The Journal of School Nursing2023, Vol. 39(6) 463–474© The Author(s) 2021Article reuse guidelines:sagepub.com/journals-permissionsDOI:10.1177/10598405211038235journals.sagepub.com/home/jsn
Youth Risk Behavior Survey 2011–2017 data were examined for associations among high school population subsets who self-reported suicide risk behaviors and experiences with bullying. High-school students who reported suicidal risk behaviors were 4.64 times more likely to have experienced bullying electronically. Ninth grade and female students were more likely than others to experience suicide risk behaviors and bullying. At the interpersonal level, school nurses are able to identify students who are experiencing bullying and who exhibit suicide risk behaviors. At the systems level, bullying prevention efforts should target all students. School nurses, administrators, policy makers, and health providers should consider datadriven recommendations in bullying prevention programs.
KeywordsAdolescents, suicide, high school, school nursing, population health, bullying, Youth Risk Behavior Survey
Bullying among the adolescent population is a public health crisis. High school students across the United States experience victimization by bullying at consistently alarming rates. According to the Youth Risk Behavior Survey (YRBS) in 2017, 20% of students in Grades 9 through 12 reported experiencing bullying (Kann et al., 2018). Key factors of bullying are an imbalance of power between the victim and perpetrator, intent to harm, and repetition (Olweus, 1994). Bullying occurs in multiple contexts related to school, such as at school events, or on the internet (Gladden et al., 2014). Forms of bullying are conceptualized as direct or indirect behaviors toward the victim (Olweus, 1994, 2013). Direct forms of bullying include aggressive behaviors that occur in the presence of the victim, including physical force and harmful communication targeting the victim (Gladden et al., 2014; Olweus, 2013). Indirect bullying isolates the victim through rumors or social exclusion (Gladden et al., 2014). Cyberbullying, often perpetrated through email or social media, may be viewed as indirect bullying (Gladden et al., 2014; Olweus & Limber, 2018).
Positive associations exist between individual adolescent demographic characteristics and bullying victimization (Barboza et al., 2009). Students are bullied because they are perceived by peers to differ in some way from the peer group (Olweus, 1978). From 2009 to 2017, significantly more White females experienced bullying on school property compared to males and other races in Grades 9 through 12 (Centers for Disease Control and Prevention [CDC], 2018b; Kann et al., 2018). Experiencing bullying can result in internalized behaviors such as withdrawal from interactions and depressive symptoms (Cook et al., 2010). In a study of students (n = 2,670, mean age 14.5 years, 49% female), a positive association was found between suicidal ideation and experiencing bullying (Hinduja & Patchin, 2019). Adolescents who experienced school-based or online bullying were more likely to report suicide ideation. The results of these studies demonstrate potentially harmful consequences of being bullied by peers, including suicidal ideation. Suicide risk behaviors include feeling sad or hopeless for longer than two weeks; having considered, planned, or attempted suicide (CDC, 2017b). From 2009 to 2018, suicide rates among youth ages 14–18 years increased nearly 62% per 100,000 and was the second leading cause of death among this age group (Ivey-Stephenson et al., 2020).
To address these concerns, Healthy People 2020 targeted a 10% reduction in bullying on school property among all adolescents in Grades 9 through 12 by 2020 (U.S. Department of Health and Human Services [USDHS], 2018). However, researchers did not find a reduction in cyberbullying or bullying at school in a study of YRBS surveys conducted between 2011 and 2019 (Li et al., 2020). The Centers for Disease Control and Prevention (CDC) added questions to the YRBS in 2015 to assess sexual orientation (CDC, 2016b). The Healthy People 2030 health plan targets a reduction of bullying of Lesbian, Bisexual, Gay, Transsexual, and Questioning students from 33.0% to 25.1% (USDHS, 2020a), and reduction in adolescent suicide from 2.4 to 1.8 per 100 (USDHS, 2020b).
However, the direction of associations between adolescent demographic characteristics, suicide risk behaviors, and experiences of bullying among all students in Grades 9 through 12 is unknown. The purpose of this study is to examine associations between demographic characteristics of adolescents, suicide risk behaviors, and their experiences of bullying from 2011 to 2017. This study aims to identify associations between adolescent grade, sex, race/ethnicity, suicide risk behaviors, and their experiences of being bullied from 2011 to 2017. The research questions are: Is there an association between adolescent demographic characteristics and the prevalence of experiencing bullying? Is there an association between suicide risk behaviors and the prevalence of experiencing bullying?
Bullying occurs within a social context, influenced by individual characteristics of the child and contextual characteristics of the setting (Cook et al., 2010). The social ecological model was adapted to describe the influence of relationships in the environment on child development (Barboza et al., 2009; Bronfenbrenner, 1994; Espelage, 2014). At the innermost circle of the model is the physical and emotionally developing child (Eriksson et al., 2018). This is the microsystem of intrapersonal relationships from where the child experiences their environment (Bronfenbrenner, 1977, 1994). The psychology of the child resides in the microsystem. Microsystem predictors of bullying are the individual demographic characteristics of age, sex, and race (Espelage, 2014), and psychological characteristics identifying suicide risk behaviors. Adolescent characteristics and the context of the environment can predict bullying behaviors and victimization (Cook et al., 2010). The microsystem is nested within the mesosystem (Barboza et al., 2009; Bronfenbrenner, 1994), where interpersonal relations of the microsystems link with settings containing the child (Bronfenbrenner, 1994). Peer groups and schools are mesosystem factors in the environment that can promote or prevent bullying victimization (Bronfenbrenner, 1994; Espelage, 2014). The model is presented in Appendix A.
A review of the literature was conducted within the framework of the social ecological model, beginning with adolescent demographic characteristics. How male and female youths perceive bullying varied by grade in a study by Hellström et al. (2015). The investigators examined students in Grades 7 and 9 (n = 149) and found that more ninth graders than seventh graders identified being bullied as being ignored and not being allowed to join activities with friends. Among ninth graders, more females than males reported repeatedly writing mean messages on Facebook as an example of bullying behavior. Females in ninth grade also reported mean text messages and calling people mean names as bullying. Males and females experienced bullying in these age groups. However, in a study of students (n = 2,295) between 12 and 16 years of age, females were more likely to be victims, and males were more likely to bully (Cuadrado-Gordillo, 2012).
