effects of bullying to students research paper

• Research article
• Open access
• Published: 14 December 2021

Bullying at school and mental health problems among adolescents: a repeated cross-sectional study

• Håkan Källmén 1 &
• Mats Hallgren   ORCID: orcid.org/0000-0002-0599-2403 2  

Child and Adolescent Psychiatry and Mental Health volume  15 , Article number:  74 ( 2021 ) Cite this article

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To examine recent trends in bullying and mental health problems among adolescents and the association between them.

A questionnaire measuring mental health problems, bullying at school, socio-economic status, and the school environment was distributed to all secondary school students aged 15 (school-year 9) and 18 (school-year 11) in Stockholm during 2014, 2018, and 2020 (n = 32,722). Associations between bullying and mental health problems were assessed using logistic regression analyses adjusting for relevant demographic, socio-economic, and school-related factors.

The prevalence of bullying remained stable and was highest among girls in year 9; range = 4.9% to 16.9%. Mental health problems increased; range = + 1.2% (year 9 boys) to + 4.6% (year 11 girls) and were consistently higher among girls (17.2% in year 11, 2020). In adjusted models, having been bullied was detrimentally associated with mental health (OR = 2.57 [2.24–2.96]). Reports of mental health problems were four times higher among boys who had been bullied compared to those not bullied. The corresponding figure for girls was 2.4 times higher.

Conclusions

Exposure to bullying at school was associated with higher odds of mental health problems. Boys appear to be more vulnerable to the deleterious effects of bullying than girls.

Introduction

Bullying involves repeated hurtful actions between peers where an imbalance of power exists [ 1 ]. Arseneault et al. [ 2 ] conducted a review of the mental health consequences of bullying for children and adolescents and found that bullying is associated with severe symptoms of mental health problems, including self-harm and suicidality. Bullying was shown to have detrimental effects that persist into late adolescence and contribute independently to mental health problems. Updated reviews have presented evidence indicating that bullying is causative of mental illness in many adolescents [ 3 , 4 ].

There are indications that mental health problems are increasing among adolescents in some Nordic countries. Hagquist et al. [ 5 ] examined trends in mental health among Scandinavian adolescents (n = 116, 531) aged 11–15 years between 1993 and 2014. Mental health problems were operationalized as difficulty concentrating, sleep disorders, headache, stomach pain, feeling tense, sad and/or dizzy. The study revealed increasing rates of adolescent mental health problems in all four counties (Finland, Sweden, Norway, and Denmark), with Sweden experiencing the sharpest increase among older adolescents, particularly girls. Worsening adolescent mental health has also been reported in the United Kingdom. A study of 28,100 school-aged adolescents in England found that two out of five young people scored above thresholds for emotional problems, conduct problems or hyperactivity [ 6 ]. Female gender, deprivation, high needs status (educational/social), ethnic background, and older age were all associated with higher odds of experiencing mental health difficulties.

Bullying is shown to increase the risk of poor mental health and may partly explain these detrimental changes. Le et al. [ 7 ] reported an inverse association between bullying and mental health among 11–16-year-olds in Vietnam. They also found that poor mental health can make some children and adolescents more vulnerable to bullying at school. Bayer et al. [ 8 ] examined links between bullying at school and mental health among 8–9-year-old children in Australia. Those who experienced bullying more than once a week had poorer mental health than children who experienced bullying less frequently. Friendships moderated this association, such that children with more friends experienced fewer mental health problems (protective effect). Hysing et al. [ 9 ] investigated the association between experiences of bullying (as a victim or perpetrator) and mental health, sleep disorders, and school performance among 16–19 year olds from Norway (n = 10,200). Participants were categorized as victims, bullies, or bully-victims (that is, victims who also bullied others). All three categories were associated with worse mental health, school performance, and sleeping difficulties. Those who had been bullied also reported more emotional problems, while those who bullied others reported more conduct disorders [ 9 ].

As most adolescents spend a considerable amount of time at school, the school environment has been a major focus of mental health research [ 10 , 11 ]. In a recent review, Saminathen et al. [ 12 ] concluded that school is a potential protective factor against mental health problems, as it provides a socially supportive context and prepares students for higher education and employment. However, it may also be the primary setting for protracted bullying and stress [ 13 ]. Another factor associated with adolescent mental health is parental socio-economic status (SES) [ 14 ]. A systematic review indicated that lower parental SES is associated with poorer adolescent mental health [ 15 ]. However, no previous studies have examined whether SES modifies or attenuates the association between bullying and mental health. Similarly, it remains unclear whether school related factors, such as school grades and the school environment, influence the relationship between bullying and mental health. This information could help to identify those adolescents most at risk of harm from bullying.

To address these issues, we investigated the prevalence of bullying at school and mental health problems among Swedish adolescents aged 15–18 years between 2014 and 2020 using a population-based school survey. We also examined associations between bullying at school and mental health problems adjusting for relevant demographic, socioeconomic, and school-related factors. We hypothesized that: (1) bullying and adolescent mental health problems have increased over time; (2) There is an association between bullying victimization and mental health, so that mental health problems are more prevalent among those who have been victims of bullying; and (3) that school-related factors would attenuate the association between bullying and mental health.

Participants

The Stockholm school survey is completed every other year by students in lower secondary school (year 9—compulsory) and upper secondary school (year 11). The survey is mandatory for public schools, but voluntary for private schools. The purpose of the survey is to help inform decision making by local authorities that will ultimately improve students’ wellbeing. The questions relate to life circumstances, including SES, schoolwork, bullying, drug use, health, and crime. Non-completers are those who were absent from school when the survey was completed (< 5%). Response rates vary from year to year but are typically around 75%. For the current study data were available for 2014, 2018 and 2020. In 2014; 5235 boys and 5761 girls responded, in 2018; 5017 boys and 5211 girls responded, and in 2020; 5633 boys and 5865 girls responded (total n = 32,722). Data for the exposure variable, bullied at school, were missing for 4159 students, leaving 28,563 participants in the crude model. The fully adjusted model (described below) included 15,985 participants. The mean age in grade 9 was 15.3 years (SD = 0.51) and in grade 11, 17.3 years (SD = 0.61). As the data are completely anonymous, the study was exempt from ethical approval according to an earlier decision from the Ethical Review Board in Stockholm (2010-241 31-5). Details of the survey are available via a website [ 16 ], and are described in a previous paper [ 17 ].

Students completed the questionnaire during a school lesson, placed it in a sealed envelope and handed it to their teacher. Student were permitted the entire lesson (about 40 min) to complete the questionnaire and were informed that participation was voluntary (and that they were free to cancel their participation at any time without consequences). Students were also informed that the Origo Group was responsible for collection of the data on behalf of the City of Stockholm.

Study outcome

Mental health problems were assessed by using a modified version of the Psychosomatic Problem Scale [ 18 ] shown to be appropriate for children and adolescents and invariant across gender and years. The scale was later modified [ 19 ]. In the modified version, items about difficulty concentrating and feeling giddy were deleted and an item about ‘life being great to live’ was added. Seven different symptoms or problems, such as headaches, depression, feeling fear, stomach problems, difficulty sleeping, believing it’s great to live (coded negatively as seldom or rarely) and poor appetite were used. Students who responded (on a 5-point scale) that any of these problems typically occurs ‘at least once a week’ were considered as having indicators of a mental health problem. Cronbach alpha was 0.69 across the whole sample. Adding these problem areas, a total index was created from 0 to 7 mental health symptoms. Those who scored between 0 and 4 points on the total symptoms index were considered to have a low indication of mental health problems (coded as 0); those who scored between 5 and 7 symptoms were considered as likely having mental health problems (coded as 1).

Primary exposure

Experiences of bullying were measured by the following two questions: Have you felt bullied or harassed during the past school year? Have you been involved in bullying or harassing other students during this school year? Alternatives for the first question were: yes or no with several options describing how the bullying had taken place (if yes). Alternatives indicating emotional bullying were feelings of being mocked, ridiculed, socially excluded, or teased. Alternatives indicating physical bullying were being beaten, kicked, forced to do something against their will, robbed, or locked away somewhere. The response alternatives for the second question gave an estimation of how often the respondent had participated in bullying others (from once to several times a week). Combining the answers to these two questions, five different categories of bullying were identified: (1) never been bullied and never bully others; (2) victims of emotional (verbal) bullying who have never bullied others; (3) victims of physical bullying who have never bullied others; (4) victims of bullying who have also bullied others; and (5) perpetrators of bullying, but not victims. As the number of positive cases in the last three categories was low (range = 3–15 cases) bully categories 2–4 were combined into one primary exposure variable: ‘bullied at school’.

Assessment year was operationalized as the year when data was collected: 2014, 2018, and 2020. Age was operationalized as school grade 9 (15–16 years) or 11 (17–18 years). Gender was self-reported (boy or girl). The school situation To assess experiences of the school situation, students responded to 18 statements about well-being in school, participation in important school matters, perceptions of their teachers, and teaching quality. Responses were given on a four-point Likert scale ranging from ‘do not agree at all’ to ‘fully agree’. To reduce the 18-items down to their essential factors, we performed a principal axis factor analysis. Results showed that the 18 statements formed five factors which, according to the Kaiser criterion (eigen values > 1) explained 56% of the covariance in the student’s experience of the school situation. The five factors identified were: (1) Participation in school; (2) Interesting and meaningful work; (3) Feeling well at school; (4) Structured school lessons; and (5) Praise for achievements. For each factor, an index was created that was dichotomised (poor versus good circumstance) using the median-split and dummy coded with ‘good circumstance’ as reference. A description of the items included in each factor is available as Additional file 1 . Socio-economic status (SES) was assessed with three questions about the education level of the student’s mother and father (dichotomized as university degree versus not), and the amount of spending money the student typically received for entertainment each month (> SEK 1000 [approximately $120] versus less). Higher parental education and more spending money were used as reference categories. School grades in Swedish, English, and mathematics were measured separately on a 7-point scale and dichotomized as high (grades A, B, and C) versus low (grades D, E, and F). High school grades were used as the reference category.

