Gender Differences in Image-Based Sexual Abuse and Cyberbullying victimization

Gender Differences in Image-Based Sexual Abuse and Cyberbullying victimization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender Differences in Image-Based Sexual Abuse and Cyberbullying victimization Yunhao Hu, Elizabeth Clancy, Bianca Klettke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6703468/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2025 Read the published version in International Journal of Bullying Prevention → Version 1 posted 5 You are reading this latest preprint version Abstract Harmful experiences such as cyberbullying victimization have been associated with the unwanted and non-consensual subset of sexting behaviours known as image-based sexual abuse (IBSA). However, there is little understanding surrounding gender differences in that association. The present study contributes to that understanding through examining gender differences in relationships between IBSA, cyberbullying perpetration, and cyberbullying victimization. Study participants consisted of 1683 young cisgendered adults ( M age = 23.15, SD = 2.23, 52.7% women) who completed an anonymous online survey on sexting and harmful online behaviours. Associations between sext sending/receiving and cyberbullying victimization could be largely accounted for by IBSA victimization, but with unique gendered patterns. Specifically, being a victim of non-consensual sext dissemination and receiving unwanted sexts was found to be associated with cyberbullying victimization for women but not for men. Conversely, pressured sexting and receiving unsolicited sexts via AirDrop was associated with cyberbullying victimization for men, but not women. Finally, cyberbullying perpetration predicted cyberbullying victimization for both genders, but had a stronger association for men, suggesting that the impact of IBSA is gendered and nuanced. Future research should explore the social environments in which IBSA victimization occurs and broader gendered behaviours. 1. Introduction Innovations in digital communicative technology have fundamentally changed how people access information and navigate their daily lives, in both positive and negative ways. The personal and relational consequences of online use, as well as how accessibility and anonymity might impact online behaviours have become subjects of great importance. One such behaviour is cyberbullying, defined as an intentional, repeated act of aggression, carried out by a group or individual through an electronic medium, against a victim who cannot easily defend themselves (Smith et al., 2008). Findings regarding prevalence rates for cyberbullying behaviours vary widely depending on definitions and measurements utilized in research. A systematic review on cyberbullying in adults (Jenaro et al., 2018) found that cyberbullying perpetration rates ranged from 0.56% to 54.3%, while cyberbullying victimization rates ranged from 2.38% to 90.86%. Concerningly, victims of cyberbullying have been found to experience greater symptoms of depression and anxiety, shame, guilt, and suicidal ideation (Bottino et al., 2015; Jenaro et al., 2018; Kowalski et al., 2019; Kwan et al., 2020; Medrano et al., 2018). These consequences occur for adolescents and adults and can impact both perpetrators and victims (Bottino et al., 2015; Jenaro et al., 2018). This is perhaps unsurprising, as research has suggested a bi-directional relationship between cyberbullying and cyberbullying victimization (Dou et al., 2020; Hemphill & Heerde, 2014; Hua et al., 2019; Myers & Cowie, 2019; Tanrikulu & Erdur-Baker, 2021; Zsila et al., 2019). The association between cyberbullying perpetration and cyberbullying victimization offers a potential explanation for the complex nature of cyberbullying behaviours. Victims of cyberbullying are likely to become perpetrators themselves, either through retaliatory behaviour due to anger, or through bullying other victims to redirect their feelings. In turn, perpetrators can become victims as a result of retaliation from their initial targets (Dou et al., 2020; Hemphill & Heerde, 2014; Wang et al., 2019; Zsila et al., 2019). Given the persistence of cyberbullying behaviours and its associated consequences, it is important to better understand the risk factors associated with cyberbullying victimization, improve prevention programs and protect victims of cyberbullying behaviours. One such risk factor may be the experiences of unwanted and non-consensual sexting. as prior research has demonstrated that individuals who are subjected to non-consensual sext dissemination or receive unsolicited sexual content are at an increased risk of cyberbullying victimization due to shared vulnerabilities (Burke & Norvilitis, 2020; Gámez‐Guadix et al., 2022a; Ojeda et al., 2019; van Ouytsel et al., 2021; Wachs et al., 2021). This underscores the broader implications of sexting, particularly when it occurs without consent. 1.1. Sexting & Image-Based Sexual Abuse Sexting is a popular form of online sexual interaction among adolescents and adults, and can be defined as the sending, receiving, or forwarding of sexually explicit content to others through electronic means (K. Walker & Sleath, 2017). A meta-analytic study found that 38.3% of young adults sent sexts, 41.5% have received sexts, while 47.7% sent and received sexts reciprocally (Mori et al., 2020). Early research conceptualized sexting as a harmful behaviour, similar to harassment (Diliberto & Mattey, 2009) and as a risk factor for cyberbullying victimization, given that shared intimate material could be distributed to third parties and used for intimidation or blackmail (Dake et al., 2012; Gámez-Guadix et al., 2015; Reyns et al., 2013; Woodward et al., 2017). This conceptualization is problematic, placing responsibility for the behaviour on cyberbullying victims engaged in consensual sexting activity, rather than on the perpetrators of non-consensual behaviours (Döring, 2014; Henry & Powell, 2015; Krieger, 2017; McGlynn & Rackley, 2017). As such, this conceptualisation encourages victim blaming, minimizes the harm experienced by victims, and downplays perpetrator responsibility (Döring, 2014; McGlynn et al., 2021; Naezer & van Oosterhout, 2021; K. Walker & Sleath, 2017). Recent studies have found that the association between sexting and harmful outcomes such as cyberbullying victimization can be explained by the unwanted and non-consensual subset of sexting behaviours (Burke & Norvilitis, 2020; Gámez‐Guadix et al., 2022a; Klettke et al., 2019; Ojeda et al., 2019; van Ouytsel et al., 2021; Wachs et al., 2021), otherwise known as image-based sexual abuse (IBSA; DeKeseredy & Schwartz, 2016; McGlynn & Rackley, 2017; Powell & Henry, 2018). The most prominent of IBSA behaviours is non-consensual sext dissemination, defined as the distribution of intimate, nude, and/or sexually explicit images to audiences other than its intended recipient without the consent of those depicted (K. Walker & Sleath, 2017), which has been associated with harmful behaviours such as cybervictimization and intimate partner violence (Cornelius et al., 2020; Gámez‐Guadix et al., 2022a). Other problematic forms of IBSA include pressured sexting (e.g., coercion, peer pressure), receiving unwanted sexts (e.g., cyberflashing), and receiving anonymous unsolicited sexts (e.g., via AirDrop). Victims of IBSA have been found to experience negative health consequences similar to those experienced by victims of cyberbullying, including depression, self-harm, and the loss of self-esteem (Bates, 2017; Burke & Norvilitis, 2020; McGlynn et al., 2021; Valiukas et al., 2019; Wachs et al., 2021). 1.2. Gender Differences in Cyberbullying victimization and IBSA While research into IBSA and cyberbullying victimization is growing (Gámez‐Guadix et al., 2022b; Henry & Beard, 2024; Pedersen et al., 2023), there is little understanding surrounding gender differences in the relationship between IBSA and cyberbullying victimization. Cyberbullying and IBSA victimization have been found to impact men and women differently. Meta-analyses suggest men are more likely to engage in cyberbullying perpetration, while women are more likely to experience cyberbullying victimization (Guo, 2016; Sun et al., 2016).This is surprising given the strong bidirectionality in the bully-victim relationship, suggesting the presence of additional factors placing women at greater risk of experiencing cyberbullying victimization. Moreover, studies found that women experienced greater levels of emotional distress (e.g., depressive symptoms) in response to cyberbullying victimisation compared to men (Brown et al., 2014; Foody et al., 2019). Previous findings regarding gender differences in overall sext sending/receiving behaviours are varied; some meta-analytic research found that men were more likely to both send and receive sexts (Mori et al., 2020), while others found no significant gender differences (Madigan et al., 2018; Molla-Esparza et al., 2020). Concerning IBSA victimization, research found that the likelihood of becoming a victim of non-consensual sext dissemination did not differ significantly across genders (Clancy et al., 2020; Madigan et al., 2018; Mussap et al., 2023; Powell et al., 2020; Sciacca et al., 2023; K. Walker et al., 2021). However, consequences of victimization were found to be more severe for women than men. For example, women who had their sexts shared without consent were more likely to be confronted with social stigmatization and blamed for the initial creation or sharing of sexts (McGlynn et al., 2021; Naezer & van Oosterhout, 2021; K. Walker & Sleath, 2017). Women were also more likely to experience non-consensual sext dissemination in the context of interpersonal harm, such as stalking, sexual assault, and intimate partner abuse (McGlynn et al., 2021; Powell et al., 2020). Further, studies have shown that women are more likely to experience pressured-sexting and receive unwanted sexts (Klettke et al., 2019; van Ouytsel et al., 2017; Wachs et al., 2021; K. Walker et al., 2021; K. Walker & Sleath, 2017). Gender differences were also observed in the impact of unwanted and pressured sexting, with men found to experience more negative mental health outcomes as a result of receiving unwanted sexts (Klettke et al., 2019), while women experienced greater emotional distress as a result of pressured sexting (Wachs et al., 2021). Finally, to our knowledge, no empirical research currently exists on the receiving of unsolicited sexts via AirDrop. It is important to consider the experiences of victims who receive unsolicited sexts via AirDrop, given that sexts can be anonymously exchanged through AirDrop only if the devices are physically close (i.e. within 30 feet, (Freeman, 2020), indicating a level of proximity between the perpetrator and victim. Knowing a perpetrator is physically close but being unable to identify them could be an aggravating factor for victims, thus a better understanding of proximate IBSA victimization is warranted. One study on cyber-flashing law reform places receiving unsolicited sexts via AirDrop as a gendered experience, with men acting primarily as perpetrators and women as victims (McGlynn & Johnson, 2021). Given that victimization in cyberbullying and IBSA instances are gendered, associations between the two phenomena may likewise differ between genders. A better understanding of these differences would assist with the development of future gender-based cyberbullying prevention programs. 