
SC R
0.59 2.94 1.49 5.99 1.12 2.13
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Cigarette smoking (n=2914) Non-victim/Non-perpetrator Victim
Female adolescent Odds Ratio 95% CI (OR)
0.48 – 4.34