Statistical analyses

The prevalence of mental health problems and bullying at school are presented using descriptive statistics, stratified by survey year (2014, 2018, 2020), gender, and school year (9 versus 11). As noted, we reduced the 18-item questionnaire assessing school function down to five essential factors by conducting a principal axis factor analysis (see Additional file 1 ). We then calculated the association between bullying at school (defined above) and mental health problems using multivariable logistic regression. Results are presented as odds ratios (OR) with 95% confidence intervals (Cis). To assess the contribution of SES and school-related factors to this association, three models are presented: Crude, Model 1 adjusted for demographic factors: age, gender, and assessment year; Model 2 adjusted for Model 1 plus SES (parental education and student spending money), and Model 3 adjusted for Model 2 plus school-related factors (school grades and the five factors identified in the principal factor analysis). These covariates were entered into the regression models in three blocks, where the final model represents the fully adjusted analyses. In all models, the category ‘not bullied at school’ was used as the reference. Pseudo R-square was calculated to estimate what proportion of the variance in mental health problems was explained by each model. Unlike the R-square statistic derived from linear regression, the Pseudo R-square statistic derived from logistic regression gives an indicator of the explained variance, as opposed to an exact estimate, and is considered informative in identifying the relative contribution of each model to the outcome [ 20 ]. All analyses were performed using SPSS v. 26.0.

Prevalence of bullying at school and mental health problems

Estimates of the prevalence of bullying at school and mental health problems across the 12 strata of data (3 years × 2 school grades × 2 genders) are shown in Table 1 . The prevalence of bullying at school increased minimally (< 1%) between 2014 and 2020, except among girls in grade 11 (2.5% increase). Mental health problems increased between 2014 and 2020 (range = 1.2% [boys in year 11] to 4.6% [girls in year 11]); were three to four times more prevalent among girls (range = 11.6% to 17.2%) compared to boys (range = 2.6% to 4.9%); and were more prevalent among older adolescents compared to younger adolescents (range = 1% to 3.1% higher). Pooling all data, reports of mental health problems were four times more prevalent among boys who had been victims of bullying compared to those who reported no experiences with bullying. The corresponding figure for girls was two and a half times as prevalent.

Associations between bullying at school and mental health problems

Table 2 shows the association between bullying at school and mental health problems after adjustment for relevant covariates. Demographic factors, including female gender (OR = 3.87; CI 3.48–4.29), older age (OR = 1.38, CI 1.26–1.50), and more recent assessment year (OR = 1.18, CI 1.13–1.25) were associated with higher odds of mental health problems. In Model 2, none of the included SES variables (parental education and student spending money) were associated with mental health problems. In Model 3 (fully adjusted), the following school-related factors were associated with higher odds of mental health problems: lower grades in Swedish (OR = 1.42, CI 1.22–1.67); uninteresting or meaningless schoolwork (OR = 2.44, CI 2.13–2.78); feeling unwell at school (OR = 1.64, CI 1.34–1.85); unstructured school lessons (OR = 1.31, CI = 1.16–1.47); and no praise for achievements (OR = 1.19, CI 1.06–1.34). After adjustment for all covariates, being bullied at school remained associated with higher odds of mental health problems (OR = 2.57; CI 2.24–2.96). Demographic and school-related factors explained 12% and 6% of the variance in mental health problems, respectively (Pseudo R-Square). The inclusion of socioeconomic factors did not alter the variance explained.

Our findings indicate that mental health problems increased among Swedish adolescents between 2014 and 2020, while the prevalence of bullying at school remained stable (< 1% increase), except among girls in year 11, where the prevalence increased by 2.5%. As previously reported [ 5 , 6 ], mental health problems were more common among girls and older adolescents. These findings align with previous studies showing that adolescents who are bullied at school are more likely to experience mental health problems compared to those who are not bullied [ 3 , 4 , 9 ]. This detrimental relationship was observed after adjustment for school-related factors shown to be associated with adolescent mental health [ 10 ].

A novel finding was that boys who had been bullied at school reported a four-times higher prevalence of mental health problems compared to non-bullied boys. The corresponding figure for girls was 2.5 times higher for those who were bullied compared to non-bullied girls, which could indicate that boys are more vulnerable to the deleterious effects of bullying than girls. Alternatively, it may indicate that boys are (on average) bullied more frequently or more intensely than girls, leading to worse mental health. Social support could also play a role; adolescent girls often have stronger social networks than boys and could be more inclined to voice concerns about bullying to significant others, who in turn may offer supports which are protective [ 21 ]. Related studies partly confirm this speculative explanation. An Estonian study involving 2048 children and adolescents aged 10–16 years found that, compared to girls, boys who had been bullied were more likely to report severe distress, measured by poor mental health and feelings of hopelessness [ 22 ].

Other studies suggest that heritable traits, such as the tendency to internalize problems and having low self-esteem are associated with being a bully-victim [ 23 ]. Genetics are understood to explain a large proportion of bullying-related behaviors among adolescents. A study from the Netherlands involving 8215 primary school children found that genetics explained approximately 65% of the risk of being a bully-victim [ 24 ]. This proportion was similar for boys and girls. Higher than average body mass index (BMI) is another recognized risk factor [ 25 ]. A recent Australian trial involving 13 schools and 1087 students (mean age = 13 years) targeted adolescents with high-risk personality traits (hopelessness, anxiety sensitivity, impulsivity, sensation seeking) to reduce bullying at school; both as victims and perpetrators [ 26 ]. There was no significant intervention effect for bullying victimization or perpetration in the total sample. In a secondary analysis, compared to the control schools, intervention school students showed greater reductions in victimization, suicidal ideation, and emotional symptoms. These findings potentially support targeting high-risk personality traits in bullying prevention [ 26 ].

The relative stability of bullying at school between 2014 and 2020 suggests that other factors may better explain the increase in mental health problems seen here. Many factors could be contributing to these changes, including the increasingly competitive labour market, higher demands for education, and the rapid expansion of social media [ 19 , 27 , 28 ]. A recent Swedish study involving 29,199 students aged between 11 and 16 years found that the effects of school stress on psychosomatic symptoms have become stronger over time (1993–2017) and have increased more among girls than among boys [ 10 ]. Research is needed examining possible gender differences in perceived school stress and how these differences moderate associations between bullying and mental health.

Strengths and limitations

Strengths of the current study include the large participant sample from diverse schools; public and private, theoretical and practical orientations. The survey included items measuring diverse aspects of the school environment; factors previously linked to adolescent mental health but rarely included as covariates in studies of bullying and mental health. Some limitations are also acknowledged. These data are cross-sectional which means that the direction of the associations cannot be determined. Moreover, all the variables measured were self-reported. Previous studies indicate that students tend to under-report bullying and mental health problems [ 29 ]; thus, our results may underestimate the prevalence of these behaviors.

In conclusion, consistent with our stated hypotheses, we observed an increase in self-reported mental health problems among Swedish adolescents, and a detrimental association between bullying at school and mental health problems. Although bullying at school does not appear to be the primary explanation for these changes, bullying was detrimentally associated with mental health after adjustment for relevant demographic, socio-economic, and school-related factors, confirming our third hypothesis. The finding that boys are potentially more vulnerable than girls to the deleterious effects of bullying should be replicated in future studies, and the mechanisms investigated. Future studies should examine the longitudinal association between bullying and mental health, including which factors mediate/moderate this relationship. Epigenetic studies are also required to better understand the complex interaction between environmental and biological risk factors for adolescent mental health [ 24 ].

Availability of data and materials

Data requests will be considered on a case-by-case basis; please email the corresponding author.

Code availability

Not applicable.

Olweus D. School bullying: development and some important challenges. Ann Rev Clin Psychol. 2013;9(9):751–80. https://doi.org/10.1146/annurev-clinpsy-050212-185516 .

Article   Google Scholar  

Arseneault L, Bowes L, Shakoor S. Bullying victimization in youths and mental health problems: “Much ado about nothing”? Psychol Med. 2010;40(5):717–29. https://doi.org/10.1017/S0033291709991383 .

Article   CAS   PubMed   Google Scholar  

Arseneault L. The long-term impact of bullying victimization on mental health. World Psychiatry. 2017;16(1):27–8. https://doi.org/10.1002/wps.20399 .

Article   PubMed   PubMed Central   Google Scholar  

Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: a systematic review and meta-analysis. World J Psychiatry. 2017;7(1):60–76. https://doi.org/10.5498/wjp.v7.i1.60 .

Hagquist C, Due P, Torsheim T, Valimaa R. Cross-country comparisons of trends in adolescent psychosomatic symptoms—a Rasch analysis of HBSC data from four Nordic countries. Health Qual Life Outcomes. 2019;17(1):27. https://doi.org/10.1186/s12955-019-1097-x .

Deighton J, Lereya ST, Casey P, Patalay P, Humphrey N, Wolpert M. Prevalence of mental health problems in schools: poverty and other risk factors among 28 000 adolescents in England. Br J Psychiatry. 2019;215(3):565–7. https://doi.org/10.1192/bjp.2019.19 .