1.3. Current Research The objective of this study was to examine cisgendered differences in predictors for cyberbullying victimization. Based on previous literature, we expect sext sending and receiving to be significantly associated with cyberbullying victimization for both men and women (Hypothesis 1), however that association would be better accounted for by IBSA victimization (Hypothesis 2). Concerning gender differences, based on prior findings surrounding the consequences of IBSA, we expected being a victim of non-consensual sext dissemination and pressured sexting and would be more strongly associated with cyberbullying victimization for women, while receiving unwanted sexts would have a greater association with cyberbullying victimization for men (Hypothesis 3). Due to the paucity of research exploring the receiving of unsolicited sexts via AirDrop, no a priori hypothesis was developed. Finally, given that prior research found men were more likely to exhibit cyberbullying behaviour as a result of cyberbullying victimization (Cunningham et al., 2015; Hemphill & Heerde, 2014; Orel et al., 2017; Zsila et al., 2019), we hypothesised that cyberbullying perpetration would have a stronger association with cyberbullying victimization for men compared to women (Hypothesis 4). 2. Method 2.1. Participants An initial 2828 participants commenced the survey. After removal of incomplete responses ( N = 1012), those who were outside age inclusion criteria ( N = 101), and gender diverse participants due to insufficient sample size ( N = 33) and the study’s focus on cis-gendered adults, this resulted in a final sample of 1682 young adults (47.3% men , 52.7% women ). Eligible participants were 18–30 years old, with a mean age of 23.15 years ( SD = 3.23). Most participants reported being sexually active (76.4%) with the average age of sexual activity being 17.32 years ( SD = 2.4). Regarding sexual orientation, 75.6% identified themselves as being Heterosexual, 14.9% as Bisexual, 4.3% as Lesbian/Gay, 2.9% as Uncertain, and 2.3% as Other or unwilling to disclose. Concerning educational attainment, 37.7% achieved a Bachelor’s degree, 30.8% Year 12 or equivalent, 13.5% Postgraduate Degree, 6.6% Certificate level, and 6.1% Advanced Diploma/Diploma. Most participants were from Australia (77.2%), followed by United Kingdom (9.9%), USA (5.0%), Other Country (5.0%), and New Zealand (2.9%). Concerning ethnicity, 48.9% were Australian, 20.8% were British or European, 15.6% were Asian, and 14.7% endorsed another ethnicity. 2.2. Procedure This study was approved by [Blinded for Review] Ethics Committee. Before participants commenced the voluntary, confidential survey, general study aims were outlined via a plain language statement. Participants provided informed consent before they commenced the survey, which took approximately 20–25 minutes to complete. Overall, 1204 participants (71.6%) were recruited via social media, while 478 participants (28.4%) were recruited through survey aggregator Prolific to produce a more gender balanced sample. No incentive was offered for social media participants while Prolific participants received a minor financial reimbursement. Prolific participants were, on average, older than social media participants ( Prolific M = 24.3, SD = 3.54, social media M = 22.70, SD = 2.98; t (1680) = 9.38, p < .001), more likely to be men ( Prolific 95.4%, social media 28.2%, χ2(1) = 620.67, p < .001), and engage in cyberbullying ( Prolific 39.5%, social media 31.4%, χ2(1) = 10.16, p < .001). Social media participants were more likely to be sexually active ( Prolific 68.1%, social media 79.7%, χ2(1) = 25.12, p < .001), send sexts ( Prolific 52.1%, social media 74.7%, χ2(1) = 80.48, p < .001), receive sexts ( Prolific 71.8%, social media 84.3%, χ2(1) = 34.62, p < .001), received unwanted sexts ( Prolific 19.2%, social media 50.8%, χ2(1) = 140.25, p < .001), send sexts under pressure ( Prolific 16.7%, social media 38.0%, χ2(1) = 70.89, p < .001), and be victims of non-consensual sext dissemination ( Prolific 5.6%, social media 11.5%, χ2(1) = 13.37, p < .001). There was no significant difference between Prolific and social media participants concerning cyberbullying victimization ( Prolific 69.2%, social media 67.4%, χ2(1) = 0.56, p = .45), and receiving unsolicited sexts via AirDrop ( Prolific 6.7%, social media 7.4%, χ2(1) = 0.25, p = .62). 2.3 Measures The Internet Harassment Survey(Ybarra et al., 2007 ) includes questions on how many times a participant experienced cyberbullying perpetration or victimization in the past year. The questionnaire consisted of three items on cyberbullying victimization (e.g., Receive rude or nasty comments from someone while online ) and three items on cyberbullying (e.g., Spread rumours about someone online ). Items were rated on a frequency scale: everyday/almost every day; once or twice a week; once or twice a month; a few times a year; less than a few times a year; neve r. Participants who reported experiencing cyberbullying perpetration or victimization in the past year were coded as being perpetrators or victim respectively. In this sample, the Cronbach’s alpha was 0.78 for victimization and 0.75 for perpetration, consistent with prior validation efforts (Ybarra et al., 2007 ). The Sexting Behaviours Questionnaire was used to explore sexting behaviours among young adults (Clancy et al., 2021). For the purposes of this study, sexts were defined as “sexually explicit images, sent, received or shared via mobile phone messaging or apps.” Participants were asked whether they had experienced or engaged in the following behaviours ( Yes/No ): sent sexts “Have you ever sent an image-based sext of yourself?” ; received sexts “Have you ever received an image-based sext” ; received unwanted sexts “Have you ever received an image-based sext that was unwanted/unwelcome?” ; pressured sexting “Have you felt pressure to send an image-based sext? If yes, as a result of that pressure, did you send an image-based sext?” ; received unsolicited sexts via AirDrop “Have you ever been airdropped a sexually explicit image without your consent?” ; and non-consensual sext dissemination victimization “Have you ever sent an image-based sext of yourself that was subsequently forwarded (to your knowledge)? Had you given permission for this image to be forwarded?” 2.4 Analysis IBM SPSS Statistics 28.0 was used to conduct relevant statistical analyses for the study. Descriptive and chi-square analysis enabled review of sample characteristics by gender. Bivariate correlational analysis tested the association between measured variables for men and women. Only variables significantly correlated with cyberbullying victimization were included in subsequent stepwise logistical regressions to examine gender differences in associations. 3. Results Descriptive statistics for study variables examined by gender are shown in Table 1 . Men were significantly more likely to engage in cyberbullying perpetration and experience cyberbullying victimization, while women were significantly more likely to send and receive sexts, sext under pressure, receive unwanted sexts, and be victims of non-consensual sext dissemination. Men and women did not significantly differ in regard to receiving unsolicited sexts via AirDrop. Table 1 Key Variables by Gender Variable Full Sample (N = 1682) Men (n = 795) Women (n = 887) Comparison, Men to Women Sent Sext 68.3% 60.3% 75.4% χ 2 (1) = 44.54, p < .001 Received Sext 80.7% 76.7% 84.3% χ 2 (1) = 15.57, p < .001 Pressured Sexting * 46.9% 31.5% 57.8% χ 2 (1) = 77.68, p < .001 Received Unwanted Sext ** 51.8% 26.2% 72.7% χ 2 (1) = 290.97, p < .001 Airdropped Sext Without Consent 7.2% 6.7% 7.7% χ 2 (1) = 0.63, p = .428 Non-Consensual Dissemination Victim 9.9% 7.2% 12.3% χ 2 (1) = 12.35, p < .001 Cyberbullying Perpetrator 33.7% 42.3% 27.0% χ 2 (1) = 49.37, p < .001 Cyberbullying Victim 67.9% 71.9% 64.3% χ 2 (1) = 11.37, p < .001 Note: * = % provided based on those who sent sexts (N = 1148), ** = % provided based on those who received sexts (N = 1358). 3.1. Correlations Bivariate correlations were conducted separately for men and women. Correlation analyses for men found cyberbullying victimization to be significantly associated with all variables other than sext sending/receiving, detailed findings are presented in Table 2 . In contrast, correlation analysis for women found all predictor variables to be associated with cyberbullying victimization, as detailed in Table 3 . Table 2 Correlations for Key Variables by Gender (Men) Variables Age Sent Sext Received Sext Pressured Sexting Received Unwanted Sext Airdropped Sext Without Consent Non-Consensual Dissemination Victim Cyberbullying Perpetrator Cyberbully Victim Age 1 — Sent Sext 0.19 1 — Received Sext .070 .642** 1 — Pressured Sexting − .036 .393** .267** 1 — Received Unwanted Sext − .025 .199** .276** .213** 1 — Airdropped Sext Without Consent − .073* .001 .052 .076* .168** 1 — Non-Consensual Dissemination Victim − .009 .226** .153** .325** .213** .063 1 — Cyberbullying Perpetrator − .177** .008 .043 .138** .079* .118** .108** 1 — Cyberbully Victim − .148** .014 .067 .152** .111** .144** .098** .472** 1 * p < .05, ** p < .01 Table 3 Correlations for Key Variables by Gender (Women) Variables Age Sent Sext Received Sext Pressured Sexting Received Unwanted Sext Airdropped Sext Without Consent Non-Consensual Dissemination Victim Cyberbullying Perpetrator Cyberbully Victim Age 1 — Sent Sext 0.10 1 — Received Sext − .038 .618** 1 — Pressured Sexting − .106** .501** .353** 1 — Received Unwanted Sext − .101** .262** .543** .351** 1 — Airdropped Sext Without Consent − .051 − .003 .019 .063* .081* 1 — Non-Consensual Dissemination Victim − .048 .198** .152** .274** .220** .137** 1 — Cyberbullying Perpetrator − .075* .112** .100** .137** .112* .090** .114** 1 — Cyberbully Victim − .129** .082* .132** .161** .253** .082* .179** .362** 1 * p < .05, ** p < .01 3.2. Stepwise Logistic Regression Separate stepwise logistic regressions were conducted for men (Table 4 ) and women (Table 5 ), with cyberbullying victimization as the dependent variable. Predictor variables included age, sext-sending, sexts-receiving, pressured sexting, receiving unwanted sexts, receiving unsolicited sexts via AirDrop, non-consensual sext dissemination victimization, and cyberbullying. Only variables significantly associated with cyberbullying victimization in prior bivariate correlations were included in each regression. Table 4 Stepwise Logistic Regression Results Regarding Cyberbullying victimization (Men) Variable 95% CI B df p Exp(B) Lower Higher R 2 Nagelkerke R 2 Change Step 1 .031 .031** Step 2 .106 .075** Step 3 .377 .271** Constant 1.07 1 .124 2.91 - - Age -0.05 1 .095 0.95 0.90 1.01 Pressured Sexting 0.61 1 .032 1.85 1.05 3.24 Received Unwanted Sext 0.36 1 .164 1.43 0.86 2.39 Received Unsolicited Airdropped Sext 1.96 1 .009 7.11 1.63 31.10 Non-Consensual Sext Dissemination 0.47 1 .321 1.61 0.63 4.09 Cyberbully Perpetration 3.10 1 < .001 22.24 11.81 41.91 Note. ** p < 0.01 Table 5 Stepwise Logistic Regression Results Regarding Cyberbully Victimization (Women) Variable 95% CI B df p Exp(B) Lower Higher R 2 Nagelkerke R 2 Change Step 1 .022 .022** Step 2 .044 .022** Step 3 .131 .087** Step 4 .289 .158** Constant 1.30 1 .042 3.67 - - Age -0.071 1 .008 0.93 0.88 0.98 Sent Sext -0.11 1 .666 0.89 0.53 1.46 Received Sext -0.21 1 .492 0.81 0.45 1.47 Pressured Sexting 0.14 .469 1.15 0.79 1.69 Received Sext Unwanted 0.96 1 < .001 2.62 1.18 3.85 Received Unsolicited Airdropped Sext 0.29 1 .389 1.34 0.69 2.61 Non-Consensual Sext Dissemination 1.06 1 .001 2.87 1.53 5.40 Cyberbully Perpetration 2.46 1 < .001 11.65 6.66 20.36 Note. ** p < 0.01 3.2.1 