Differences in perceptions of bullying have been found among races and ethnicities. Rajan et al. (2015) conducted a secondary analysis of data from 84,734 participants in the YRBS from 2001 to 2011. The researchers found that White females and Hispanic females reported an increased prevalence in experiencing bullying on school property White females reported a higher prevalence of experiencing electronic bullying compared to Hispanic females (Rajan et al., 2015). However, in a study of 61,042 high school students who participated in YRBS years 2009 through 2015, Pontes et al. (2018) found the prevalence of experiencing bullying on school property decreased. Between 2009 and 2015, reports of bullying decreased from 18.7% in 2009 to 15.8% in 2015 among males. During the same time frame, females reported a significant increase in experiencing bullying on school property. The prevalence of experiencing bullying among females increased from 21.2% in 2009 to 24.8% in 2015. No significant linear trend was identified for males or females in the likelihood of being bullied electronically. The likelihood of reporting being bullied at school was highest among non-Hispanic White male, White female, and Hispanic female students. The investigators found reports of experiencing bullying decreased with increasing grades (Pontes et al., 2018). Analysis of YRBS data from 2001 through 2016 showed bullying was likely to be experienced by females and White students. Researchers examined Nonviolence Death Reporting System data from 2003 to 2017 (Clark et al., 2020). Of the 9,884 adolescents (mean age 16 years) who died by suicide, 3.4% (n = 334) were identified as lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ). The investigators found that youth who identified as LGBTQ, who died by suicide, were five times more likely to have been bullied compared to their peers. Similarly, a cross-sectional study of students who reported ever having a same-sex partner in their lifetime was conducted using the 2015 YRBS data (Turpin et al., 2019). Investigators found that 924 students (n = 1,001) in Grades 9 through 12 reported having same-sex partners. Of the students (n = 382) with same-sex partners who reported suicide planning, 53% (n = 202) were bullied at school.
Suicide risk behaviors differ between sexes and forms of bullying. Kim et al. (2018) studied adolescent bullying and found that males and females reacted differently to being cyberbullied. Males who were cyberbullied were more likely to demonstrate violence and aggression toward other students. Females who were the recipients of cyberbullying were more likely to report feelings of depression. Furthermore, Smalley et al. (2017) found high school students (n = 261,506) who were bullied were four times more likely to report suicidal ideation. Moreover, bullied high school students were five times more likely to attempt suicide compared to nonbullied students. Smalley et al. (2017) did not investigate whether bullying occurred electronically or at school. However, the results of this review indicate dire consequences of being bullied electronically or at school.
School health services have been recognized by researchers as valuable tools for early identification and prevention of bullying (Borup & Holstein, 2007). In a cross-sectional survey of students in Grades 5, 7, and 9 (n = 5,205), investigators examined differences between students’ visiting the school nurse. The odds of repeated visits to the school nurse by students who were bullied were nearly twice that of not-bullied students (p < .05, odds ratio [OR] 1.8, confidence interval [CI]: 1.3–2.5). A mixed-methods study was conducted to explore how students talked with school nurses about being bullied (Kvarme et al., 2020). Eighth-grade students (n = 70) participated in individual interviews with the school nurse. Bullying was discussed in 20% (n = 29) of interviews with the school nurse. Students revealed that they trusted school nurses and found them easy to talk with about being bullied (Kvarme et al., 2020).
This secondary analysis used data from the YRBS. The YRBS measures adolescent behaviors that impact the leading causes of morbidity and mortality in youth and into adulthood (Kann et al., 1993). The questionnaire was designed for student self-administration in the classroom. The CDC conducts biennial cross-sectional studies to assess the prevalence of adolescent health risk behaviors using the YRBS (Brener et al., 2013). The YRBS sample comprised students in Grade 9 through Grade 12 in the United States and District of Columbia (CDC, 2012, 2014, 2016a, 2018a). Regular public and religious school classrooms and students were included in the sampling frame (Brener et al., 2013).
This secondary analysis followed a descriptive, correlational study design of the YRBS data from 2011 through 2017. YRBS cross-sectional data were used to examine associations among adolescent demographic characteristics, suicide risk behaviors, and the prevalence of bullying. The YRBS uses a three-stage cluster sample design to produce a nationally representative sample of ninth through 12th grade students who attend public and private schools (Underwood et al., 2020). Since YRBS data are publicly available and de-identified, this secondary analysis was granted exempt status by a Midwestern University Institutional Review Board.
The YRBS data files for survey years 2011 through 2017 comprised students from 2011, 2013, 2015, and 2017. De-identified data were downloaded from the CDC website to a secure computer for analysis. The data were compared by year and variables were described and associations statistically analyzed. As the analyses for each of the years were not significantly different between the years, the data were not reported separately by years, but were aggregated together for the analysis.
Nominal categorical variables were used to answer the research questions. Responses were coded as dichotomous variables and the variables represented demographic characteristics of sex as male or female; Grades 9, 10, 11, and 12; and race/ethnicity. The sample characteristic of race/ethnicity had an unbalanced sample, with the majority being identified as “White,” therefore the race/ethnicity variable was recorded as a dichotomous variable of “White” and “All other race/ethnic” groups.
Questions from the YRBS classified suicide risk behaviors. Students responded that they (1) felt sad or hopeless almost every day for two weeks or more in a row; (2) seriously considered attempting suicide, (3) made a plan about how they would attempt suicide, and (4) how many times they attempted suicide (CDC, 2016b). Sadness and hopelessness have been reported predictors for suicide ideation among students in Grades 8 through 11 (James et al., 2017). Furthermore, hopelessness has been identified as a predictor of suicide ideation in depressed male and female adolescents (Wolfe et al., 2019). Therefore, suicide risk behaviors were operationalized as a global variable using “feeling sad or hopeless longer than two weeks,” and “having considered, planned, and attempted suicide.”