Article   PubMed Central   Google Scholar  

Le HTH, Tran N, Campbell MA, Gatton ML, Nguyen HT, Dunne MP. Mental health problems both precede and follow bullying among adolescents and the effects differ by gender: a cross-lagged panel analysis of school-based longitudinal data in Vietnam. Int J Ment Health Syst. 2019. https://doi.org/10.1186/s13033-019-0291-x .

Bayer JK, Mundy L, Stokes I, Hearps S, Allen N, Patton G. Bullying, mental health and friendship in Australian primary school children. Child Adolesc Ment Health. 2018;23(4):334–40. https://doi.org/10.1111/camh.12261 .

Article   PubMed   Google Scholar  

Hysing M, Askeland KG, La Greca AM, Solberg ME, Breivik K, Sivertsen B. Bullying involvement in adolescence: implications for sleep, mental health, and academic outcomes. J Interpers Violence. 2019. https://doi.org/10.1177/0886260519853409 .

Hogberg B, Strandh M, Hagquist C. Gender and secular trends in adolescent mental health over 24 years—the role of school-related stress. Soc Sci Med. 2020. https://doi.org/10.1016/j.socscimed.2020.112890 .

Kidger J, Araya R, Donovan J, Gunnell D. The effect of the school environment on the emotional health of adolescents: a systematic review. Pediatrics. 2012;129(5):925–49. https://doi.org/10.1542/peds.2011-2248 .

Saminathen MG, Låftman SB, Modin B. En fungerande skola för alla: skolmiljön som skyddsfaktor för ungas psykiska välbefinnande. [A functioning school for all: the school environment as a protective factor for young people’s mental well-being]. Socialmedicinsk tidskrift [Soc Med]. 2020;97(5–6):804–16.

Google Scholar  

Bibou-Nakou I, Tsiantis J, Assimopoulos H, Chatzilambou P, Giannakopoulou D. School factors related to bullying: a qualitative study of early adolescent students. Soc Psychol Educ. 2012;15(2):125–45. https://doi.org/10.1007/s11218-012-9179-1 .

Vukojevic M, Zovko A, Talic I, Tanovic M, Resic B, Vrdoljak I, Splavski B. Parental socioeconomic status as a predictor of physical and mental health outcomes in children—literature review. Acta Clin Croat. 2017;56(4):742–8. https://doi.org/10.20471/acc.2017.56.04.23 .

Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: a systematic review. Soc Sci Med. 2013;90:24–31. https://doi.org/10.1016/j.socscimed.2013.04.026 .

Stockholm City. Stockholmsenkät (The Stockholm Student Survey). 2021. https://start.stockholm/aktuellt/nyheter/2020/09/presstraff-stockholmsenkaten-2020/ . Accessed 19 Nov 2021.

Zeebari Z, Lundin A, Dickman PW, Hallgren M. Are changes in alcohol consumption among swedish youth really occurring “in concert”? A new perspective using quantile regression. Alc Alcohol. 2017;52(4):487–95. https://doi.org/10.1093/alcalc/agx020 .

Hagquist C. Psychometric properties of the PsychoSomatic Problems Scale: a Rasch analysis on adolescent data. Social Indicat Res. 2008;86(3):511–23. https://doi.org/10.1007/s11205-007-9186-3 .

Hagquist C. Ungas psykiska hälsa i Sverige–komplexa trender och stora kunskapsluckor [Young people’s mental health in Sweden—complex trends and large knowledge gaps]. Socialmedicinsk tidskrift [Soc Med]. 2013;90(5):671–83.

Wu W, West SG. Detecting misspecification in mean structures for growth curve models: performance of pseudo R(2)s and concordance correlation coefficients. Struct Equ Model. 2013;20(3):455–78. https://doi.org/10.1080/10705511.2013.797829 .

Holt MK, Espelage DL. Perceived social support among bullies, victims, and bully-victims. J Youth Adolscence. 2007;36(8):984–94. https://doi.org/10.1007/s10964-006-9153-3 .

Mark L, Varnik A, Sisask M. Who suffers most from being involved in bullying-bully, victim, or bully-victim? J Sch Health. 2019;89(2):136–44. https://doi.org/10.1111/josh.12720 .

Tsaousis I. The relationship of self-esteem to bullying perpetration and peer victimization among schoolchildren and adolescents: a meta-analytic review. Aggress Violent Behav. 2016;31:186–99. https://doi.org/10.1016/j.avb.2016.09.005 .

Veldkamp SAM, Boomsma DI, de Zeeuw EL, van Beijsterveldt CEM, Bartels M, Dolan CV, van Bergen E. Genetic and environmental influences on different forms of bullying perpetration, bullying victimization, and their co-occurrence. Behav Genet. 2019;49(5):432–43. https://doi.org/10.1007/s10519-019-09968-5 .

Janssen I, Craig WM, Boyce WF, Pickett W. Associations between overweight and obesity with bullying behaviors in school-aged children. Pediatrics. 2004;113(5):1187–94. https://doi.org/10.1542/peds.113.5.1187 .

Kelly EV, Newton NC, Stapinski LA, Conrod PJ, Barrett EL, Champion KE, Teesson M. A novel approach to tackling bullying in schools: personality-targeted intervention for adolescent victims and bullies in Australia. J Am Acad Child Adolesc Psychiatry. 2020;59(4):508. https://doi.org/10.1016/j.jaac.2019.04.010 .

Gunnell D, Kidger J, Elvidge H. Adolescent mental health in crisis. BMJ. 2018. https://doi.org/10.1136/bmj.k2608 .

O’Reilly M, Dogra N, Whiteman N, Hughes J, Eruyar S, Reilly P. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin Child Psychol Psychiatry. 2018;23:601–13.

Unnever JD, Cornell DG. Middle school victims of bullying: who reports being bullied? Aggr Behav. 2004;30(5):373–88. https://doi.org/10.1002/ab.20030 .

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Acknowledgements

Authors are grateful to the Department for Social Affairs, Stockholm, for permission to use data from the Stockholm School Survey.

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HK conceived the study and analyzed the data (with input from MH). HK and MH interpreted the data and jointly wrote the manuscript. All authors read and approved the final manuscript.

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Principal factor analysis description.

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Källmén, H., Hallgren, M. Bullying at school and mental health problems among adolescents: a repeated cross-sectional study. Child Adolesc Psychiatry Ment Health 15 , 74 (2021). https://doi.org/10.1186/s13034-021-00425-y

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ORIGINAL RESEARCH article

Understanding alternative bullying perspectives through research engagement with young people.

• School of Education and Social Care, Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom

Bullying research has traditionally been dominated by largescale cohort studies focusing on the personality traits of bullies and victims. These studies focus on bullying prevalence, risk and protective factors, and negative outcomes. A limitation of this approach is that it does not explain why bullying happens. Qualitative research can help shed light on these factors. This paper discusses the findings from four mainly qualitative research projects including a systematic review and three empirical studies involving young people to various degrees within the research process as respondents, co-researchers and commissioners of research. Much quantitative research suggests that young people are a homogenous group and through the use of surveys and other large scale methods, generalizations can be drawn about how bullying is understood and how it can be dealt with. Findings from the studies presented in this paper, add to our understanding that young people appear particularly concerned about the role of wider contextual and relational factors in deciding if bullying has happened. These studies underscore the relational aspects of definitions of bullying and, how the dynamics of young people’s friendships can shift what is understood as bullying or not. Moreover, to appreciate the relational and social contexts underpinning bullying behaviors, adults and young people need to work together on bullying agendas and engage with multiple definitions, effects and forms of support. Qualitative methodologies, in particular participatory research opens up the complexities of young lives and enables these insights to come to the fore. Through this approach, effective supports can be designed based on what young people want and need rather than those interpreted as supportive through adult understanding.

Introduction

Research on school bullying has developed rapidly since the 1970s. Originating in social and psychological research in Norway, Sweden, and Finland, this body of research largely focusses on individualized personality traits of perpetrators and victims ( Olweus, 1995 ). Global interest in this phenomenon subsequently spread and bullying research began in the United Kingdom, Australia, and the United States ( Griffin and Gross, 2004 ). Usually quantitative in nature, many studies examine bullying prevalence, risk and protective factors, and negative outcomes ( Patton et al., 2017 ). Whilst quantitative research collates key demographic information to show variations in bullying behaviors and tendencies, this dominant bullying literature fails to explain why bullying happens. Nor does it attempt to understand the wider social contexts in which bullying occurs. Qualitative research on the other hand, in particular participatory research, can help shed light on these factors by highlighting the complexities of the contextual and relational aspects of bullying and the particular challenges associated with addressing it. Patton et al. (2017) in their systematic review of qualitative methods used in bullying research, found that the use of such methods can enhance academic and practitioner understanding of bullying.

In this paper, I draw on four bullying studies; one systematic review of both quantitative and qualitative research ( O’Brien, 2009 ) and three empirical qualitative studies ( O’Brien and Moules, 2010 ; O’Brien, 2016 , 2017 ) (see Table 1 below). I discuss how participatory research methodologies, to varying degrees, were used to facilitate bullying knowledge production among teams of young people and adults. Young people in these presented studies were consequently involved in the research process along a continuum of involvement ( Bragg and Fielding, 2005 ). To the far left of the continuum, young people involved in research are referred to as “active respondents” and their data informs teacher practice. To the middle of the continuum sit “students as co-researchers” who work with teachers to explore an issue which has been identified by that teacher. Finally to the right, sit “students as researchers” who conduct their own research with support from teachers. Moving from left to right of the continuum shows a shift in power dynamics between young people and adults where a partnership develops. Young people are therefore recognized as equal to adults in terms of what they can bring to the project from their own unique perspective, that of being a young person now.

Table 1. The studies.