Regression Results for Men Concerning stepwise logistical regression findings for men (N = 795), age was included as a demographic variable at Step 1. The model at Step 1 was significant, χ 2 (1) = 17.33, p < .001, and accounted for 3.1% ( R 2 Nagelkerke = .031) of variance in cyberbullying victimization. At Step 2, unwanted and non-consensual sexting behaviours were added, specifically pressured sexting, receiving unwanted sexts, receiving unsolicited sexts via AirDrop, and non-consensual sext dissemination victimization. The model at Step 2 was significant, χ 2 (5) = 60.87, p < .001, and explained 10.6% ( R 2 Nagelkerke = .106) of variance in cyberbullying victimization. Step 3 included cyberbully perpetration as a predictor variable. The model at step 3 was significant, χ 2 (6) = 241.59, p < .001, and explained 37.7% ( R 2 Nagelkerke = .377) of variance in cyberbullying victimization. Cyberbullying perpetration, Exp(B) = 22.24, p < .001, was the strongest predictor for victimization, followed by receiving unsolicited sexts via AirDrop, Exp(B) = 7.11, p = .009, and pressured sexting, Exp(B) = 1.85, p = .032. Age, receiving unwanted sexts, and being a victim of non-consensual sext dissemination did not significantly predict cyberbullying victimization for men. 3.2.2. Regression Results for Women Reviewing the stepwise logistic regression results for women (N = 887), age was included at Step 1. The model at Step 1 was significant, χ 2 (1) = 14.61, p < .001, and accounted for 2.2% ( R 2 Nagelkerke = .022) of variance in cyberbullying victimization. General sexting behaviours were added at Step 2, including sext sending and receiving behaviours. The model at Step 2 was significant, χ 2 (3) = 28.60, p < .001, accounting for 4.4% ( R 2 Nagelkerke = .044) of variance in cyberbullying victimization. At Step 3, unwanted and non-consensual sexting behaviours including pressured sext sending, receiving unwanted sexts, receiving unsolicited sexts via AirDrop, and non-consensual sext dissemination victimization were included as predictor variables. The model at Step 3 was significant χ 2 (7) = 88.73, p < .001, and accounted for 13.1% ( R 2 Nagelkerke = .131) of variance in cyberbullying victimization. Cyberbullying perpetration was included in Step 4. The model at step 4 was significant, χ 2 (8) = 209.42, p < .001, and explained 28.9% ( R 2 Nagelkerke = .289) of variance in cyberbullying victimization. Cyberbullying perpetration was the strongest predictor of cyberbullying victimization, Exp(B) = 11.65, p < .001, followed by being a victim of non-consensual sext dissemination, Exp(B) = 2.87, p = .001, receiving unwanted sexts, Exp(B) = 2.62, p < .001, and age, Exp(B) = 0.93, p = .008. Sext sending, sext receiving, pressured sexting, and receiving unsolicited sexts via AirDrop were not significant predictors of cyberbullying victimization for women. 4. Discussion Overall, results found significant gender differences in how image-based sexual abuse (IBSA) and cyberbullying predicts cyberbullying victimization. Data for this cohort were collected during periods of COVID-19 confinement (2020–2021), during which many nations imposed nationwide lockdowns. This restriction in travel likely led to a number of differences in online behaviour compared to prior research. Specifically, this study found sext sending and receiving to be more common than previously reported (Mori et al., 2020 ), which is consistent with findings from other studies on sexting during COVID-19 confinement (Bianchi et al., 2021 ; Maes & Vandenbosch, 2022 ; Thomas et al., 2022 ). Further, the present study found non-consensual sext-dissemination to be less common than previously reported in meta-analytic research (Mori et al., 2020 ). Given that the majority of non-consensual sharing of intimate images occurs via physical means (K. Walker et al., 2021 ), Covid limitations on travel may have inadvertently reduced opportunities for non-consensual sext dissemination. In general, women were more likely to experience IBSA victimization compared to men, however, we observed no gender differences in the likelihood of receiving unsolicited sexts via AirDrop. A possible reason is that AirDropped sexts can be distributed by perpetrators without knowing who receives it, as senders and receivers cannot be identified by the name of the device, chosen by the user (Freeman, 2020 ) Therefore, victims are likely being indiscriminately targeted in public areas, thus challenging the predominant female-victim narrative (Freeman, 2020 ; McGlynn & Johnson, 2021 ) . Correlational findings showed that sexting behaviours were associated with cyberbullying victimization for women, though not for men. Given IBSA victimization is more common in women for this cohort, this was expected, as women who are victims of IBSA are more likely to experience cyber-bullying (Gámez-Guadix et al., 2022a ; Ojeda et al., 2019 ). This supports previous research on the role of consent in delineating between healthy and harmful sexting behaviours (Cornelius et al., 2020 ; Klettke et al., 2019 ; Wachs et al., 2021 ). Further, in support of our hypothesis and results from prior research (Gámez‐Guadix et al., 2022a; Ojeda et al., 2019 ), the association between sext sending/receiving and cyberbullying victimization in women was largely accounted for by IBSA victimization. Regarding gender differences in the relationship between IBSA and cyberbullying victimization, as expected, there were substantial differences in how IBSA predicted cyberbullying victimization for men and women. Non-consensual sext dissemination significantly predicted cyberbullying victimization for women, though not for men, which could suggest the presence of double standards when it comes to the socio-cultural expectations around sexual behaviour (Endendijk et al., 2020 ). Women are perceived to experience more reputational damage as victims of non-consensual dissemination, making them more vulnerable to subsequent cyberbullying. As with other forms of harassment, such as slut-shaming, stalking, and intimate-partner abuse (McGlynn et al., 2021 ; Naezer & van Oosterhout, 2021 ; Powell et al., 2020 ), cyberbullying victimization could be a secondary consequence of non-consensual sext dissemination for women. The receiving of unwanted sexts was also significantly associated with cyberbullying victimization for women, though not for men. This was unexpected, given prior research found men who received unwanted sexts experienced greater psychological distress compared to women (Klettke et al., 2019 ). Expectations and associated behaviours surrounding unwanted sexting could explain the gendered association between unwanted sexting and cyberbullying victimization. For example, women are expected to be more receptive to sexual advances (Burkett, 2015 ; Endendijk et al., 2020 ), thus, cyberbullying may occur within the context of unmet expectations when it comes to unwanted sexting. Future studies could explore this possibility by examining gender differences in perpetrator motivations and expectations behind the sending of unwanted sexts. Regarding the association between pressured sexting and cyberbullying victimization, although women are more likely to send sexts under pressure, men who are victims of pressured sexting are more likely experience cyberbullying victimization. This finding suggests that men and women may experience different forms of pressured sexting. Prior research found victims can experience pressures to sext from the recipient, peers, and digital media (Burkett, 2015 ; K. Walker et al., 2021 ; S. Walker et al., 2013 ). Sexualized media culture disproportionality targets and encourages women to expose and share intimate media of themselves (Burkett, 2015 ). Additionally, women experience more social pressure around sexting (K. Walker et al., 2021 ). Consequently, this normalization of exposure could result in a lower level of individual pressure needed to encourage women to sext. In contrast, men could experience greater levels of individual pressure to sext, which may take the form of pernicious behaviour, including extortion, threats, or cyberbullying. Future research should explore gender difference in the ways pressured sexting may occur (e.g., blackmail, extortion, digital media). As previously mentioned, unsolicited sext sharing via AirDrop can be considered an indiscriminate form of IBSA, affecting men and women at similar rates. However, interestingly, we found that receiving unsolicited sexts via AirDrop significantly predicted cyberbullying victimization for men, but not for women. This suggests that while men and women experience similar rates of unsolicited sexts, the impact of these experiences differs across genders. It is possible that exposure to one form of cyber-abuse can inoculate women to other forms of online harassment due to their more commonly endorsed coping strategies. Research into coping strategies utilized in response to cyberbullying (Orel et al., 2017 ; Price & Dalgleish, 2010 ; Ronis & Slaunwhite, 2019 ) found that women were more likely to engage in help-seeking behaviours (e.g., seeking support from family, teachers, or friends), and blocking behaviours (e.g., ignoring or blocking the perpetrator). Men, on the other hand, were more likely to endorse retaliatory behaviour (e.g., encouraging friends to cyberbully the initial perpetrator). Help-seeking behaviours in particular, have been found to be effective in curtailing harassment, while retaliatory behaviour has been found to be ineffective in ameliorating abuse (Fox & Tang, 2017 ; Orel et al., 2017 ; Price & Dalgleish, 2010 ; Ronis & Slaunwhite, 2019 ). Prior experience with cyber-abuse could encourage women to adopt constructive coping-strategies, thus, women who experience cyberbullying victimization are less likely to receive unwanted sexts via AirDrop, and vice versa. In contrast, men may remain susceptible to various forms of online abuse even after initial exposure. As expected, cyberbullying perpetration was significantly associated with cyberbullying victimization for both genders. In line with prior research findings (Dou et al., 2020 ; Hemphill & Heerde, 2014 ; Roberto et al., 2014 ), current research indicate that cyberbullying victims can become perpetrators through retaliatory behaviour or to redirect their feelings from being bullied themselves, while perpetrators may in turn be victimized through retaliation by their victims, Concerning gender differences, the association between cyberbullying and cyberbullying victimization was stronger for men in comparison to women. This was expected, given prior findings that men are more likely than women to endorse retaliatory coping strategies (Orel et al., 2017 ; Ronis & Slaunwhite, 2019 ). Future prevention programs should incorporate education on effective coping strategies and discourage retaliatory behaviour, especially for men, to restrict the cycle of abusive behaviour. 4.1. Limitations Important limitations in the present study should be noted. Given that we utilized a cross-sectional design, caution is encouraged when establishing causation between variables. While non-consensual and unwanted sexting experiences may predispose individuals to becoming victims of cyberbullying, the association may be bidirectional or reciprocal. Future longitudinal research should clarify the temporal order of these associations, and/or explore the possibility of a bidirectional relationship between IBSA and cyberbullying victimization. Additionally, this study used data from an online convenience sample during periods of COVID-19 confinement, thus limiting the generalisability of findings. Further, though anonymity of responses was emphasized to participants, there remains the possibility that socially undesirable behaviours such as cyberbullying and non-consensual sexting behaviours went underreported. Finally, the data utilized in this study was quantitative and offered little efficacy in detailing the nuanced victim experience, such as forms of sexting pressure or the emotional impact of victimization. Future studies may utilize mixed methods or qualitative design (e.g., interviews) to better understand IBSA and cyberbullying victimization. 