Experiencing bullying was operationalized in two forms: experiencing bullying on school property and experiencing bullying electronically. Bullying behavior was measured in two questions asking students about experiencing bullying (CDC, 2017b). The global variable for bullying represented experiencing bullying (a) at school, (b) electronically, or (c) any combination of (a) and (b). Experiencing bullying was experiencing bullying on school property and experiencing bullying electronically. Prior to answering the questions, students read the description of bullying:
Bullying is when one or more students tease, threaten, and spread rumors, hit, shove, or hurt another student repeatedly. It is not bullying when two students of about the same strength or power argue or fight or tease each other in a friendly way (CDC, 2017a, p. 7).
One YRBS question is, during the past 12 months have you ever been bullied on school property? A second YRBS question is, during the past 12 months have you ever been bullied electronically? Between 2011 and 2015, the question read, during the past 12 months have you ever been bullied electronically? (Count being bullied through e-mail, chat rooms, instant messaging, websites, or texting; CDC, 2016b.) In 2017, the question described being bullied electronically as being bullied through texting, Instagram, Facebook, or other social media (CDC, 2017a). Variables in these survey questions were used to answer the research questions. Experiencing bullying on school property was coded A (yes responses coded as 1), and B (no response coded as 0), and experiencing bullying electronically was coded as A (yes response coded as 1), and B (no response coded as 0).
YRBS student records were weighted to account for nonresponse and distribution of students by grade, sex as male or female, and race/ethnicity (Brener et al., 2013). Statistical analysis was conducted using IBM SPSS Complex Samples Statistical Analysis version 26. Due to the number of tests and the sample size, alpha was set at <.01. Associations between the categorical variables and experiencing bullying were examined using cross tabulation. Chi-square tests for independence were conducted to explore the association between the categorical nominal variables (Simpson, 2015).
Student demographic characteristics were examined by survey year. Adolescents self-reported demographic characteristics of grades (9–12), sex (male/female), and race/ethnicity (White versus all others). First, chi-square tests were conducted to identify differences between demographic characteristics of students (N = 57,937) by individual survey years 2011, 2013, 2015, and 2017 for sex, grade, or race/ethnicities. Demographic characteristics of the sample are presented in Table 1. No significant differences were found between individual years for sex (X2 = 20.27, p > .45), grade (X2 = 1.83, p > 1.00), or race/ethnicities (X2 = 161.26, p > .98). Therefore, data for individual survey years 2011–2017 were merged into an aggregated data set and then analyzed.
To answer the first research question, grade, sex, and race/ethnicity data were examined with experiences of bullying. When the data were examined between the years, no significant differences were found for experiencing bullying on school property (X2 = 7.59, p > .66), electronically (X2 = 14.25, p > .31), or by at least one or both of these forms of bullying (X2 = 14.51, p > .50). Therefore, data for the four survey years were merged, creating an aggregated sample of students (N = 59,937). Twenty-three percent of high school students in Grade 9 through Grade 12 (n = 13, 946) reported experiencing some form of bullying, whether: (a) at school, (b) electronically, or (c) any combination of (a) and (b). “Being bullied by at least one way” encompassed being bullied at school, electronically, or any combination of (a) and (b). Students in ninth grade (n = 4,349, 31%) were statistically more likely to experience bullying in at least one form compared to students in grades 10 (n = 3,613, 27%), 11 (n = 3,208, 23%), and 12 (n = 2,718, 19%) (X2 = 306.95, p < .001). Frequencies of students experiencing bullying on school property declined as their grade levels increased. Across all grades, students (n = 10, 704, 18%) reported they were bullied while on school property. Students in ninth grade (n = 3,556, 33%) were statistically more likely to experience bullying while on school property when compared to students in grades 10 (n = 2,826, 28%), 11 (n = 2,404, 22%), or 12 (n = 1,879, 17%) (X2 = 445.33, p < .001). Fourteen percent of students in Grades 9 through 12 (n = 8,267) reported experiencing bullying electronically. Ninth grade students (n = 2,383, 29%) were statistically more likely to experience bullying electronically compared to students in grades 10 (n = 2,121, 27%), 11 (n = 1,950, 23%), or 12 (n = 1,773, 21%) (X2 = 58.81, p < .001). Frequencies of experiencing bullying electronically declined as students’ grade levels increased. High school students (n = 10,793) of all races/ethnicities experienced at least one form of bullying. Males and females (n = 13,954) reported experiencing bullying at school, electronically, or a combination of the two forms. Females (n = 8,372, 60%) were more likely to report being bullied at least one way compared to males (n = 5,582, 40%) (X2 = 891.74, p < .001). Male and female students (n = 10,707) were bullied on school property. Female students (n = 6,210, 58%) were more likely to experience bullying on school property when compared to male students (n = 4,497, 42%) (X2 = 434.50, p < .001). Experiences of electronic bullying (n = 8,274) were also more common among females (n = 5,460, 66%) as compared to males (n = 2,813, 34%) (X2 = 427.49, p < .001). The results are presented in Table 2.
To answer the second research question, comparisons of frequencies of suicide risk behaviors associated with forms of bullying were computed by individual year. The four survey years were compared and no significant differences were found for suicide risk behaviors (X2 = 9.69, p > .55) between 2011 and 2017. Associations between students’ reports of suicide risk behaviors and their experiences of bullying were then examined using chi-square analyses. Students (n = 8,550) who reported suicide risk behaviors were four times more likely to have experienced bullying by at least one form compared to their peers who did not report suicide behaviors (n = 5,130, 60%; n = 3,420, 40%) (X2 = 5,179.52, p < .001). Comparatively, students (n = 6,616) who reported suicide risk behaviors were over three times more likely to have been bullied at school compared to peers who did not report suicide risk behaviors (n = 3,969, 60%; n = 2,646, 40%) (X2 = 3,861.62, p < .001). Students (n = 5,589) who reported suicide risk behaviors were over four times more likely to have experienced bullying electronically compared to peers who did not report suicide behaviors (n = 3,689, 66%; n = 1,900, 34%) (X2 = 4,357.42, p < .001). The ORs indicate students who reported suicide risk behaviors were more likely to have experienced at least one form of bullying compared to their peers. ORs are presented in Table 3.