In this paper, I advocate for the active involvement of young people in the research process in order to enhance bullying knowledge. Traditional quantitative studies have a tendency to homogenize young people by suggesting similarity in thinking about what constitutes bullying. However, qualitative studies have demonstrated that regardless of variables, young people understand bullying in different ways so there is a need for further research that starts from these perspectives and focusses on issues that young people deem important. Consequently, participatory research allows for the stories of the collective to emerge without losing the stories of the individual, a task not enabled through quantitative approaches.

What Is Bullying?

Researching school bullying has been problematic and is partly related to the difficulty in defining it ( Espelage, 2018 ). Broadly speaking, bullying is recognized as aggressive, repeated, intentional behavior involving an imbalance of power aimed toward an individual or group of individuals who cannot easily defend themselves ( Vaillancourt et al., 2008 ). In more recent times, “traditional” bullying behaviors have been extended to include cyber-bullying, involving the use of the internet and mobile-phones ( Espelage, 2018 ). Disagreements have been noted in the literature about how bullying is defined by researchers linked to subject discipline and culture. Some researchers for example, disagree about the inclusion or not of repetition in definitions ( Griffin and Gross, 2004 ) and these disagreements have had an impact on interpreting findings and prevalence rates. However, evidence further suggests that young people also view bullying in different ways ( Guerin and Hennessy, 2002 ; Cuadrado-Gordillo, 2012 ; Eriksen, 2018 ). Vaillancourt et al. (2008) explored differences between researchers and young people’s definitions of bullying, and found that children’s definitions were usually spontaneous, and did not always encompass the elements of repetition, power imbalance and intent. They concluded, that children need to be provided with a bullying definition so similarities and comparisons can be drawn. In contrast, Huang and Cornell (2015) found no evidence that the inclusion of a definition effected prevalence rates. Their findings, they suggest, indicate that young people use their own perceptions of bullying when answering self-report questionnaires and they are not influenced by an imposed definition.

Nevertheless, differences in children and young people’s bullying definitions are evident in the research literature and have been explained by recourse to age and stage of development ( Smith et al., 2002 ) and their assumed lack of understanding about what constitutes bullying ( Boulton and Flemington, 1996 ). Naylor et al. (2001) for example, found that younger children think similarly in their definitions of bullying, while Smith et al. (2002) found that 8 year olds did not distinguish as clearly between different forms of behavioral aggression as 14 year olds. Methodological limitations associated with understanding bullying have been identified by Forsberg et al. (2018) and Maunder and Crafter (2018) . These authors postulate that quantitative approaches, although providing crucial insights in understanding bullying, are reliant on pre-defined variables, which can shield some of the complexities that qualitative designs can unravel, as individual experiences of bullying are brought to the fore. Indeed, La Fontaine (1991) suggests that unlike standard self-report questionnaires and other quantitative methods used to collect bullying data, analyzing qualitative data such as those collected from a helpline, enables the voice of young people to be heard and consequently empowers adults to understand bullying on their terms rather than relying solely on interpretations and perceptions of adults. Moore and Maclean (2012) collected survey, as well as interview and focus group data, on victimization occurring on the journey to and from school. They found that what young people determined as victimization varied and was influenced by a multifaceted array of circumstances, some of which adults were unaware of. Context for example, played an important role where certain behaviors in one situation could be regarded as victimization while in another they were not. Specific behaviors including ignoring an individual was particularly hurtful and supporting a friend who was the subject of victimization could lead to their own victimization.

Lee (2006) suggests that some bullying research does not reflect individual experiences, and are thus difficult for participants to relate to. Canty et al. (2016) reiterates this and suggests that when researchers provide young people with bullying definitions in which to position their own experiences, this can mask some of the complexities that the research intends to uncover. Such approaches result in an oversight into the socially constructed and individual experiences of bullying ( Eriksen, 2018 ). Griffin and Gross (2004) further argue that when researchers use vague or ambiguous definitions an “overclassification of children as bullies or victims” (p. 381) ensues. Consequently, quantitative research does not consider children as reliable in interpreting their own lived experiences and therefore some of the interactions they consider as bullying, that do not fit within the conventional definitions, are concealed. This approach favors the adult definition of bullying regarding it as “more reliable” than the definitions of children and young people Canty et al. (2016) . The perceived “seriousness” of bullying has also been explored. Overall, young people and adults are more likely to consider direct bullying (face-to-face actions including hitting, threatening and calling names) as “more serious” than indirect bullying (rumor spreading, social exclusion, forcing others to do something they do not want to do) ( Maunder et al., 2010 ; Skrzypiec et al., 2011 ). This perception of “seriousness,” alongside ambiguous definitions of bullying, has further implications for reporting it. Despite the advice given to young people to report incidents of school bullying ( Moore and Maclean, 2012 ), the literature suggests that many are reluctant to do so ( deLara, 2012 ; Moore and Maclean, 2012 ).

Several factors have been highlighted as to why young people are reluctant to report bullying ( Black et al., 2010 ). deLara (2012) , found apprehension in reporting bullying to teachers due to the fear that they will either not do enough or too much and inadvertently make the situation worse, or fear that teachers will not believe young people. Research also shows that young people are reluctant to tell their parents about bullying due to perceived over-reaction and fear that the bullying will be reported to their school ( deLara, 2012 ; Moore and Maclean, 2012 ). Oliver and Candappa (2007) suggest that young people are more likely to confide in their friends than adults (see also Moore and Maclean, 2012 ; Allen, 2014 ). However, if young people believe they are being bullied, but are unable to recognize their experiences within a predefined definition of bullying, this is likely to impact on their ability to report it.

Research from psychology, sociology, education and other disciplines, utilizing both quantitative and qualitative approaches, have enabled the generation of bullying knowledge to date. However, in order to understand why bullying happens and how it is influenced by wider social constructs there is a need for further qualitative studies, which hear directly from children and young people themselves. The next section of this paper discusses the theoretical underpinnings of this paper, which recognizes that young people are active agents in generating new bullying knowledge alongside adults.

Theoretical Underpinnings – Hearing From Children and Young People

The sociology of childhood ( James, 2007 ; Tisdall and Punch, 2012 ) and children’s rights agenda more broadly ( United Nations Convention on the Rights of the Child, 1989 ) have offered new understandings and methods for research which recognize children and young people as active agents and experts on their own lives. From this perspective, research is conducted with rather than on children and young people ( Kellett, 2010 ).

Participatory methodologies have proven particularly useful for involving young people in research as co-researchers (see for example O’Brien and Moules, 2007 ; Stoudt, 2009 ; Kellett, 2010 ; Spears et al., 2016 ). This process of enquiry actively involves those normally being studied in research activities. Previously, “traditional” researchers devalued the experiences of research participants arguing that due to their distance from them, they themselves are better equipped to interpret these experiences ( Beresford, 2006 ). However, Beresford (2006) suggests that the shorter the distance between direct experience and interpretation, the less distorted and inaccurate the resulting knowledge is likely to be. Jones (2004) further advocates that when young people’s voices are absent from research about them the research is incomplete. Certainly Spears et al. (2016) , adopted this approach in their study with the Young and Well Cooperative Research Centre (CRC) in Australia. Young people played an active role within a multidisciplinary team alongside researchers, practitioners and policymakers to co-create and co-evaluate the learning from four marketing campaigns for youth wellbeing through participatory research. Through this methodological approach, findings show that young people were able to reconceptualize mental health and wellbeing from their own perspectives as well as share their lived experiences with others ( Spears et al., 2016 ). Bland and Atweh (2007) , Ozer and Wright (2012) , highlight the benefits afforded to young people through this process, including participating in dialog with decision-makers and bringing aspects of teaching and learning to their attention.

Against this background, data presented for this paper represents findings from four studies underpinned by the ethos that bullying is socially constructed and is best understood by exploring the context to which it occurs ( Schott and Sondergaard, 2014 ; Eriksen, 2018 ). This socially constructed view focusses on the evolving positions within young people’s groups, and argues that within a bullying situation sometimes a young person is the bully, sometimes the victim and sometimes the bystander/witness, which contrasts the traditional view of bullying ( Schott and Sondergaard, 2014 ). The focus therefore is on group relationships and dynamics. For that reason, Horton (2011) proposes that if bullying is an extensive problem including many young people, then focusing entirely on personality traits will not generate new bullying knowledge and will be problematic in terms of interventions. It is important to acknowledge that this change in focus and view of bullying and how it is manifested in groups, does not negate the individual experiences of bullying rather the focus shifts to the process of being accepted, or not, by the group ( Schott and Sondergaard, 2014 ).

The Studies

This section provides a broad overview of the four included studies underpinned by participatory methodologies. Table 1 presents the details of each study. Young people were involved in the research process as respondents, co-researchers and commissioners of research, along a continuum as identified by Bragg and Fielding (2005) . This ranged from “active respondents” to the left of the continuum, “students as co-researchers” in the middle and “students as researchers” to the right of the continuum. Young people were therefore recognized as equal to adults in terms of what they can bring to the project from their own unique perspectives ( Bradbury-Jones et al., 2018 ).

A key finding from study one ( O’Brien, 2009 ) was the lack of voice afforded to young people through the research process and can be seen to reflect the far left of Bragg and Fielding (2005) continuum, as young people were not directly involved as “active respondents” but their views were included in secondary data analysis and informed the studies that followed. For example, the quantitative studies used an agreed academic definition of bullying which may or may not have influenced how young participants defined bullying within the studies. On the other hand, the qualitative study involved a group of students in deciding which questions to ask of the research participants and in interpreting the findings.