5. Conclusion The current study investigated gendered differences in the association between cyberbullying, image-based sexual abuse (IBSA), and cyberbullying victimization. We found that the influence of sext sending/receiving on cyberbullying victimization could be largely accounted for by IBSA behaviours. Within these behaviours, being a victim of non-consensual dissemination and unwanted sexting was significantly associated with cyberbullying victimization for women but not for men. Further, being a victim of pressured sexting or receiving unsolicited sexts via Airdrop placed men at a higher risk of being a victim of cyberbullying, while not having the same effect for women. Finally, cyberbullying perpetration was significantly associated with cyberbullying victimization for both genders but had a substantially stronger association for men. Findings from the current study suggests gender interactions between IBSA victim experiences and cyberbullying victimization necessitates a nuanced approach. Notably, there is a significant difference in how men and women are affected by different forms of non-consensual and unwanted sexting behaviours. These differences could be moderated by the social environment in which the behaviour occurred, and coping methods utilized by the victim. Future research should examine how these moderative effects occur, while gender-based prevention programs should acknowledge gendered differences in victim experiences and target IBSA behaviours relevant to their gender. Declarations Funding : No competitive or external grant funding was secured for this project. A small amount of student-project funding was used to reimburse participants via Prolific. Ethics : Ethical approval for this study was provided by a suitably convened Ethics Committee. Please refer to cover letter for details of the ethics approval process (not included here to maintain blinded review process) and we have also provided a copy of the Plain Language Statement and Consent form, which notes that aggregated data could be used for publication. Consent for Publication: All authors consent to the submission of this paper. 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International Journal of Mental Health and Addiction , 17 (5), 1252–1267. https://doi.org/10.1007/s11469-018-9893-9 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2025 Read the published version in International Journal of Bullying Prevention → Version 1 posted Editorial decision: Revision requested 09 Jul, 2025 Reviewers invited by journal 28 May, 2025 Editor assigned by journal 27 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 19 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6703468","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463448536,"identity":"9fca0608-f3f0-475c-995d-c2ed72923f73","order_by":0,"name":"Yunhao Hu","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Yunhao","middleName":"","lastName":"Hu","suffix":""},{"id":463448537,"identity":"ec1854e5-2879-4121-87a4-70686a827920","order_by":1,"name":"Elizabeth Clancy","email":"data:image/png;base64,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","orcid":"","institution":"Deakin University","correspondingAuthor":true,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Clancy","suffix":""},{"id":463448538,"identity":"4ece81d1-6089-47f5-8846-332a72184763","order_by":2,"name":"Bianca Klettke","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Bianca","middleName":"","lastName":"Klettke","suffix":""}],"badges":[],"createdAt":"2025-05-20 04:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6703468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6703468/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42380-025-00329-x","type":"published","date":"2025-10-07T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93419849,"identity":"0339507c-bba3-40da-9213-a7ae9da8ad99","added_by":"auto","created_at":"2025-10-13 16:08:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1095853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703468/v1/02c0e261-16e4-4b3b-a6e1-2d3e6b40b68e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Differences in Image-Based Sexual Abuse and Cyberbullying victimization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInnovations in digital communicative technology have fundamentally changed how people access information and navigate their daily lives, in both positive and negative ways. The personal and relational consequences of online use, as well as how accessibility and anonymity might impact online behaviours have become subjects of great importance. One such behaviour is cyberbullying, defined as an intentional, repeated act of aggression, carried out by a group or individual through an electronic medium, against a victim who cannot easily defend themselves (Smith et al., 2008).\u003c/p\u003e\n\u003cp\u003eFindings regarding prevalence rates for cyberbullying behaviours vary widely depending on definitions and measurements utilized in research. A systematic review on cyberbullying in adults (Jenaro et al., 2018) found that cyberbullying perpetration rates ranged from 0.56% to 54.3%, while cyberbullying victimization rates ranged from 2.38% to 90.86%. Concerningly, victims of cyberbullying have been found to experience greater symptoms of depression and anxiety, shame, guilt, and suicidal ideation (Bottino et al., 2015; Jenaro et al., 2018; Kowalski et al., 2019; Kwan et al., 2020; Medrano et al., 2018). These consequences occur for adolescents and adults and can impact both perpetrators and victims (Bottino et al., 2015; Jenaro et al., 2018). This is perhaps unsurprising, as research has suggested a bi-directional relationship between cyberbullying and cyberbullying victimization (Dou et al., 2020; Hemphill \u0026amp; Heerde, 2014; Hua et al., 2019; Myers \u0026amp; Cowie, 2019; Tanrikulu \u0026amp; Erdur-Baker, 2021; Zsila et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe association between cyberbullying perpetration and cyberbullying victimization offers a potential explanation for the complex nature of cyberbullying behaviours. Victims of cyberbullying are likely to become perpetrators themselves, either through retaliatory behaviour due to anger, or through bullying other victims to redirect their feelings. In turn, perpetrators can become victims as a result of retaliation from their initial targets (Dou et al., 2020; Hemphill \u0026amp; Heerde, 2014; Wang et al., 2019; Zsila et al., 2019). Given the persistence of cyberbullying behaviours and its associated consequences, it is important to better understand the risk factors associated with cyberbullying victimization, improve prevention programs and protect victims of cyberbullying behaviours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne such risk factor may be the experiences of unwanted and non-consensual sexting. as prior research has demonstrated that individuals who are subjected to non-consensual sext dissemination or receive unsolicited sexual content are at an increased risk of cyberbullying victimization due to shared vulnerabilities (Burke \u0026amp; Norvilitis, 2020; G\u0026aacute;mez‐Guadix et al., 2022a; Ojeda et al., 2019; van Ouytsel et al., 2021; Wachs et al., 2021). This underscores the broader implications of sexting, particularly when it occurs without consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.1. Sexting \u0026amp; Image-Based Sexual Abuse\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSexting is a popular form of online sexual interaction among adolescents and adults, and can be defined as the sending, receiving, or forwarding of sexually explicit content to others through electronic means (K. Walker \u0026amp; Sleath, 2017). A meta-analytic study found that 38.3% of young adults sent sexts, 41.5% have received sexts, while 47.7% sent and received sexts reciprocally (Mori et al., 2020). Early research conceptualized sexting as a harmful behaviour, similar to harassment (Diliberto \u0026amp; Mattey, 2009) and as a risk factor for cyberbullying victimization, given that shared intimate material could be distributed to third parties and used for intimidation or blackmail (Dake et al., 2012; G\u0026aacute;mez-Guadix et al., 2015; Reyns et al., 2013; Woodward et al., 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis conceptualization is problematic, placing responsibility for the behaviour on cyberbullying victims engaged in consensual sexting activity, rather than on the perpetrators of non-consensual behaviours (D\u0026ouml;ring, 2014; Henry \u0026amp; Powell, 2015; Krieger, 2017; McGlynn \u0026amp; Rackley, 2017). As such, this conceptualisation encourages victim blaming, minimizes the harm experienced by victims, and downplays perpetrator responsibility (D\u0026ouml;ring, 2014; McGlynn et al., 2021; Naezer \u0026amp; van Oosterhout, 2021; K. Walker \u0026amp; Sleath, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent studies have found that the association between sexting and harmful outcomes such as cyberbullying victimization can be explained by the unwanted and non-consensual subset of sexting behaviours (Burke \u0026amp; Norvilitis, 2020; G\u0026aacute;mez‐Guadix et al., 2022a; Klettke et al., 2019; Ojeda et al., 2019; van Ouytsel et al., 2021; Wachs et al., 2021), otherwise known as image-based sexual abuse (IBSA; DeKeseredy \u0026amp; Schwartz, 2016; McGlynn \u0026amp; Rackley, 2017; Powell \u0026amp; Henry, 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most prominent of IBSA behaviours is non-consensual sext dissemination, defined as the distribution of intimate, nude, and/or sexually explicit images to audiences other than its intended recipient without the consent of those depicted (K. Walker \u0026amp; Sleath, 2017), which has been associated with harmful behaviours such as cybervictimization and intimate partner violence (Cornelius et al., 2020; G\u0026aacute;mez‐Guadix et al., 2022a). Other problematic forms of IBSA include pressured sexting (e.g., coercion, peer pressure), receiving unwanted sexts (e.g., cyberflashing), and receiving anonymous unsolicited sexts (e.g., via AirDrop). Victims of IBSA have been found to experience negative health consequences similar to those experienced by victims of cyberbullying, including depression, self-harm, and the loss of self-esteem (Bates, 2017; Burke \u0026amp; Norvilitis, 2020; McGlynn et al., 2021; Valiukas et al., 2019; Wachs et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.2. Gender Differences in Cyberbullying victimization and IBSA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhile research into IBSA and cyberbullying victimization is growing (G\u0026aacute;mez‐Guadix et al., 2022b; Henry \u0026amp; Beard, 2024; Pedersen et al., 2023), there is little understanding surrounding gender differences in the relationship between IBSA and cyberbullying victimization. Cyberbullying and IBSA victimization have been found to impact men and women differently. Meta-analyses suggest men are more likely to engage in cyberbullying perpetration, while women are more likely to experience cyberbullying\u0026nbsp;victimization (Guo, 2016; Sun et al., 2016).This is surprising given the strong bidirectionality in the bully-victim relationship, suggesting the presence of additional factors placing women at greater risk of experiencing cyberbullying\u0026nbsp;victimization. Moreover, studies found that women experienced greater levels of emotional distress (e.g., depressive symptoms) in response to cyberbullying victimisation compared to men (Brown et al., 2014; Foody et al., 2019).