A positive association was found between adolescent’s demographic characteristics and experiencing bullying in the present study. Students of all grades, races/ethnicities, and both sexes reported suicide risk behaviors and experiences of bullying. However, female students and students in Grade 9 were more likely to experience bullying by at least one form compared to females in Grades 10, 11, or 12. Bullying experiences were also reported in a secondary analysis of a youth behavior survey conducted by Wang et al. (2009). The researchers investigated associations between student demographics in Grade 6 through Grade 10 and bullying. Wang et al. (2009) found that students who were female, younger, or White were more likely to experience bullying. Conversely, Silva et al. (2013) obtained different findings on bullying in their cross-sectional study of students in Grade 2 through Grade 9. In the study conducted by Silva et al. (2013), which included younger participants than the current study, fewer female students (n = 76; 39%) were bullied, compared to 101 boys (54%). Wang et al. (2009) and Silva et al. (2013) reported male students were more likely to experience physical bullying. Wang et al. (2009) and Silva et al. (2013) found that female students were more likely to be bullied electronically compared to male students.
A quantitative study of students in Grades 7 through 12 (n = 465) was conducted to compare demographic characteristics and bullying (e.g., electronic or face-to-face at school) (Lapidot-Lefler & Dolev-Cohen, 2015). The participants completed a questionnaire online. Twenty-six percent of the students (n = 122) were in high school. However, no differences between grades or sex were found for cyberbullying, though males were more likely to experience face-to-face bullying. According to the researchers, face-to-face bullying carries over into the cyberbullying world, as the same perpetrators of school bullying attacked victims online. Across the research studies (Lapidot-Lefler & Dolev-Cohen, 2015; Silva et al., 2013; Wang et al., 2009), students of either sex, any race/ethnicity, or any grade may experience bullying. The findings of the current study indicate the way students experience bullying may change as they progress through their grade years in high school.
A positive association was found between student suicide risk behaviors and experiencing bullying in the present study. In the current study, students who reported suicide risk behaviors were more likely to have experienced bullying. This finding is similar to earlier research in which medical records of youths were screened for mental health concerns and bullying (Kodish et al., 2016). Controlling for depression, investigators found that students reporting suicide risk behaviors were more likely to have experienced a form of bullying. In another cross-sectional investigation involving younger adolescents, Espelage and Holt (2013) identified a positive association between suicidal ideation and bullying. The investigators found that after controlling for depression, 60% of students who are victims of bullying reported suicidal ideation. Students (mean age 13.8 years) were more likely to consider suicide when they had experienced online or face-to-face bullying compared to peers who were not bullied. Students in higher grade levels in this study provided similar feedback, as those who reported suicide risk behaviors were more likely to have experienced at least one form of bullying compared to nonbullied peers. The clinical significance of these studies (Espelage & Holt, 2013; Kodish et al., 2016) and the current study is concerning. All three of the studies report a high prevalence of bullying among high school students. Of concern is that the incidence of adolescent suicide has also increased since 2012 (Hedegaard et al., 2020).
Suicide risk behavior was significantly associated with experiences of bullying during adolescence in the present study, particularly among female students. Prevalence of bullying was reported in all grades, though it was highest among ninth graders. Additional evidence suggests that adolescent experiences of bullying are adverse childhood events that potentially place individuals at risk for negative mental health outcomes in adulthood (Finkelhor et al., 2015), which is important to consider in the practice arena of health care. Preventing student bullying requires a multidisciplinary team approach (Masiello, 2014). Members of the multidisciplinary team must collaborate to develop multifaceted and developmentally appropriate strategies, including dissemination of antibullying messaging. School nurses have a vital role on the multidisciplinary team in protecting and promoting the mental health of students (National Association of School Nurses [NASN], 2018). In an integrative review, investigators found that school nurses were often members of multidisciplinary teams and collaborate with other professionals in school-based suicide prevention initiatives (Pestaner et al., 2021). Promoting healthy, safe school environments void of bullying is within the school nurse’s scope of practice (NASN, 2018).
School nurses see a variety of students each day, many of whom may be victims of bullying (Salmeron & Christian, 2016). In one meta-analysis of students (n = 219,560) across 30 studies in Grade 2 through Grade 12, bullied youths were twice as likely to experience psychosomatic complaints compared to nonbullied peers (Gini & Pozzoli, 2013). Perron (2015) conducted a study of students (n = 222) in Grade 3 through Grade 12 to examine associations between perceived school climate, bullying, and psychosomatic complaints. Fifty-three percent of the participants were in high school. Students who experienced bullying sought care from the school nurse not for bullying, but for psychosomatic issues. Although the samples included students who were younger than the adolescents in the present study, their findings are significant for their description of help-seeking behaviors of bullying victims. Research of youths (n = 1,838) in Grade 9 through Grade 12 was conducted to determine whether involvement in bullying was associated with suicidal ideation (Hepburn et al., 2012). Investigators found that when controlling for age, race, and sex, being a victim of bullying increased the likelihood of a suicidal attempt. Given the associations between demographic characteristics, suicide behaviors, and the prevalence of bullying in the current study, it is clear that bullying continues to be a significant public health problem encountered in schools. School nurses must be properly prepared to implement programs to prevent bullying across all grades, starting with the youngest students. An environment needs to be created where victims can safely disclose when experiences of bullying occur, with appropriate supports in place to intervene and create a safe learning environment (NASN, 2019).
The results of this study indicate that suicide risk behaviors and experiences of bullying should be of concern to those caring for adolescents in the school setting. Considered gatekeepers for mental health concerns, school nurses spend onethird of their time addressing student mental health issues (Bohnenkamp et al., 2015). School nurses form supportive, therapeutic relationships with their students (Kvarme et al., 2013) and identify students at risk for suicide (Bohnenkamp et al., 2015). Researchers (Gini & Pozzoli, 2013; Kvarme et al., 2013; Perron, 2015) suggest that victims’ help-seeking behaviors may lead students to seek help from the school nurse. School nurses should routinely ask students about their welfare when they present to the health room with problems, including bullying (National Academy of Sciences [NAS], 2016). School nurses form trusting relationships with students (Kvarme et al., 2013). Therefore, it is appropriate for school nurses to talk with students about experiences of bullying and referring students to other professionals for help (Perron, 2015) as appropriate.