In contrast, study two ( O’Brien and Moules, 2010 ) was commissioned and led by a group of young people called PEAR (Public health, Education, Awareness, Researchers), who were established to advise on public health research in England. PEAR members were based in two large English cities and comprised 20 young people aged between 13 and 20 years. The premise of the study was that PEAR members wanted to commission research into cyber bullying and the effects this has on mental health from the perspectives of young people rather than adult perspectives. This project was innovative as young people commissioned the research and participated as researchers ( Davey, 2011 ) and can be seen to reflect the middle “students as co-researchers” as well as moving toward to right “students as researchers” of Bragg and Fielding (2005) continuum. Although the young people did not carry out the day-to-day work on the project, they were responsible for leading and shaping it. More importantly, the research topic and focus were decided with young people and adults together.

Study three ( O’Brien, 2016 ) involved five self-selecting students from an independent day and boarding school who worked with me to answer this question: What do young people in this independent day and boarding school view as the core issue of bullying in the school and how do they want to address this? These students called themselves R4U (Research for You) with the slogan researching for life without fear . Three cycles of Participatory Action Research (PAR) ensued, where decision making about direction of the research, including methods, analysis and dissemination of findings were made by the research team. As current students of the school, R4U had a unique “insider knowledge” that complemented my position as the “academic researcher.” By working together to generate understanding about bullying at the school, the findings thus reflected this diversity in knowledge. As the project evolved so too did the involvement of the young researchers and my knowledge as the “outsider” (see O’Brien et al., 2018a for further details). Similar to study two, this project is situated between the middle: “students as co-researchers” and the right: “students as researchers” of Bragg and Fielding (2005) continuum.

Study four ( O’Brien, 2017 ) was small-scale and involved interviewing four young people who were receiving support from a charity providing therapeutic and educational support to young people who self-exclude from school due to anxiety, as a result of bullying. Self-exclusion, for the purposes of this study, means that a young person has made a decision not to go to school. It is different from “being excluded” or “truanting” because these young people do not feel safe at school and are therefore too anxious to attend. Little is known about the experiences of young people who self-exclude due to bullying and this study helped to unravel some of these issues. This study reflects the left of Bragg and Fielding (2005) continuum where the young people were involved as “active respondents” in informing adult understanding of the issue.

A variety of research methods were used across the four studies including questionnaires, interviews and focus groups (see Table 1 for more details). In studies two and three, young researchers were fundamental in deciding the types of questions to be asked, where they were asked and who we asked. In study three the young researchers conducted their own peer-led interviews. The diversity of methods used across the studies are a strength for this paper. An over-reliance on one method is not portrayed and the methods used reflected the requirements of the individual studies.

Informed Consent

Voluntary positive agreement to participate in research is referred to as “consent” while “assent,” refers to a person’s compliance to participate ( Coyne, 2010 ). The difference in these terms are normally used to distinguish the “legal competency of children over and under 16 years in relation to research.” ( Coyne, 2010 , 228). In England, children have a legal right to consent so therefore assent is non-applicable ( Coyne, 2010 ). However, there are still tensions surrounding the ability of children and young people under the age of 18 years to consent in research which are related to their vulnerability, age and stage of development ( Lambert and Glacken, 2011 ). The research in the three empirical studies (two, three and four) started from the premise that all young participants were competent to consent to participate and took the approach of Coyne (2010) who argues that parental/carer consent is not always necessary in social research. University Research Ethics Committees (RECs) are nonetheless usually unfamiliar with the theoretical underpinnings that children are viewed as social actors and generally able to consent for themselves ( Lambert and Glacken, 2011 ; Fox, 2013 ; Parsons et al., 2015 ).

In order to ensure the young people in these reported studies were fully informed of the intentions of each project and to adhere to ethical principles, age appropriate participant information sheets were provided to all participants detailing each study’s requirements. Young people were then asked to provide their own consent by signing a consent form, any questions they had about the studies were discussed. Information sheets were made available to parents in studies three and four. In study two, the parents of young people participating in the focus groups were informed of the study through the organizations used to recruit the young people. My full contact details were provided on these sheets so parents/carers could address any queries they had about the project if they wished. When young people participated in the online questionnaire (study two) we did not know who they were so could not provide separate information to parents. Consequently, all participants were given the opportunity to participate in the research without the consent of their parents/carers unless they were deemed incompetent to consent. In this case the onus was on the adult (parent or carer for example) to prove incompetency ( Alderson, 2007 ). Favorable ethical approval, including approval for the above consent procedures, was granted by the Faculty Research Ethics Committee at Anglia Ruskin University.

In the next section I provide a synthesis of the findings across the four studies before discussing how participatory research with young people can offer new understandings of bullying and its impacts on young people.

Although each study was designed to answer specific bullying research questions, the following key themes cut across all four studies 1 :

• Bullying definitions

◦ Behaviors

• Impact of bullying on victim

• Reporting bullying

Bullying Definitions

Young people had various understandings about what they considered bullying to be. Overall, participants agreed that aggressive direct behaviors, mainly focusing on physical aggression, constituted bullying:

“…if someone is physically hurt then that is bullying straight away.” (Female, study 3).

“I think [cyber-bullying is] not as bad because with verbal or physical, you are more likely to come in contact with your attacker regularly, and that can be disturbing. However, with cyber-bullying it is virtual so you can find ways to avoid the person.” (Female, study 2).

Name-calling was an ambiguous concept, young people generally believed that in isolation name-calling might not be bullying behavior or it could be interpreted as “joking” or “banter”:

“I never really see any, a bit of name calling and taking the mick but nothing ever serious.” (Male, study 3).

The concept of “banter” or “joking” was explored in study three as a result of the participatory design. Young people suggested “banter” involves:

“…a personal joke or group banter has no intention to harm another, it is merely playful jokes.” (Female, study 3).

However, underpinning this understanding of “banter” was the importance of intentionality:

“Banter saying things bad as a joke and everyone knows it is a joke.” (Male, study 3).

“Banter” was thus contentious when perception and reception were ambiguous. In some cases, “banter” was considered “normal behavior”:

“…we’ve just been joking about, but it’s never been anything harsh it’s just been like having a joke…” (Male, study 3).

The same view was evident in relation to cyber-bullying. Some participants were rather dismissive of this approach suggesting that it did not exist:

“I don’t really think it exists. If you’re being cyber-“bullied” then there is something wrong with you- it is insanely easy to avoid, by blocking people and so on. Perhaps it consists of people insulting you online?” (Male, study 2).

When young people considered additional factors added to name calling such as the type of name-calling, or aspects of repetition or intention, then a different view was apparent.

“…but it has to be constant it can’t be a single time because that always happens.” (Male, study 3).

Likewise with words used on social media, young people considered intentionality in their consideration of whether particular behaviors were bullying, highlighting important nuances in how bullying is conceptualized:

“Some people they don’t want to sound cruel but because maybe if you don’t put a smiley face on it, it might seem cruel when sometimes you don’t mean it.” (Female, study 2).

Study one also found that young people were more likely to discuss sexist or racist bullying in interviews or focus groups but this information was scarce in the questionnaire data. This is possibly as a result of how the questions were framed and the researchers’ perspectives informing the questions.

Evident across the four studies was the understanding young people had about the effects of continuous name-calling on victims:

“…you can take one comment, you can just like almost brush it off, but if you keep on being bullied and bullied and bullied then you might kind of think, hang on a minute, they’ve taken it a step too far, like it’s actually become more personal, whereas just like a cheeky comment between friends it’s become something that’s more serious and more personal and more annoying or hurtful to someone.” (Female, study 3).

“Cyber-bullying is basically still verbal bullying and is definitely psychological bullying. Any bullying is psychological though, really. And any bullying is going to be harmful.” (Female, study 2).

Aspects of indirect bullying (social exclusion) were features of studies one and three. For the most part, the research reviewed in study one found that as young people got older they were less likely to consider characteristics of social exclusion in their definitions of bullying. In study three, when discussing the school’s anti-bullying policy, study participants raised questions about “ isolating a student from a friendship group .” Some contested this statement as a form of bullying:

“…. there is avoiding, as in, not actively playing a role in trying to be friends which I don’t really see as bullying I see this as just not getting someone to join your friendship group. Whereas if you were actually leaving him out and rejecting him if he tries to be friends then I think I would see that as malicious and bullying.” (Male, study 3).

“Isolating a student from a friendship group – I believe there are various reasons for which a student can be isolated from a group – including by choice.” (Female, study 3).

Cyber-bullying was explored in detail in study two but less so in the other three studies. Most study two participants considered that cyber-bullying was just as harmful, or in some cases worse than, ‘traditional’ bullying due to the use of similar forms of “harassment,” “antagonizing,” “tormenting,” and ‘threatening’ through online platforms. Some young people believed that the physical distance between the victim and the bully is an important aspect of cyber-bullying:

“I think it’s worse because people find it easier to abuse someone when not face to face.” (Male, study 2).

“I think it could be worse, because lots of other people can get involved, whereas when it’s physical bullying it’s normally just between one or two or a smaller group, things could escalate too because especially Facebook, they’ve got potential to escalate.” (Female, study 2).

Other participants in study two spoke about bullying at school which transfers to an online platform highlighting no “escape” for some. In addition, it was made clearer that some young people considered distancing in relation to bullying and how this influences perceptions of severity:

“…when there’s an argument it can continue when you’re not at school or whatever and they can continue it over Facebook and everyone can see it then other people get involved.” (Female, study 2).

“I was cyber-bullied on Facebook, because someone put several hurtful comments in response to my status updates and profile pictures. This actually was extended into school by the bully…” (Male, study 2).