\u003c/p\u003e\n\u003cp\u003ePrevious findings regarding gender differences in overall sext sending/receiving behaviours are varied; some meta-analytic research found that men were more likely to both send and receive sexts (Mori et al., 2020), while others found no significant gender differences (Madigan et al., 2018; Molla-Esparza et al., 2020). Concerning IBSA victimization, research found that the likelihood of becoming a victim of non-consensual sext dissemination did not differ significantly across genders (Clancy et al., 2020; Madigan et al., 2018; Mussap et al., 2023; Powell et al., 2020; Sciacca et al., 2023; K. Walker et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, consequences of victimization were found to be more severe for women than men. For example, women who had their sexts shared without consent were more likely to be confronted with social stigmatization and blamed for the initial creation or sharing of sexts (McGlynn et al., 2021; Naezer \u0026amp; van Oosterhout, 2021; K. Walker \u0026amp; Sleath, 2017). Women were also more likely to experience non-consensual sext dissemination in the context of interpersonal harm, such as stalking, sexual assault, and intimate partner abuse (McGlynn et al., 2021; Powell et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, studies have shown that women are more likely to experience pressured-sexting and receive unwanted sexts (Klettke et al., 2019; van Ouytsel et al., 2017; Wachs et al., 2021; K. Walker et al., 2021; K. Walker \u0026amp; Sleath, 2017). Gender differences were also observed in the impact of unwanted and pressured sexting, with men found to experience more negative mental health outcomes as a result of receiving unwanted sexts (Klettke et al., 2019), while women experienced greater emotional distress as a result of pressured sexting (Wachs et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, to our knowledge, no empirical research currently exists on the receiving of unsolicited sexts via AirDrop. It is important to consider the experiences of victims who receive unsolicited sexts via AirDrop, given that sexts can be anonymously exchanged through AirDrop only if the devices are physically close (i.e. within 30 feet, (Freeman, 2020), indicating a level of proximity between the perpetrator and victim. Knowing a perpetrator is physically close but being unable to identify them could be an aggravating factor for victims, thus a better understanding of proximate IBSA victimization is warranted. One study on cyber-flashing law reform places receiving unsolicited sexts via AirDrop as a gendered experience, with men acting primarily as perpetrators and women as victims (McGlynn \u0026amp; Johnson, 2021). Given that victimization in cyberbullying and IBSA instances are gendered, associations between the two phenomena may likewise differ between genders. A better understanding of these differences would assist with the development of future gender-based cyberbullying prevention programs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.3. Current Research\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe objective of this study was to examine cisgendered differences in predictors for cyberbullying victimization. Based on previous literature, we expect sext sending and receiving to be significantly associated with cyberbullying victimization for both men and women (Hypothesis 1), however that association would be better accounted for by IBSA victimization (Hypothesis 2). Concerning gender differences, based on prior findings surrounding the consequences of IBSA, we expected being a victim of non-consensual sext dissemination and pressured sexting and would be more strongly associated with cyberbullying victimization for women, while receiving unwanted sexts would have a greater association with cyberbullying victimization for men (Hypothesis 3). Due to the paucity of research exploring the receiving of unsolicited sexts via AirDrop, no a priori hypothesis was developed. Finally, given that prior research found men were more likely to exhibit cyberbullying behaviour as a result of cyberbullying victimization (Cunningham et al., 2015; Hemphill \u0026amp; Heerde, 2014; Orel et al., 2017; Zsila et al., 2019), we hypothesised that cyberbullying perpetration would have a stronger association with cyberbullying victimization for men compared to women (Hypothesis 4).\u0026nbsp;\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eAn initial 2828 participants commenced the survey. After removal of incomplete responses (\u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1012), those who were outside age inclusion criteria (\u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;101), and gender diverse participants due to insufficient sample size (\u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;33) and the study\u0026rsquo;s focus on cis-gendered adults, this resulted in a final sample of 1682 young adults (47.3% \u003cem\u003emen\u003c/em\u003e, 52.7% \u003cem\u003ewomen\u003c/em\u003e). Eligible participants were 18\u0026ndash;30 years old, with a mean age of 23.15 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.23). Most participants reported being sexually active (76.4%) with the average age of sexual activity being 17.32 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4). Regarding sexual orientation, 75.6% identified themselves as being Heterosexual, 14.9% as Bisexual, 4.3% as Lesbian/Gay, 2.9% as Uncertain, and 2.3% as Other or unwilling to disclose. Concerning educational attainment, 37.7% achieved a Bachelor\u0026rsquo;s degree, 30.8% Year 12 or equivalent, 13.5% Postgraduate Degree, 6.6% Certificate level, and 6.1% Advanced Diploma/Diploma. Most participants were from Australia (77.2%), followed by United Kingdom (9.9%), USA (5.0%), Other Country (5.0%), and New Zealand (2.9%). Concerning ethnicity, 48.9% were Australian, 20.8% were British or European, 15.6% were Asian, and 14.7% endorsed another ethnicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Procedure\u003c/h2\u003e \u003cp\u003e This study was approved by [Blinded for Review] Ethics Committee. Before participants commenced the voluntary, confidential survey, general study aims were outlined via a plain language statement. Participants provided informed consent before they commenced the survey, which took approximately 20\u0026ndash;25 minutes to complete. Overall, 1204 participants (71.6%) were recruited via social media, while 478 participants (28.4%) were recruited through survey aggregator Prolific to produce a more gender balanced sample. No incentive was offered for social media participants while Prolific participants received a minor financial reimbursement.\u003c/p\u003e \u003cp\u003eProlific participants were, on average, older than social media participants (\u003cem\u003eProlific M\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.3, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.54, \u003cem\u003esocial media M\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.70, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.98; \u003cem\u003et\u003c/em\u003e (1680)\u0026thinsp;=\u0026thinsp;9.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), more likely to be men (\u003cem\u003eProlific\u003c/em\u003e 95.4%, \u003cem\u003esocial media\u003c/em\u003e 28.2%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;620.67, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), and engage in cyberbullying (\u003cem\u003eProlific\u003c/em\u003e 39.5%, \u003cem\u003esocial media\u003c/em\u003e 31.4%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.16, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001). Social media participants were more likely to be sexually active (\u003cem\u003eProlific\u003c/em\u003e 68.1%, \u003cem\u003esocial media\u003c/em\u003e 79.7%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25.12, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), send sexts (\u003cem\u003eProlific\u003c/em\u003e 52.1%, \u003cem\u003esocial media\u003c/em\u003e 74.7%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;80.48, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), receive sexts (\u003cem\u003eProlific\u003c/em\u003e 71.8%, \u003cem\u003esocial media\u003c/em\u003e 84.3%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34.62, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), received unwanted sexts (\u003cem\u003eProlific\u003c/em\u003e 19.2%, \u003cem\u003esocial media\u003c/em\u003e 50.8%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;140.25, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), send sexts under pressure (\u003cem\u003eProlific\u003c/em\u003e 16.7%, \u003cem\u003esocial media\u003c/em\u003e 38.0%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70.89, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), and be victims of non-consensual sext dissemination (\u003cem\u003eProlific\u003c/em\u003e 5.6%, \u003cem\u003esocial media\u003c/em\u003e 11.5%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.37, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001). There was no significant difference between Prolific and social media participants concerning cyberbullying victimization (\u003cem\u003eProlific\u003c/em\u003e 69.2%, \u003cem\u003esocial media\u003c/em\u003e 67.4%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.45), and receiving unsolicited sexts via AirDrop (\u003cem\u003eProlific\u003c/em\u003e 6.7%, \u003cem\u003esocial media\u003c/em\u003e 7.4%, \u003cem\u003eχ2(1)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.62).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measures\u003c/h2\u003e \u003cp\u003eThe Internet Harassment Survey(Ybarra et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) includes questions on how many times a participant experienced cyberbullying perpetration or victimization in the past year. The questionnaire consisted of three items on cyberbullying victimization (e.g., \u003cem\u003eReceive rude or nasty comments from someone while online\u003c/em\u003e) and three items on cyberbullying (e.g., \u003cem\u003eSpread rumours about someone online\u003c/em\u003e). Items were rated on a frequency scale: \u003cem\u003eeveryday/almost every day; once or twice a week; once or twice a month; a few times a year; less than a few times a year; neve\u003c/em\u003er. Participants who reported experiencing cyberbullying perpetration or victimization in the past year were coded as being perpetrators or victim respectively. In this sample, the Cronbach\u0026rsquo;s alpha was 0.78 for victimization and 0.75 for perpetration, consistent with prior validation efforts (Ybarra et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Sexting Behaviours Questionnaire was used to explore sexting behaviours among young adults (Clancy et al., 2021). For the purposes of this study, sexts were defined as \u0026ldquo;sexually explicit images, sent, received or shared via mobile phone messaging or apps.