School nurses are frequently involved in the development and implementation of policies related to health issues in schools and communities (Anderson et al., 2018). Researchers recommend antibullying policies (Limber, 2014; Kub & Feldman, 2015) and suicide prevention programs (Bohnenkamp et al., 2015) should be implemented within school settings. School nurses can play a key role in facilitating school support for students and ensuring resources are available (Kim et al., 2020) to address antibullying and suicide prevention. Kim et al. (2020) found that victimization by cyberbullying was associated with suicidal ideation. However, positive connections with school staff reduced the impact of cyberbullying on the adolescent’s risk for suicide. Whole-school-based bullying prevention programs in which school nurses may build relationships with students would be valuable, including programs such as Link Crew, Problem Behavioral Interventions and Support, and Olweus Bullying prevention programs.
The findings of this study may be used to inform local, state, and national policy makers. School nurses contribute to policy development at the school and systems level (Anderson et al., 2018). School health data are a resource that schools can use to inform policy makers of the mental health concerns and aggressive behaviors among students (Basch, 2011). At the local level, school nurses should collaborate with decision makers to identify school health data management strategies. Integration of data elements into an ongoing health surveillance system can be led by school nurses. Health room data gathered by school nurses may provide an indirect measure of bullying (Perron, 2015), provided nurses ask if students were bullied. Tracking the frequency and reasons for student visits to the health room can alert providers that students may be experiencing bullying (Perron, 2015). School nurses should be contacted to assess students when the health data indicate they may be experiencing bullying or suicide risk behaviors. Health data can inform policy makers at the systems level, enhancing community and school-wide suicide and bullying prevention programs.
Given that the data for this secondary analysis is from a large data set, the findings might be valuable to consider in high schools across the United States. Reviewing the demographics of the population and their reported behaviors can assist school nurses in developing early identification of children who are at risk, and ensuring an appropriate reporting mechanism is in place. Findings from this investigation can further inform efforts to prevent suicide and bullying in the adolescent population consistent with the objectives for Healthy People 2030. Bullying prevention programs in the adolescent population need to include evaluation of their progress in comparison to national objectives and benchmarks.
Additional research is needed to understand associations between demographic characteristics of aggregate student populations and the current study variables. The YRBS data from 2011 to 2015 did not report risk behaviors within the LGBTQ population. Research describing an association between LGBTQ adolescents, suicide risk behaviors, and experiencing bullying is limited. However, prior research suggests that adolescents who identified as lesbian, gay, bisexual, and queer (LGBQ) were more likely to experience bullying than their peers who do not identify as LGBQ (Hillard et al., 2014). Transgender students were not included in the study conducted by Hillard et al. (2014). Furthermore, YRBS data from 2011 to 2015 did not report risk behaviors within the LGBTQ community. Research examining associations between LGBTQ student population demographic characteristics, bullying experiences, and suicide risk behaviors, would be valuable to develop policy protections and intervention guidance for educators and health practitioners. In addition, research is needed to understand whether antibullying policies have been effective for marginalized students protected by civil rights laws (NAS, 2016). The outcomes from the examination of antibullying policies can guide systems-level prevention efforts to ensure compliance with civil rights laws and protection of aggregate populations.
In 2020, the CDC recommended the closure of many schools for extended periods of time in areas with community spread of the coronavirus (CDC, n.d.). Over 45 million public school students transitioned from classroom to distance learning models (Education Week, 2020). The extent to which school campus closures impacted the prevalence of bullying is unknown. However, distance learning via the internet may increase students’ initiation of cyberbullying and place other students at risk for experiencing cyberbullying. Furthermore, the youth’s altered learning and social environments over an extended period of time need to be considered in future YRBS survey results.
The YRBS survey is self-reported student data and is subject to recall and social desirability bias. Oregon, Washington, Wyoming, and Minnesota do not participate in the Youth Risk Surveillance System Survey (CDC, 2019). Illinois, Michigan, Ohio, Tennessee, Georgia, and Alabama have underweighted state results (CDC, 2019). Therefore, students who experienced suicide risk behaviors or bullying may be underrepresented in this study. Unfortunately, it was not possible with this data set to examine the specific mechanisms that are driving higher reported suicide risk behaviors among females and ninth graders. Future research needs to examine the prevalence of higher reported suicide risk behaviors among females, ninth graders, and LGBTQ youth. Possible confounders for this investigation, including rurality, school size, student disabilities, ethnicity, or sexual orientation, were not considered in this study. Data on race/ethnicity that ensure representation of greater diversity within the sample is needed. Equivalence in the demographic characteristic within the sample would provide the balance needed to run a logistical regression analysis to examine confounding variables and explore greater nuances within race/ethnicity. Additional research is needed to gain greater insights into how school nurses can work to address the issue of bullying among high school students while also minimizing suicide risk behaviors.
The findings from this study suggest students who reported suicide risk behaviors were more likely to have been bullied. Demographic characteristic differences among student subpopulations may indicate a need for targeted interventions. It is important to nurture systems- and schoollevel policy and strategies for early prevention and identification of bullying.
Positive associations were described that may serve as guidelines for prevention and early identification of students experiencing bullying. The results of this study support Hinduja and Patchin’s (2019) findings, indicating that students who are female, White, and of early high school age were more likely to suffer a devastating impact from being bullied. Overall, this study found that subpopulations experienced suicide behaviors and bullying. Unfortunately, it was not possible with this data set to examine the mechanisms driving the higher reported suicide risk behaviors among females and ninth graders. Students who experience bullying should be identified, and bullying must be prevented across the school environment. Therefore, prevention efforts should follow the social ecological model. At the intrapersonal level student, demographic characteristics should be considered when prevention policies are developed and implemented. School nurses have unique relationships with individual students, providing a link between the school climate and student health policies at the interpersonal level. Students, school nurses, administrators, lawmakers, and health practitioners should consider findings of this study and the data-driven recommendations in their bullying prevention programs at the organizational level. More research is needed to understand multiple factors that influence victimization by bullying, such as crossing student demographic characteristics. Future studies should explore experiences of LGBTQ students and the school nurse’s role in bullying prevention. Additional research is needed to evaluate efficacy and areas of improvement for current bullying prevention initiatives.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The authors received no financial support for the research, authorship, and/or publication of this article.