Impact of Bullying on Victim

Although bullying behaviors were a primary consideration of young people’s understanding of bullying, many considered the consequences associated with bullying and in particular, the impact on mental health. In these examples, the specifics of the bullying event were irrelevant to young people and the focus was on how the behavior was received by the recipient.

In study two, young people divulged how cyber-bullying had adversely affected their ability to go to school and to socialize outside school. Indeed some young people reported the affects it had on their confidence and self-esteem:

“I developed anorexia nervosa. Although not the single cause of my illness, bullying greatly contributed to my low self-esteem which led to becoming ill.” (Female, study 2).

“It hurts people’s feelings and can even lead to committing suicide….” (Female, study 2).

Across the studies, young people who had been bullied themselves shared their individual experiences:

“….you feel insecure and it just builds up and builds up and then in the end you have no self-confidence.” (Female, study 2).

“…it was an everyday thing I just couldn’t take it and it was causing me a lot of anxiety.” (Male, study 4).

“I am different to everyone in my class …. I couldn’t take it no more I was upset all the time and it made me feel anxious and I wasn’t sleeping but spent all my time in bed being sad and unhappy.” (Male, study 4).

Young people who had not experienced bullying themselves agreed that the impact it had on a person was a large determiner of whether bullying had happened:

“When your self-confidence is severely affected and you become shy. Also when you start believing what the bullies are saying about you and start to doubt yourself.” (Female, study 3).

“…it makes the victim feel bad about themselves which mostly leads to depression and sadness.” (Male, study 2).

Further evidence around the impact of bullying was apparent in the data in terms of how relational aspects can affect perceived severity. In the case of cyber-bullying, young people suggested a sense of detachment because the bullying takes place online. Consequently, as the relational element is removed bullying becomes easier to execute:

“…because people don’t have to face them over a computer so it’s so much easier. It’s so much quicker as well cos on something like Facebook it’s not just you, you can get everyone on Facebook to help you bully that person.” (Female, study 2).

“Due to technology being cheaper, it is easier for young people to bully people in this way because they don’t believe they can be tracked.” (Male, study 2).

“The effects are the same and often the bullying can be worse as the perpetrator is unknown or can disguise their identity. Away from the eyes of teachers etc., more can be done without anyone knowing.” (Female, study 2).

Relational aspects of bullying were further highlighted with regards to how “banter” was understood, particularly with in-group bullying and how the same example can either be seen as “banter” or bullying depending on the nature of the relationship:

“…we’ve just been joking about, but it’s never been anything harsh it’s just been like having a joke. well, I haven’t done it but I’ve been in a crowd where people do it, so I don’t want to get involved just in case it started an argument.” (Female, study 3).

“But it also depends…who your groups with, for example, if I spoke to my friends from [School]… I wouldn’t like use taboo language with them because to them it may seem inappropriate and probably a bit shocked, but if I was with my friends outside of school we use taboo language, we’ll be ourselves and we’ll be comfortable with it, and if a stranger walked past and heard us obviously they’d be thinking that we’re being bullied ourselves.” (Female, study 3).

Furthermore, how individuals are perceived by others tended to influence whether they were believed or not. In study four for example, participants suggested that who the bullies were within the school might have impacted how complaints were acted upon by school officials:

“When I went to the school about it, the students said I had attacked them – all eight of them! I just realized that no one believes me….” (Female, study 4).

While in study three, a characteristic of bullying was the influence the aggressor has over the victim:

“When the victim starts to feel in danger or start to fear the other person. Consequently he or she tries to avoid the bad guy (or girl!)” (Male, study 3).

These relational and contextual issues also influenced a young person’s ability to report bullying.

Reporting Bullying

Young people were more likely to report bullying when they considered it was ‘serious’ enough. Just under half of participants in study two sought emotional/practical support if they worried about, or were affected by cyber-bullying, with most talking to their parents. In study three, young people were less likely to seek support but when they did, most went to their teachers. In study four, all participants reported bullying in school where they did not feel supported.

Fear of making the bullying worse was captured across the studies as a reason for not reporting it:

“I’m scared that if I tell then the bullying will still go on and they will do more.” (Female, study 3).

“The bully might bully you if he finds out.” (Male, study 3).

Being able to deal with the incident themselves was also a reason for non-reporting:

“…it’s embarrassing and not necessary, my friends help me through it, adults never seem to understand.” (Female, study 2).

“I don’t tend to talk to anyone about it, I just keep it to myself and obviously that’s the worst thing you should ever do, you should never keep it to yourself, because I regret keeping it to myself to be honest….” (Female, study 3).

“…but I think I’d deal with it myself ‘cos. I was quite insecure but now I’m quite secure with myself, so I’ll sort it out myself. I think it’s just over time I’ve just sort of hardened to it.” (Male, study 3).

Most young people seeking support for bullying said they spoke to an adult but the helpfulness of this support varied. This finding is important for understanding relationships between young people and adults. Those who felt supported by their teachers for example, suggested that they took the time to listen and understood what they were telling them. They also reassured young people who in turn believed that the adult they confided in would know what to do:

“So I think the best teacher to talk to is [Miss A] and even though people are scared of her I would recommend it, because she’s a good listener and she can sense when you don’t want to talk about something, whereas the other teachers force it out of you.” (Female, study 3).

“My school has had assemblies about cyber-bullying and ways you can stop it or you can report it anonymously…. you can write your name or you can’t, it’s all up to YOU.” (Male, study 2).

Others however had a negative experience of reporting bullying and a number of reasons were provided as to why. Firstly, young people stated that adults did not believe them which made the bullying worse on some level:

“I went to the teachers a couple of times but, no, I don’t think they could do anything. I did sort of go three times and it still kept on going, so I just had to sort of deal with it and I sort of took it on the cheek….” (Male, study 3).

Secondly, young people suggested that adults did not always listen to their concerns, or in some cases did not take their concerns seriously enough:

“…I had had a really bad day with the girls so I came out and I explained all this to my head of year and how it was affecting me but instead of supporting me he put me straight into isolation.” (Male, study 4).

“I could understand them thinking I maybe got the wrong end of the stick with one incident but this was 18 months of me constantly reporting different incidents.” (Female, study 4).

“If cyber-bullying is brought to our school’s attention, usually, they expect printed proof of the situation and will take it into their own hand depending on its seriousness. However this is usually a couple of detentions. And it’s just not enough.” (Female, study 2).

Finally, some young people suggested that teachers did not always know what to do when bullying concerns were raised and consequently punished those making the complaint:

“I think I would have offered support instead of punishment to someone who was suffering with anxiety. I wouldn’t have seen anxiety as bad behavior I think that’s quite ignorant but they saw it as bad behavior.” (Male, study 4).

It is worth reiterating, that the majority of young people across the studies did not report bullying to anybody , which further underscores the contextual issues underpinning bullying and its role in enabling or disabling bullying behaviors. Some considered it was “pointless” reporting the bullying and others feared the situation would be made worse if they did:

“My school hide and say that bullying doesn’t go on cos they don’t wanna look bad for Ofsted.” (Male, study 2).

“My school is oblivious to anything that happens, many things against school rules happen beneath their eyes but they either refuse to acknowledge it or are just not paying attention so we must suffer.” (Female, study 2).

“That’s why I find that when you get bullied you’re scared of telling because either, in most cases the teacher will – oh yeah, yeah, don’t worry, we’ll sort it out and then they don’t tend to, and then they get bullied more for it.” (Female, study 3).

Young people were concerned that reporting bullying would have a negative impact on their friendship groups. Some were anxious about disrupting the status quo within:

“I think everyone would talk about me behind my back and say I was mean and everyone would hate me.” (Female, study 3).

Others expressed concern about the potential vulnerability they were likely to experience if they raised concerns of bullying:

“I was worried it might affect my other friendships.”(Boy, study 2).

“I’m scared that if I tell, then the bullying will still go on and they will do more.” (Female, study 3).

“….because they might tell off the bullies and then the bullies will like get back at you.” (Female, study 3).

These findings underscore the importance of contextual and relational factors in understanding bullying from the perspectives of young people and how these factors influence a young person’s ability or willingness to report bullying.

Finally one young person who had self-excluded from school due to severe bullying suggested that schools:

“…need to be looking out for their students’ mental wellbeing – not only be there to teach them but to support and mentor them. Keep them safe really… I missed out on about three years of socializing outside of school because I just couldn’t do it. I think it’s important that students are encouraged to stand up for each other.” (Female, study 4).

The studies presented in this paper illustrate the multitude of perceptions underpinning young people’s understandings of what constitutes bullying, both in terms of the behavior and also the impact that this behavior has on an individual. In turn, the ambiguity of what constitutes bullying had an impact on a young person’s ability to seek support. Discrepancies in bullying perceptions within and between young people’s groups are shown, highlighting the fluid and changing roles that occur within a bullying situation. Findings from quantitative studies have demonstrated the differing perceptions of bullying by adults and young people (see for example Smith et al., 2002 ; Vaillancourt et al., 2008 ; Maunder et al., 2010 ; Cuadrado-Gordillo, 2012 ). However, by combining findings from participatory research, new understandings of the relational and contextual factors important to young people come to the fore.

Young people participating in these four studies had unique knowledge and experiences of bullying and the social interactions of other young people in their schools and wider friendship groups. The underpinning participatory design enabled me to work alongside young people to analyze and understand their unique perspectives of bullying in more detail. The research teams were therefore able to construct meaning together, based not entirely on our own assumptions and ideologies, but including the viewpoint of the wider research participant group ( Thomson and Gunter, 2008 ). Together, through the process of co-constructing bullying knowledge, we were able to build on what is already known in this field and contribute to the view that bullying is socially constructed through the experiences of young people and the groups they occupy ( Schott and Sondergaard, 2014 ).