\u0026rdquo; Participants were asked whether they had experienced or engaged in the following behaviours (\u003cem\u003eYes/No\u003c/em\u003e): sent sexts \u003cem\u003e\u0026ldquo;Have you ever sent an image-based sext of yourself?\u0026rdquo;\u003c/em\u003e; received sexts \u003cem\u003e\u0026ldquo;Have you ever received an image-based sext\u0026rdquo;\u003c/em\u003e; received unwanted sexts \u003cem\u003e\u0026ldquo;Have you ever received an image-based sext that was unwanted/unwelcome?\u0026rdquo;\u003c/em\u003e; pressured sexting \u003cem\u003e\u0026ldquo;Have you felt pressure to send an image-based sext? If yes, as a result of that pressure, did you send an image-based sext?\u0026rdquo;\u003c/em\u003e; received unsolicited sexts via AirDrop \u003cem\u003e\u0026ldquo;Have you ever been airdropped a sexually explicit image without your consent?\u0026rdquo;\u003c/em\u003e; and non-consensual sext dissemination victimization \u003cem\u003e\u0026ldquo;Have you ever sent an image-based sext of yourself that was subsequently forwarded (to your knowledge)? Had you given permission for this image to be forwarded?\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis\u003c/h2\u003e \u003cp\u003eIBM SPSS Statistics 28.0 was used to conduct relevant statistical analyses for the study. Descriptive and chi-square analysis enabled review of sample characteristics by gender. Bivariate correlational analysis tested the association between measured variables for men and women. Only variables significantly correlated with cyberbullying victimization were included in subsequent stepwise logistical regressions to examine gender differences in associations.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eDescriptive statistics for study variables examined by gender are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Men were significantly more likely to engage in cyberbullying perpetration and experience cyberbullying victimization, while women were significantly more likely to send and receive sexts, sext under pressure, receive unwanted sexts, and be victims of non-consensual sext dissemination. Men and women did not significantly differ in regard to receiving unsolicited sexts via AirDrop.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eKey Variables by Gender\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull Sample (N\u0026thinsp;=\u0026thinsp;1682)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;795)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;887)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComparison, Men to Women\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSent Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;44.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;15.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured Sexting *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;77.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Unwanted Sext **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;290.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirdropped Sext Without Consent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;0.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Consensual Dissemination Victim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;12.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbullying Perpetrator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;49.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbullying Victim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;11.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: * = % provided based on those who sent sexts (N\u0026thinsp;=\u0026thinsp;1148), ** = % provided based on those who received sexts (N\u0026thinsp;=\u0026thinsp;1358).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Correlations\u003c/h2\u003e \u003cp\u003eBivariate correlations were conducted separately for men and women. Correlation analyses for men found cyberbullying victimization to be significantly associated with all variables other than sext sending/receiving, detailed findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In contrast, correlation analysis for women found all predictor variables to be associated with cyberbullying victimization, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations for Key Variables by Gender (Men)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSent Sext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReceived Sext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePressured Sexting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReceived Unwanted Sext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAirdropped Sext Without Consent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNon-Consensual Dissemination Victim\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCyberbullying Perpetrator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCyberbully Victim\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSent Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.642**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured Sexting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.393**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.267**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Unwanted Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.199**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.276**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.213**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirdropped Sext Without Consent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.073*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.076*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.168**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Consensual Dissemination Victim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.226**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.153**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.325**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.213**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbullying Perpetrator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.177**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.138**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.079*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.118**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.108**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbully Victim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.148**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.152**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.111**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.144**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.098**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.472**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations for Key Variables by Gender (Women)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSent Sext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReceived Sext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePressured Sexting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReceived Unwanted Sext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAirdropped Sext Without Consent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNon-Consensual Dissemination Victim\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCyberbullying Perpetrator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCyberbully Victim\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSent Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.618**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured Sexting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.106**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.501**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.353**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Unwanted Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.101**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.262**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.543**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.351**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirdropped Sext Without Consent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.063*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.081*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Consensual Dissemination Victim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.198**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.152**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.274**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.220**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.137**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbullying Perpetrator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.075*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.112**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.100**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.137**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.112*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.090**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.114**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbully Victim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.129**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.082*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.132**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.161**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.253**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.082*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.179**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.362**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Stepwise Logistic Regression\u003c/h2\u003e \u003cp\u003eSeparate stepwise logistic regressions were conducted for men (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and women (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with cyberbullying victimization as the dependent variable. Predictor variables included age, sext-sending, sexts-receiving, pressured sexting, receiving unwanted sexts, receiving unsolicited sexts via AirDrop, non-consensual sext dissemination victimization, and cyberbullying. Only variables significantly associated with cyberbullying victimization in prior bivariate correlations were included in each regression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eStepwise Logistic Regression Results Regarding Cyberbullying victimization (Men)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eNagelkerke\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eChange\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.031**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.075**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.271**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured Sexting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Unwanted Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Unsolicited Airdropped Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Consensual Sext Dissemination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbully Perpetration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eStepwise Logistic Regression Results Regarding Cyberbully Victimization (Women)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eNagelkerke\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eChange\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.022**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.022**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.087**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.158**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSent Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured Sexting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Sext Unwanted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Unsolicited Airdropped Sext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Consensual Sext Dissemination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyberbully Perpetration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Regression Results for Men\u003c/h2\u003e \u003cp\u003eConcerning stepwise logistical regression findings for men (N\u0026thinsp;=\u0026thinsp;795), age was included as a demographic variable at Step 1. The model at Step 1 was significant, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(1)\u0026thinsp;=\u003c/em\u003e\u0026thinsp;17.