Terese Blakeslee https://orcid.org/0000-0002-0773-3747
Anderson, L. J. W., Schaffer, M. A., Hiltz, C., O’Leary, S. A., Luehr, R. E., & Yoney, E. L. (2018). Public health interventions: School nurse practice stories. Journal of School Nursing, 34(3), 192–202. https://doi.org/10.1177/1059840517721951
Barboza, G. E., Schiamberg, L. B., Oehmke, J., Korzeniewski, S. J., Post, L. S., & Heraux, C. G. (2009). Individual characteristics and the multiple contexts of adolescent bullying: An ecological perspective. Journal of Youth Adolescence, 38(1), 101–121. https://doi.org/10.1007/s10964-008-9271-1
Basch, C. (2011). Healthier students are better learners: High-quality, strategically planned, and effectively coordinated school health programs must be a fundamental mission of schools to help close the achievement gap. Journal of School Health, 81(10), 650–662. https://doi.org/10.1111/j.1746-1561.2011.00640.x
Bohnenkamp, J. H., Stephan, S. H., & Bobo, N. (2015). Supporting mental health: The role of the school nurse in coordinated school mental health care. Psychology in the Schools, 52(7), 714–727. https://doi.org/10.1002/pits.21851
Borup, I., & Holstein, B. (2007). Schoolchildren who are victims of bullying report benefit from health dialogues with the school health nurse. Health Education Journal, 66(1), 58–67. https://doi.org/10.1177/0017896907073787
Brener, N., Kann, L., Shanklin, S., Kinchen, S., Eaton, D. K., Hawkins, J., & Flint, K. H. (2013, March 1). Methodology of the youth risk behavior surveillance system 2013. MMWR: Morbidity and Mortality Weekly Report, 62(1), 1–23. https://www.cdc.gov/mmwr/preview/mmwrhtml/rr6201a1.htm
Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513–531. https://doi.org/10.1037//0003-066x.32.7.513
Bronfenbrenner, U. (1994). Ecological models of human development. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (2nd ed., Vol. 3, pp. 37–41). Elsevier.
Centers for Disease Control and Prevention. (n.d.). Considerations for school closure. https://www.cdc.gov/coronavirus/2019-ncov/downloads/considerations-for-school-closure.pdf
Centers for Disease Control and Prevention. (2016a). 2015 YRBS data user’s guide. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2015/2015_yrbs-data-users_guide_smy_combined.pdf
Centers for Disease Control and Prevention. (2016b). YRBS questionnaire content –1991–2017. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2017/YRBS_questionnaire_content_1991-2017.pdf
Centers for Disease Control and Prevention. (2017a). 2017 national youth risk behavior survey. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2017/2017_yrbs_national_hs_questionnaire.pdf
Centers for Disease Control and Prevention. (2017b). Youth risk behavior survey (YRBS) 2017 standard questionnaire item rationale. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2017/2017_standard_YRBS_item_rationale.pdf
Centers for Disease Control and Prevention. (2018a). 2017 YRBS dataset user’s guide. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2017/2017_YRBS_Data_Users_Guide.pdf
Centers for Disease Control and Prevention. (2018b). Youth risk behavior survey. Data summary & trends report. 2007–2017. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/trendsreport.pdf
Centers for Disease Control and Prevention. (2012). 2011 YRBS data user’s guide. ftp://ftp.cdc.gov/pub/data/yrbs/2011/YRBS_2011_National_User_Guide.pdf
Centers for Disease Control and Prevention. (2014). 2013 YRBS data user’s guide. ftp://ftp.cdc.gov/pub/data/yrbs/2013/YRBS_2013_National_User_Guide.pdf
Centers for Disease Control and Prevention. (2019). YRBSS participation maps & history. https://www.cdc.gov/healthyyouth/data/yrbs/participation.htm
Clark, K. A., Cochran, S. D., Maiolastesi, A. J., & Panchankis, J. E. (2020). Prevalence of bullying among youth classified as LGBTQ who died by suicide as reported in the National Violent Death Reporting System, 2003–2017. JAMA Pediatrics, 1749(12), 1211–1213. https://doi.org/10.1001/jamapediatrics.2020.0940
Cook, C. R., Williams, K. R., Guerra, N. G., Kim, T. E., & Sadek, S. (2010). Predictors of bullying and victimization in youthhood and adolescence: A meta-analytic investigation. School Psychology Quarterly, 25(2), 65–83. https://doi.org/10.1037/a0020149
Cuadrado-Gordillo, I. (2012). Repetition, power imbalance, and intentionality: Do these criteria conform to teenagers’ perception of bullying? A role-based analysis. Journal of Interpersonal Violence, 27(10), 1889–1910. https://doi.org/10.1177/0886260511431436
Education Week. (2020, April 26). Map: Coronavirus and school closures. https://www.edweek.org/ew/section/multimedia/mapcoronavirus-and-school-closures.html
Eriksson, M., Ghazinour, M., & Hammarström, A. (2018). Different uses of Bronfenbrenner’s ecological theory in public mental health research: What is their value for guiding public mental health policy and practice? Social Theory & Health, 16(4), 414–433. https://doi.org/10.1057/s41285-018-0065-6
Espelage, D. L. (2014). Ecological theory: Preventing youth bullying, aggression, and victimization. Theory into Practice, 53(4), 1–17. https://doi.org/10.1080/00405841.2014.947216
Espelage, D. L., & Holt, M. K. (2013). Suicidal ideation and school bullying experiences after controlling for depression and delinquency. Journal of Adolescent Health, 53(1), S27–S31. https://doi.org/10.1016/j.jadohealth.2012.09.017
Finkelhor, D., Shattuck, A., Turner, H., & Hamby, S. (2015). A revised inventory of adverse youthhood experiences. Child Abuse & Neglect, 48, 13–21. https://doi.org/10.1016/j.chiabu.2015.07.011
Gini, G., & Pozzoli, T. (2013). Bullied children and psychologic problems: A meta-analysis. Pediatrics, 132(4), 720–729. https://doi.org/10.1542/peds.2013-0614