With regards to understanding what bullying is, the findings from these studies corroborate those of the wider literature from both paradigms of inquiry (for example Naylor et al., 2001 ; Canty et al., 2016 ); that being the discrepancies in definitions between adults and young people and also between young people themselves. Yet, findings here suggest that young people’s bullying definitions are contextually and relationally contingent. With the exception of physical bullying, young people did not differentiate between direct or indirect behaviors, instead they tended to agree that other contextual and relational factors played a role in deciding if particular behaviors were bullying (or not). The participatory research design enabled reflection and further investigation of the ideas that were particularly important to young people such as repetition and intentionality. Repetition was generally seen as being indicative of bullying being “serious,” and therefore more likely to be reported, and without repetition, a level of normality was perceived. This finding contradicts some work on bullying definitions, Cuadrado-Gordillo (2012) for example found that regardless of the role played by young people in a bullying episode (victim, aggressor or witness), the criteria of ‘repetition’ was not important in how they defined bullying.

Relational factors underpinning young people’s perception of bullying and indeed it’s “seriousness” were further reflected in their willingness or otherwise to report it. Fear of disrupting the status quo of the wider friendship group, potentially leading to their own exclusion from the group, was raised as a concern by young people. Some were concerned their friends would not support them if they reported bullying, while others feared further retaliation as a result. Friendship groups have been identified as a source of support for those who have experienced bullying and as a protective factor against further bullying ( Allen, 2014 ). Although participants did not suggest their friendship groups are unsupportive it is possible that group dynamics underscore seeking (or not) support for bullying. Other literature has described such practices as evidence of a power imbalance ( Olweus, 1995 ; Cuadrado-Gordillo, 2012 ) but young people in these studies did not describe these unequal relationships in this way and instead focused on the outcomes and impacts of bullying. Indeed Cuadrado-Gordillo (2012) also found that young people in their quantitative study did not consider “power imbalance” in their understanding of bullying and were more likely to consider intention. This paper, however, underscores the relational aspects of definitions of bullying and, how the dynamics of young people’s friendships can shift what is understood as bullying or not. Without such nuances, some behaviors may be overlooked as bullying, whereas other more obvious behaviors draw further attention. This paper also shows that contextual issues such as support structures can shift how young people see bullying. Contextual factors were evident across the four studies through the recognition of bullying being enabled or disabled by institutional factors, including a school’s ability to respond appropriately to bullying concerns. Young people suggested that schools could be influenced by bullies, perceiving them as non-threatening and consequently not dealing appropriately with the situation. Indeed some young people reported that their schools placed the onus on them as victims to change, consequently placing the “blame” on victims instead. These findings raise questions about who young people feel able to confide in about bullying as well as issues around training and teacher preparedness to deal with bullying in schools. Evidenced in these four studies, is that young people feel somewhat disconnected from adults when they have bullying concerns. Those who did report bullying, identified particular individuals they trusted and knew would support them. Novick and Isaacs (2010) identified teachers who young people felt comfortable in approaching to report bullying and described them as “most active, engaged and responsive.” (p. 291). The bullying literature suggests that as young people get older they are more likely to confide in friends than adults ( Moore and Maclean, 2012 ; Allen, 2014 ). However, findings from this paper indicate that although fewer young people reported bullying, those who did confided in an adult. Young people have identified that a variety of supports are required to tackle bullying and that adults need to listen and work with them so nuanced bullying behaviors are not recognized as “normal” behaviors. Within the data presented in this paper, “banter” was portrayed as “normal” behavior. Young people did not specify what behaviors they regarded as “banter,” but suggested that when banter is repeated and intentional the lines are blurred about what is bullying and what is banter.

Exploring bullying nuances in this paper, was enhanced by the involvement of young people in the research process who had a unique “insider” perspective about what it is like to be a young person now and how bullying is currently affecting young people. In studies one and four, young people were “active respondents” ( Bragg and Fielding, 2005 ) and provided adults with their own unique perspectives on bullying. It could be argued that study one did not involve the participation of young people. However, this study informed the basis of the subsequent studies due to the discrepancies noted in the literature about how bullying is understood between adults and young people, as well as the lack of young people’s voice and opportunity to participate in the reviewed research. Accordingly, young people’s data as “active respondents” informed adult understanding and led to future work involving more active research engagement from other young people. Participation in study four provided an opportunity for young people to contribute to future participatory research based on lived experiences as well as informing policy makers of the effects bullying has on the lives of young people ( O’Brien, 2017 ). In studies two and three, young people were involved further along Bragg and Fielding (2005) continuum as “co-researchers” and “students as researchers” with these roles shifting and moving dependent on the context of the project at the time ( O’Brien et al., 2018a ). These young researchers brought unique knowledge to the projects ( Bradbury-Jones et al., 2018 ) that could not be accessed elsewhere. Perspectives offered by the young researchers supported adults in understanding more about traditional and cyber-bullying from their perspectives. Furthermore, this knowledge can be added to other, quantitative studies to further understand why bullying happens alongside bullying prevalence, risk and protective factors, and negative outcomes.

Findings from the four studies offer an alternative perspective to how bullying is understood by young people. Complexities in defining bullying have been further uncovered as understanding is informed by individual factors, as well as wider social and relational contexts ( Horton, 2011 ; Schott and Sondergaard, 2014 ). This has implications for the type of support young people require. This paper highlights how definitions of bullying shift in response to relational and contextual aspects deemed important to young people. Because of this, further nuances were uncovered through the research process itself as the respective studies showed discrepancies in bullying perceptions within and between young people’s groups.

These understandings can act as a starting point for young people and adults to collaborate in research which seeks to understand bullying and the context to which it occurs. Furthermore, such collaborations enable adults to theorize and understand the complexities associated with bullying from the perspective of those at the center. There is a need for additional participatory research projects involving such collaborations where adults and young people can learn from each other as well as combining findings from different methodologies to enable a more comprehensive picture of the issues for young people to emerge. Further research is needed to unravel the complexities of bullying among and between young people, specifically in relation to the contextual and relational factors underscoring perceptions of bullying.

Data Availability

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

Ethical approval was granted for all four studies from the Faculty of Health, Education, Medicine and Social Care at the Anglia Ruskin University. The research was conducted on the premise of Gillick competency meaning that young people (in these studies over the age of 12 years) could consent for themselves to participate. Parents/carers were aware the study was happening and received information sheets explaining the process.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

These four studies were conducted at the Anglia Ruskin University. Study one was part of a wider masters degree funded by the Anglia Ruskin University, Study two was funded by a group of young people convened by the National Children’s Bureau with funding from the Wellcome Trust (United Kingdom). Study three was a wider Doctoral study funded by the Anglia Ruskin University and Study four was also funded by the Anglia Ruskin University.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

I would like to thank Dr. Grace Spencer, Ruskin Fellow at the Anglia Ruskin University for providing the critical read of this manuscript and offering constructive feedback. I would also like to thank the two independent reviewers for their feedback on the drafts of this manuscript.

• ^ These findings focus on perceptions and data from the young people in the four studies. For a full discussion on adult perceptions please refer to the individual studies.

Alderson, P. (2007). Competent children? Minors’ consent to health care treatment and research. Soc. Sci. Med. 65, 2272–2283. doi: 10.1016/j.socscimed.2007.08.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Allen, M. (2014). Local Action on Health Inequalities: Building Children and Young People’s Resilience in Schools , London: Public Health England.

Google Scholar

Beresford, P. (2006). Making the connections with direct experience: from the western front to user-controlled research. Educ. Action Res. 14, 161–170.

Black, S., Weinles, D., and Washington, E. (2010). Victim strategies to stop bullying. Youth Violence Juv. Justice 8, 138–147. doi: 10.1177/1541204009349401

CrossRef Full Text | Google Scholar

Bland, D., and Atweh, B. (2007). Students as researchers: engaging students’ voices in PAR. Educ. Action Res. 15, 337–349. doi: 10.1080/09650790701514259

Boulton, M. J., and Flemington, I. (1996). The effects of a short video intervention on secondary school Pupils’ involvement in definitions of and attitudes towards bullying. Sch. Psychol. Int. 17, 331–345. doi: 10.1177/0143034396174003

Bradbury-Jones, C., Isham, L., and Taylor, J. (2018). The complexities and contradictions in participatory research with vulnerable children and young people: a qualitative systematic review. Soc. Sci. Med. 215, 80–91. doi: 10.1016/j.socscimed.2018.08.038

Bragg, S., and Fielding, M. (2005). “It’s an equal thing. It’s about achieving together: student voices and the possibility of a radical collegiality,” in Improving Schools Through Collaborative Enquiry , eds H. Street, and J. Temperley, (London: Continuum), 105–135.

Braun, V., and Clarke, V. (2006). Using thematic analysis in Psychology. Qual. Res. Psychol. 3, 77–101.

Canty, J., Stubbe, M., Steers, D., and Collings, S. (2016). The trouble with bullying–deconstructing the conventional definition of bullying for a child-centred investigation into Children’s use of social media. Child. Soc. 30, 48–58. doi: 10.1111/chso.12103

Coyne, I. (2010). Research with children and young people: the issue of parental (proxy) consent. Child. Soc. 24, 227–237.

Cuadrado-Gordillo, I. (2012). Repetition, power imbalance, and intentionality: do these criteria conform to teenagers’ perception of bullying? A role-based analysis. J. Interpers. Violence 27, 1889–1910. doi: 10.1177/0886260511431436

Davey, C. (2011). Evaluation of the PEAR Project. London: National Children’s Bureau.

deLara, E. W. (2012). Why adolescents Don’t disclose incidents of bullying and harassment. J. Sch. Violence 11, 288–305. doi: 10.1080/15388220.2012.705931

Eriksen, I. M. (2018). The power of the word: students’ and school staff’s use of the established bullying definition. Educ. Res. 60, 157–170. doi: 10.1080/00131881.2018.1454263

Espelage, D. L. (2018). Understanding the complexity of school bully involvement. Chautauqua J. 2:20.