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and accounted for 3.1% (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;.031)\u003c/em\u003e of variance in cyberbullying victimization. At Step 2, unwanted and non-consensual sexting behaviours were added, specifically pressured sexting, receiving unwanted sexts, receiving unsolicited sexts via AirDrop, and non-consensual sext dissemination victimization. The model at Step 2 was significant, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(5)\u0026thinsp;=\u003c/em\u003e\u0026thinsp;60.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and explained 10.6% (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;.106)\u003c/em\u003e of variance in cyberbullying victimization. Step 3 included cyberbully perpetration as a predictor variable. The model at step 3 was significant, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(6)\u0026thinsp;=\u003c/em\u003e\u0026thinsp;241.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and explained 37.7% (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;.377)\u003c/em\u003e of variance in cyberbullying victimization. Cyberbullying perpetration, Exp(B)\u0026thinsp;=\u0026thinsp;22.24, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, was the strongest predictor for victimization, followed by receiving unsolicited sexts via AirDrop, \u003cem\u003eExp(B)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009, and pressured sexting, \u003cem\u003eExp(B)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.032. Age, receiving unwanted sexts, and being a victim of non-consensual sext dissemination did not significantly predict cyberbullying victimization for men.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Regression Results for Women\u003c/h2\u003e \u003cp\u003eReviewing the stepwise logistic regression results for women (N\u0026thinsp;=\u0026thinsp;887), age was included at Step 1. The model at Step 1 was significant, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(1)\u0026thinsp;=\u003c/em\u003e\u0026thinsp;14.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and accounted for 2.2% (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;.022)\u003c/em\u003e of variance in cyberbullying victimization. General sexting behaviours were added at Step 2, including sext sending and receiving behaviours. The model at Step 2 was significant, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(3)\u0026thinsp;=\u003c/em\u003e\u0026thinsp;28.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, accounting for 4.4% (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;.044)\u003c/em\u003e of variance in cyberbullying victimization. At Step 3, unwanted and non-consensual sexting behaviours including pressured sext sending, receiving unwanted sexts, receiving unsolicited sexts via AirDrop, and non-consensual sext dissemination victimization were included as predictor variables. The model at Step 3 was significant \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(7)\u0026thinsp;=\u003c/em\u003e\u0026thinsp;88.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and accounted for 13.1% (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;.131)\u003c/em\u003e of variance in cyberbullying victimization. Cyberbullying perpetration was included in Step 4. The model at step 4 was significant, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(8)\u0026thinsp;=\u003c/em\u003e\u0026thinsp;209.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and explained 28.9% (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;.289)\u003c/em\u003e of variance in cyberbullying victimization. Cyberbullying perpetration was the strongest predictor of cyberbullying victimization, \u003cem\u003eExp(B)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, followed by being a victim of non-consensual sext dissemination, \u003cem\u003eExp(B)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001, receiving unwanted sexts, \u003cem\u003eExp(B)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and age, \u003cem\u003eExp(B)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008. Sext sending, sext receiving, pressured sexting, and receiving unsolicited sexts via AirDrop were not significant predictors of cyberbullying victimization for women.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOverall, results found significant gender differences in how image-based sexual abuse (IBSA) and cyberbullying predicts cyberbullying victimization. Data for this cohort were collected during periods of COVID-19 confinement (2020\u0026ndash;2021), during which many nations imposed nationwide lockdowns. This restriction in travel likely led to a number of differences in online behaviour compared to prior research. Specifically, this study found sext sending and receiving to be more common than previously reported (Mori et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is consistent with findings from other studies on sexting during COVID-19 confinement (Bianchi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maes \u0026amp; Vandenbosch, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further, the present study found non-consensual sext-dissemination to be less common than previously reported in meta-analytic research (Mori et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given that the majority of non-consensual sharing of intimate images occurs via physical means (K. Walker et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Covid limitations on travel may have inadvertently reduced opportunities for non-consensual sext dissemination.\u003c/p\u003e \u003cp\u003eIn general, women were more likely to experience IBSA victimization compared to men, however, we observed no gender differences in the likelihood of receiving unsolicited sexts via AirDrop. A possible reason is that AirDropped sexts can be distributed by perpetrators without knowing who receives it, as senders and receivers cannot be identified by the name of the device, chosen by the user (Freeman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Therefore, victims are likely being indiscriminately targeted in public areas, thus challenging the predominant female-victim narrative (Freeman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McGlynn \u0026amp; Johnson, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eCorrelational findings showed that sexting behaviours were associated with cyberbullying victimization for women, though not for men. Given IBSA victimization is more common in women for this cohort, this was expected, as women who are victims of IBSA are more likely to experience cyber-bullying (G\u0026aacute;mez-Guadix et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Ojeda et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This supports previous research on the role of consent in delineating between healthy and harmful sexting behaviours (Cornelius et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Klettke et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wachs et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further, in support of our hypothesis and results from prior research (G\u0026aacute;mez‐Guadix et al., 2022a; Ojeda et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the association between sext sending/receiving and cyberbullying victimization in women was largely accounted for by IBSA victimization.\u003c/p\u003e \u003cp\u003eRegarding gender differences in the relationship between IBSA and cyberbullying victimization, as expected, there were substantial differences in how IBSA predicted cyberbullying victimization for men and women. Non-consensual sext dissemination significantly predicted cyberbullying victimization for women, though not for men, which could suggest the presence of double standards when it comes to the socio-cultural expectations around sexual behaviour (Endendijk et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Women are perceived to experience more reputational damage as victims of non-consensual dissemination, making them more vulnerable to subsequent cyberbullying. As with other forms of harassment, such as slut-shaming, stalking, and intimate-partner abuse (McGlynn et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Naezer \u0026amp; van Oosterhout, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Powell et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), cyberbullying victimization could be a secondary consequence of non-consensual sext dissemination for women.\u003c/p\u003e \u003cp\u003eThe receiving of unwanted sexts was also significantly associated with cyberbullying victimization for women, though not for men. This was unexpected, given prior research found men who received unwanted sexts experienced greater psychological distress compared to women (Klettke et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Expectations and associated behaviours surrounding unwanted sexting could explain the gendered association between unwanted sexting and cyberbullying victimization. For example, women are expected to be more receptive to sexual advances (Burkett, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Endendijk et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thus, cyberbullying may occur within the context of unmet expectations when it comes to unwanted sexting. Future studies could explore this possibility by examining gender differences in perpetrator motivations and expectations behind the sending of unwanted sexts.\u003c/p\u003e \u003cp\u003eRegarding the association between pressured sexting and cyberbullying victimization, although women are more likely to send sexts under pressure, men who are victims of pressured sexting are more likely experience cyberbullying victimization. This finding suggests that men and women may experience different forms of pressured sexting. Prior research found victims can experience pressures to sext from the recipient, peers, and digital media (Burkett, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; K. Walker et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; S. Walker et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Sexualized media culture disproportionality targets and encourages women to expose and share intimate media of themselves (Burkett, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, women experience more social pressure around sexting (K. Walker et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, this normalization of exposure could result in a lower level of individual pressure needed to encourage women to sext. In contrast, men could experience greater levels of individual pressure to sext, which may take the form of pernicious behaviour, including extortion, threats, or cyberbullying. Future research should explore gender difference in the ways pressured sexting may occur (e.g., blackmail, extortion, digital media).