Gladden, R. M., Vivolo-Kantor, A. M., Hamburger, M. E., & Lumpkin, C. D. (2014). Bullying surveillance among youths: Uniform definitions for public health and recommended data elements, Version 1.0. National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, and U.S. Department of Education. http://www.cdc.gov/violenceprevention/pdf/bullying-definitions-final-a.pdf
Hedegaard, H., Curtin, S. C., & Warner, M. (2020, April). Increase in suicide mortality in the United States, 1999–2018 (NCHS Data Brief No. 362). National Center for Health Statistics. https://www.cdc.gov/nchs/data/databriefs/db362-h.pdf
Hellström, L., Persson, L., & Hagquist, C. (2015). Understanding and defining bullying – Adolescents’ own views. Archives of Public Health, 73(4), 1–9. https://doi.org/10.1186/2049-3258-73-4
Hepburn, L., Azreal, D., Molnar, B., & Miller, M. (2012). Bullying and suicidal behaviors among urban high school youth. Journal of Adolescent Health, 51(1), 93–95. https://doi.org/10.1016/j.jadohealth.2011.12.014
Hillard, P., Love, L., Franks, H., Laris, B., & Coyle, K. (2014). “They were only joking”: Efforts to decrease LGBTQ bullying and harassment in Seattle public schools. Journal of School Health, 84(1), 1–9. https://doi.org/10.1111/josh.12120
Hinduja, S., & Patchin, J. (2019). Connecting adolescent suicide to the severity of bullying and cyberbullying. Journal of School Violence, 18(3), 333–346. https://doi.org/10.1080/15388220.2018.1492417
Ivey-Stephenson, A. Z., Demissie, Z., Crosby, A. E., Stone, D. M., Gaylor, E., Wilkins, N., Lowry, R., & Brown, M. (2020). Suicidal ideation and behaviors among high school students – Youth risk behavior survey, United States, 2019. MMWR: Morbidity and Mortality Weekly Report, 69(S1), 47–55. https://doi.org/10.15585/mmwr.su6901a6
James, S., Reddy, S. P., Ellahebokus, A., Sewpaul, R., & Naidoo, P. (2017). The association between adolescent risk behaviours and feelings of sadness or hopelessness: A cross-sectional survey of South African secondary school learners. Psychology, Health & Medicine, 22(7), 778–789. https://doi.org/10.1080/13548506.2017.1300669
Kann, L., McManus, T., Harris, W. A., Shanklin, S. L., Flint, K. H., Queen, B., Lowry, R., Chyen, D., Whittle, L., Thornton, J., Lim, C., Bradford, D., Yamakawa, Y., Leon, M., Brener, N., & Ethier, K. A. (2018, June 15). Youth risk behavior surveillance—United States, 2017. MMWR Surveillance Summaries, 67(8), 1–114. https://doi.org/10.15585/mmwr.ss6708a1
Kann, L., Warren, W., Collins, J., Ross, J., Collins, B., & Kolbe, L. (1993). Results from the national school-based 1991 youth risk behavior survey and progress toward achieving related health objectives for the nation. Public Health Reports, 108(S1), 47–55. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1403309/
Kim, S., Colwell, S., Kata, A., Boyle, M., & Georgiades, K. (2018). Cyberbullying victimization and adolescent mental health: Evidence of differential effects by sex and mental health problem type. Journal of Youth and Adolescence, 47(3), 661–672. https://doi.org/10.1007/s10964-017-0678-4
Kim, J., Walsh, E., Pike, K., & Thompson, E. (2020). Cyberbullying and victimization and youth suicide risk: The buffering effects of school connectedness. Journal of School Nursing, 36(4), 251–257. https://doi.org/10.1177/1059840518824395
Kodish, T., Herres, J., Shearer, A., Atte, T., Fein, J., & Diamond, G. (2016). Bullying, depression, and suicide risk in a pediatric primary care sample. Crisis: Journal of Crisis Intervention and Suicide Prevention, 37(3), 241–246. https://doi.org/10.1027/0227-5910/a000378
Kub, J., & Feldman, M. A. (2015). Bullying prevention, a call for collaborative efforts between school nurses and school psychologists. Psychology in Schools, 52(7), 658–671. https://doi.org/10.1002/pits.21853
Kvarme, L., Aabø, L., & Sæteren, B. (2013). “I feel I mean something to someone”: Solution-focused brief therapy support groups for bullied schoolchildren. Educational Psychology in Practice, 29(4), 416–431. https://doi.org/10.1080/02667363.2013.859569
Kvarme, L., Valla, L., Holen, S., & Sagatun, A. (2020). Bullying in school: Importance of and challenges involved in talking to the school nurse. Journal of School Nursing, 36(6), 451–457. https://doi.org/10.1177/1059840519846649
Lapidot-Lefler, N., & Dolev-Cohen, M. (2015). Comparing cyberbullying and school bullying among school students: Prevalence, gender, and grade level differences. Social Psychology of Education, 18(1), 1–16. https://doi.org/10.1007/s11218-014-9280-8
Li, R., Lian, Q., Su, Q., Luhani, L., Meixian, X., & Hu, J. (2020). Trends and sex disparities in school bullying victimization among U.S. youth, 2011–2019. BMC Public Health, 20, Article 1583. https://doi.org/10.1186/s12889-020-09677-3
Limber, S. (2014). Best practices in the prevention of bullying: An example of the Olweus bullying prevention program. In M. G. Masiello & D. Schroeder (Eds.), A public health approach to bullying prevention (pp. 127–148). American Public Health Association.
Masiello, A. (2014). Identifying impact—monitoring and evaluating of a bullying prevention program. In D. Masiello & D. Schroeder (Eds.), A public health approach to bullying prevention (pp. 253–273). Jones & Bartlett Learning.
National Academies of Sciences, Engineering, and Medicine. (2016). Preventing bullying through science, policy, and practice. National Academies Press. https://doi.org/10.17226/23482
National Association of School Nurses. (2018). The school nurse’s role in behavioral/mental health of students (position statement). https://files.eric.ed.gov/fulltext/ED581612.pdf
National Association of School Nurses. (2019). Federal legislation and agencies. https://www.nasn.org/advocacy/advocacy-federal
Olweus, D. (1978). Aggression in schools. Bullies and whipping males. Hemisphere Publishing.
Olweus, D. (1994). Annotation: Bullying at school: Basic facts and effects of a school-based intervention program. Journal of Child Psychology and Psychiatry, 35(7), 1171–1190. https://doi.org/10.1111/j.1469-7610.1994.tb01229.x
Olweus, D. (2013). School bullying: Development and some important challenges. Annual Review of Clinical Psychology, 9(1), 751–780. https://doi.org/10.1146/annurev-clinpsy-050212-185516
Olweus, D., & Limber, S. P. (2018). Some problems with cyberbullying research. Current Opinion in Psychology, 19, 139–143. https://doi.org/10.1016/j.copsyc.2017.04.012
Perron, T. (2015). Looking at the factors associated with bullying and visits to the school nurse, in the United States. British Journal of School Nursing, 10(6), 288–295. https://doi.org/10.12968/bjsn.2015.10.6.288
Pestaner, M. C., Tyndall, D. E., & Powell, S. B. (2021). The role of the school nurse in suicide interventions: An integrative review. Journal of School Nursing, 37(1), 41–50. https://doi.org/10.1177/1059840519889679
Pontes, N. M. H., Ayres, C. G., Lewandowski, C., & Pontes, M. C. F. (2018). Trends in bullying victimization by sex among U.S. high school students. Research in Nursing & Health, 41(3), 243–251. https://doi.org/10.1002/nur.21868
Rajan, S., Namdar, R., & Ruggles, K. V. (2015). Aggressive and violent behaviors in the school environment among a nationally representative sample of adolescent youth. Journal of School Health, 85(7), 446–457. https://doi.org/10.1111/josh.12272
Salmeron, P., & Christian, B. (2016). Evaluation of an educational program to improve school nursing staff perceptions of bullying in Pinellas County, Florida. Pediatric Nursing, 42(6), 283–292.
Silva, M., Pereira, B., Mendonça, D., Nunes, B., & De Oliveira, W. (2013). The involvement of girls and boys with bullying: An analysis of sex differences. International Journal of Environmental Research and Public Health, 10(12), 6820–6831. https://doi.org/10.3390/ijerph10126820
Simpson, S. H. (2015). Creating a data analysis plan: What to consider when choosing statistics for a study. Canadian Journal of Hospital Pharmacy, 68(4), 311–317. https://doi.org/10.4212/cjhp.v68i4.1471
Smalley, B. K., Warren, J. C., & Barefoot, N. K. (2017). Connection between experiences of bullying and risky behaviors in middle and high school students. School Mental Health, 9(1), 87–96. https://doi.org/10.1007/s12310-016-9194-z
Turpin, R., Boekeloo, B., & Dyer, T. (2019). Sexual identity modifies the association between bullying and suicide planning among adolescents with same-sex sexual partners. Journal of LGBT Youth, 16(3), 300–316. https://doi.org/10.1080/19361653.2019.1575784
Underwood, J. M., Brener, N., Thornton, J., Harris, W. A., Bryan, L. N., Shanklin, S. L., Deputy, N., Roberts, A. M., Queen, B., Chyen, D., Whittle, L., Lim, C., Yamakawa, Y., Leon-Nguyen, M., Kilmer, G., Smith-Grant, J., Demissie, Z., Jones, S. E., Clayton, H., & Dittus, P. (2020). Overview and methods for the youth risk behavior surveillance system – United, 2019. MMWR: Morbidity and Mortality Weekly Report Supplements, 69(1), 1–10. https://doi.org/10.15585/mmwr.su6901a1
U.S. Department of Health and Human Services. (2020a). Reduce bullying of lesbian, gay, or bisexual high school students – LGBT-05. https://health.gov/healthypeople/objectives-and-data/browse-objectives/lgbt/reduce-bullying-lesbian-gay-or-bisexualhigh-school-students-lgbt-05
U.S. Department of Health and Human Services. (2020b). Reduce suicide attempts by adolescents – MHMD-02. https://health.gov/healthypeople/objectives-and-data/browse-objectives/mentalhealth-and-mental-disorders/reduce-suicide-attempts-adolescentsmhmd-02
U.S. Department of Health and Human Services. (2018). IVP-35 reduce bullying among adolescents. https://www.healthypeople.gov/node/4773/data_details
Wang, J., Iannotti, R., & Nansel, T. (2009). School bullying among adolescents in the United States: Physical, verbal, relational, and cyber. Journal of Adolescent Health, 45(4), 368–375. https://doi.org/10.1016/j.jadohealth.2009.03.021
Wolfe, K. L., Nakonezny, P. A., Owen, V. J., Rial, K. V., Moorehead, A. P., Kennard, B. D., & Emslie, G. J. (2019). Hopelessness as a predictor of suicide ideation in depressed male and female adolescent youth. Suicide & Life-Threatening Behavior, 49(1), 253–263. https://doi.org/10.1111/sltb.12428
Terese Blakeslee, PhD, RN, is an assistant professor at the University of Wisconsin-Oshkosh.
Julia Snethen, PhD, RN, FAAN, is a professor and PhD Program Director at the University of Wisconsin-Milwaukee.
Rachel F. Schiffman, PhD, RN, FAAN, is Professor Emerita at the University of Wisconsin-Milwaukee.
Seok Hyun Gwon, PhD, RN, is an assistant professor at the University of Wisconsin-Milwaukee.
Marty Sapp, EdD, is Professor Emerita at the University of Wisconsin-Milwaukee.
Sheryl Kelber, MS, is a retired biostatistician from the University of Wisconsin-Milwaukee.
1 University of Wisconsin-Oshkosh, Oshkosh, WI, USA
2 University of Wisconsin-Milwaukee, Milwaukee, WI, USA
Corresponding Author:Terese Blakeslee, PhD, RN, University of Wisconsin-Oshkosh, 800 Algoma Blvd, Oshkosh, WI, USA.Email: blakeslt@uwosh.edu