\u003c/p\u003e \u003cp\u003eAs previously mentioned, unsolicited sext sharing via AirDrop can be considered an indiscriminate form of IBSA, affecting men and women at similar rates. However, interestingly, we found that receiving unsolicited sexts via AirDrop significantly predicted cyberbullying victimization for men, but not for women. This suggests that while men and women experience similar rates of unsolicited sexts, the impact of these experiences differs across genders. It is possible that exposure to one form of cyber-abuse can inoculate women to other forms of online harassment due to their more commonly endorsed coping strategies. Research into coping strategies utilized in response to cyberbullying (Orel et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Price \u0026amp; Dalgleish, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ronis \u0026amp; Slaunwhite, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that women were more likely to engage in help-seeking behaviours (e.g., seeking support from family, teachers, or friends), and blocking behaviours (e.g., ignoring or blocking the perpetrator). Men, on the other hand, were more likely to endorse retaliatory behaviour (e.g., encouraging friends to cyberbully the initial perpetrator). Help-seeking behaviours in particular, have been found to be effective in curtailing harassment, while retaliatory behaviour has been found to be ineffective in ameliorating abuse (Fox \u0026amp; Tang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Orel et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Price \u0026amp; Dalgleish, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ronis \u0026amp; Slaunwhite, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Prior experience with cyber-abuse could encourage women to adopt constructive coping-strategies, thus, women who experience cyberbullying victimization are less likely to receive unwanted sexts via AirDrop, and vice versa. In contrast, men may remain susceptible to various forms of online abuse even after initial exposure.\u003c/p\u003e \u003cp\u003eAs expected, cyberbullying perpetration was significantly associated with cyberbullying victimization for both genders. In line with prior research findings (Dou et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hemphill \u0026amp; Heerde, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Roberto et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), current research indicate that cyberbullying victims can become perpetrators through retaliatory behaviour or to redirect their feelings from being bullied themselves, while perpetrators may in turn be victimized through retaliation by their victims, Concerning gender differences, the association between cyberbullying and cyberbullying victimization was stronger for men in comparison to women. This was expected, given prior findings that men are more likely than women to endorse retaliatory coping strategies (Orel et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ronis \u0026amp; Slaunwhite, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Future prevention programs should incorporate education on effective coping strategies and discourage retaliatory behaviour, especially for men, to restrict the cycle of abusive behaviour.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Limitations\u003c/h2\u003e \u003cp\u003eImportant limitations in the present study should be noted. Given that we utilized a cross-sectional design, caution is encouraged when establishing causation between variables. While non-consensual and unwanted sexting experiences may predispose individuals to becoming victims of cyberbullying, the association may be bidirectional or reciprocal. Future longitudinal research should clarify the temporal order of these associations, and/or explore the possibility of a bidirectional relationship between IBSA and cyberbullying victimization. Additionally, this study used data from an online convenience sample during periods of COVID-19 confinement, thus limiting the generalisability of findings. Further, though anonymity of responses was emphasized to participants, there remains the possibility that socially undesirable behaviours such as cyberbullying and non-consensual sexting behaviours went underreported. Finally, the data utilized in this study was quantitative and offered little efficacy in detailing the nuanced victim experience, such as forms of sexting pressure or the emotional impact of victimization. Future studies may utilize mixed methods or qualitative design (e.g., interviews) to better understand IBSA and cyberbullying victimization.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe current study investigated gendered differences in the association between cyberbullying, image-based sexual abuse (IBSA), and cyberbullying victimization. We found that the influence of sext sending/receiving on cyberbullying victimization could be largely accounted for by IBSA behaviours. Within these behaviours, being a victim of non-consensual dissemination and unwanted sexting was significantly associated with cyberbullying victimization for women but not for men. Further, being a victim of pressured sexting or receiving unsolicited sexts via Airdrop placed men at a higher risk of being a victim of cyberbullying, while not having the same effect for women. Finally, cyberbullying perpetration was significantly associated with cyberbullying victimization for both genders but had a substantially stronger association for men. Findings from the current study suggests gender interactions between IBSA victim experiences and cyberbullying victimization necessitates a nuanced approach. Notably, there is a significant difference in how men and women are affected by different forms of non-consensual and unwanted sexting behaviours. These differences could be moderated by the social environment in which the behaviour occurred, and coping methods utilized by the victim. Future research should examine how these moderative effects occur, while gender-based prevention programs should acknowledge gendered differences in victim experiences and target IBSA behaviours relevant to their gender.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: No competitive or external grant funding was secured for this project. A small amount of student-project funding was used to reimburse participants via Prolific.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e: Ethical approval for this study was provided by a suitably convened Ethics Committee. Please refer to cover letter for details of the ethics approval process (not included here to maintain blinded review process) and we have also provided a copy of the Plain Language Statement and Consent form, which notes that aggregated data could be used for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eAll authors consent to the submission of this paper. Consent was provided by participants as noted in the PLS referenced above both for data collection and publication of aggregated results arising from the publication.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBates, S. (2017). 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Gender Differences in the Association Between Cyberbullying Victimization and Perpetration: The Role of Anger Rumination and Traditional Bullying Experiences. \u003cem\u003eInternational Journal of Mental Health and Addiction\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(5), 1252\u0026ndash;1267. https://doi.org/10.1007/s11469-018-9893-9\u003c/li\u003e\n\u003c/ol\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-bullying-prevention","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbp","sideBox":"Learn more about [International Journal of Bullying Prevention](https://rd.springer.com/journal/42380)","snPcode":"42380","submissionUrl":"https://submission.springernature.com/new-submission/42380/3","title":"International Journal of Bullying Prevention","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6703468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6703468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHarmful experiences such as cyberbullying victimization have been associated with the unwanted and non-consensual subset of sexting behaviours known as image-based sexual abuse (IBSA). However, there is little understanding surrounding gender differences in that association. The present study contributes to that understanding through examining gender differences in relationships between IBSA, cyberbullying perpetration, and cyberbullying victimization. Study participants consisted of 1683 young cisgendered adults (\u003cem\u003eM age\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.15, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.23, 52.7% women) who completed an anonymous online survey on sexting and harmful online behaviours. Associations between sext sending/receiving and cyberbullying victimization could be largely accounted for by IBSA victimization, but with unique gendered patterns. Specifically, being a victim of non-consensual sext dissemination and receiving unwanted sexts was found to be associated with cyberbullying victimization for women but not for men. Conversely, pressured sexting and receiving unsolicited sexts via AirDrop was associated with cyberbullying victimization for men, but not women. Finally, cyberbullying perpetration predicted cyberbullying victimization for both genders, but had a stronger association for men, suggesting that the impact of IBSA is gendered and nuanced. Future research should explore the social environments in which IBSA victimization occurs and broader gendered behaviours.\u003c/p\u003e","manuscriptTitle":"Gender Differences in Image-Based Sexual Abuse and Cyberbullying victimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 12:01:08","doi":"10.21203/rs.3.rs-6703468/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-09T15:40:06+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T15:27:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-27T23:33:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T23:30:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Bullying Prevention","date":"2025-05-20T03:58:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-bullying-prevention","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbp","sideBox":"Learn more about [International Journal of Bullying Prevention](https://rd.springer.com/journal/42380)","snPcode":"42380","submissionUrl":"https://submission.springernature.com/new-submission/42380/3","title":"International Journal of Bullying Prevention","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cd3dd453-c3a7-4b15-a41e-cc8d4f25bfe8","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:04:33+00:00","versionOfRecord":{"articleIdentity":"rs-6703468","link":"https://doi.org/10.1007/s42380-025-00329-x","journal":{"identity":"international-journal-of-bullying-prevention","isVorOnly":false,"title":"International Journal of Bullying Prevention"},"publishedOn":"2025-10-07 15:58:02","publishedOnDateReadable":"October 7th, 2025"},"versionCreatedAt":"2025-05-30 12:01:08","video":"","vorDoi":"10.1007/s42380-025-00329-x","vorDoiUrl":"https://doi.org/10.1007/s42380-025-00329-x","workflowStages":[]},"version":"v1","identity":"rs-6703468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6703468","identity":"rs-6703468","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}