Forsberg, C., Wood, L., Smith, J., Varjas, K., Meyers, J., Jungert, T., et al. (2018). Students’ views of factors affecting their bystander behaviors in response to school bullying: a cross-collaborative conceptual qualitative analysis. Res. Pap. Educ. 33, 127–142. doi: 10.1080/02671522.2016.1271001

Fox, R. (2013). Resisting participation: critiquing participatory research methodologies with young people. J. Youth Stud. 16, 986–999. doi: 10.1080/13676261.2013.815698

Griffin, R. S., and Gross, A. M. (2004). Childhood bullying: current empirical findings and future directions for research. Aggr. Violent Behav. 9, 379–400. doi: 10.1016/s1359-1789(03)00033-8

Guerin, S., and Hennessy, E. (2002). Pupils’ definitions of bullying. Eur. J. Psychol. Educ. 17, 249–261. doi: 10.1007/bf03173535

Horton, P. (2011). School bullying and social and moral orders. Child. Soc. 25, 268–277. doi: 10.1111/j.1099-0860.2011.00377.x

Huang, F. L., and Cornell, D. G. (2015). The impact of definition and question order on the prevalence of bullying victimization using student self-reports. Psychol. Assess. 27:1484. doi: 10.1037/pas0000149

James, A. (2007). Giving voice to children’s voices: practices and problems, pitfalls and potentials. Am. Anthropol. 109, 261–272. doi: 10.1525/aa.2007.109.2.261

Jones, A. (2004). “Involving children and yong people as researchers,” in Doing Research with Children and Young People , eds S. Fraser, V. Lewis, S. Ding, M. Kellett, and C. Robinson, (London: Sage Publications), 113–130.

Kellett, M. (2010). Small shoes, Big Steps! Empowering children as active researchers. Am. J. Commun. Psychol. 46, 195–203. doi: 10.1007/s10464-010-9324-y

La Fontaine, J. (1991). Bullying: The Child’s View – an Analysis of Telephone Calls to ChildLIne about Bullying. London: Calouste Gulbenkian Foundation.

Lambert, V., and Glacken, M. (2011). Engaging with children in research: theoretical and practical implications of negotiating informed consent/assent. Nurs. Ethics 18, 781–801. doi: 10.1177/0969733011401122

Lee, C. (2006). Exploring teachers’ definitions of bullying. Emot. Behav. Diffic. 11, 61–75. doi: 10.1080/13632750500393342

Maunder, R. E., and Crafter, S. (2018). School bullying from a sociocultural perspective. Aggr. Violent Behav. 38, 13–20. doi: 10.1016/j.avb.2017.10.010

Maunder, R. E., Harrop, A., and Tattersall, A. J. (2010). Pupil and staff perceptions of bullying in secondary schools: comparing behavioural definitions and their perceived seriousness. Educ. Res. 52, 263–282. doi: 10.1080/00131881.2010.504062

Moore, S., and Maclean, R. (2012). Victimization, friendship and resilience: crossing the land in-between. Pastor. Care Educ. 30, 147–163. doi: 10.1080/02643944.2012.679956

Naylor, P., Cowie, H., and del Rey, R. (2001). Coping strategies of secondary school children in response to being bullied. Child Psychol. Psychiatry Rev. 6, 114–120. doi: 10.1111/j.1469-7610.2009.02137.x

Novick, R. M., and Isaacs, J. (2010). Telling is compelling: the impact of students reports of bullying on teacher intervention. Educ. Psychol. 30, 283–296. doi: 10.1080/01443410903573123

O’Brien, N. (2009). Secondary school teachers’ and pupils’ definitions of bullying in the UK: a systematic review. Evid. Policy 5, 399–426.

PubMed Abstract | Google Scholar

O’Brien, N. (2014). “I Didn’t Want to be Known as a Snitch”: Using PAR to Explore Bullying in a Private day and Boarding School. Childhood Remixed. Conference Edition. Suffolk: University Campus Suffolk, 86–96.

O’Brien, N. (2016). To ‘Snitch’ or Not to ‘Snitch’? Using PAR to Explore Bullying in a Private Day and Boarding School. Available at: http://arro.anglia.ac.uk/700970/ (accessed September 20, 2018).

O’Brien, N. (2017). An Exploratory Study of Bullied Young People’s Self-Exclusion from School. Evidence: Presented at Meetings of the All Party Parliamentary Group on Bullying 2011-2016. Project Report. All Party Parliamentary Group on Bullying. Available at: http://arro.anglia.ac.uk/id/eprint/702024 (accessed September 20, 2018).

O’Brien, N., and Moules, T. (2007). So round the spiral again: a reflective participatory research project with children and young people. Educ. Action Res. J. 15, 385–402. doi: 10.1080/09650790701514382

O’Brien, N., and Moules, T. (2010). The Impact of Cyber-Bullying on Young People’s Mental Health. Project Report. Chelmsford: Anglia Ruskin University.

O’Brien, N., and Moules, T. (2013). Not sticks and stones but tweets and texts: findings from a national cyberbullying project. Pastor. Care Educ. 31, 53–65. doi: 10.1080/02643944.2012.747553

O’Brien, N., Moules, T., and Munn-Giddings, C. (2018a). “Negotiating the research space between young people and adults in a PAR study exploring school bullying,” in Reciprocal Relationships and Well-Being: Implications for Social Work and Social Policy , eds M. Torronen, C. Munn-Giddings, and L. Tarkiainen, (Oxon: Routledge), 160–175. doi: 10.4324/9781315628363-11

O’Brien, N., Munn-Giddings, C., and Moules, T. (2018b). The repercussions of reporting bullying: some experiences of students at an independent secondary school. Pastor. Care Educ. 36, 29–43. doi: 10.1080/02643944.2017.1422004

O’Brien, N., Munn-Giddings, C., and Moules, T. (2018c). The Ethics of Involving Young People Directly in the Research Process. Childhood Remixed. Conference Edition , 115–128. Available at: www.uos.ac.uk/content/centre-for-study-children-childhood (accessed May 2018).

Oliver, C., and Candappa, M. (2007). Bullying and the politics of ‘telling’. Oxford Rev. Educ. 33, 71–86. doi: 10.1080/03054980601094594

Olweus, D. (1995). Bullying or peer abuse at school: facts and intervention. Curr. Dir. Psychol. Sci. 4, 196–200. doi: 10.1111/1467-8721.ep10772640

Ozer, E. J., and Wright, D. (2012). Beyond school spirit: the effects of youth-led participatory action research in two urban high schools. J. Res. Adolesc. 22, 267–283. doi: 10.1111/j.1532-7795.2012.00780.x

Parsons, S., Abbott, C., McKnight, L., and Davies, C. (2015). High risk yet invisible: conflicting narratives on social research involving children and young people, and the role of research ethics committees. Br. Educ. Res. J. 41, 709–729. doi: 10.1002/berj.3160

Patton, D. U., Hong, J. S., Patel, S., and Kral, M. J. (2017). A systematic review of research strategies used in qualitative studies on school bullying and victimization. Trauma Violence Abuse 18, 3–16. doi: 10.1177/1524838015588502

Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., et al. (2006). Guidance on the conduct of narrative synthesis in systematic reviews. Eur. Soc. Res. Council Methods Program. doi: 10.13140/2.1.1018.4643

Schott, R. M., and Sondergaard, D. M. (2014). “Introduction: new approaches to school bullying,” in School Bullying: New Theories in Context , eds R. M. Schott, and D. M. Sondergaard, (Massachusetts, MA: Cambridge University Press), 1–17.

Skrzypiec, G., Slee, P., Murray-Harvey, R., and Pereira, B. (2011). School bullying by one or more ways: does it matter and how do students cope? Sch. Psychol. Int. 32, 288–311. doi: 10.1177/0143034311402308

Smith, P. K., Cowie, H., Olafsson, R. F., and Liefooghe, A. P. D. (2002). Definitions of bullying: a comparison of terms used, and age and gender differences, in a fourteen-country international comparison. Child Dev. 73, 1119–1133. doi: 10.1111/1467-8624.00461

Spears, B., Taddeo, C., Collin, P., Swist, T., Razzell, M., Borbone, V., et al. (2016). Safe and Well Online: Learnings from Four Social Marketing Campaigns for Youth Wellbeing. Available at: https://researchdirect.westernsydney.edu.au/islandora/object/uws:36405/datastream/PDF/view (accessed July 1, 2019).

Stoudt, B. G. (2009). The role of language & discourse in the investigation of privilege: using participatory action research to discuss theory. Dev. Methodol. Interrupt. Power Urban Rev. 41, 7–28.

Thomson, P., and Gunter, H. (2008). Researching Bullying with students: a lens on everyday life in an ‘innovative school’. Int. J. Inclusive Educ. 12, 185–200. doi: 10.1080/13603110600855713

Tisdall, E. K. M., and Punch, S. (2012). Not so ‘new’? Looking critically at childhood studies. Child. Geogr. 10, 249–264. doi: 10.1080/14733285.2012.693376

United Nations Convention on the Rights of the Child (1989). Available at: http://www.unicef.org.uk/Documents/Publication-pdfs/UNCRC_PRESS2009 10web.pdf (accessed January 19, 2014).

Vaillancourt, T., McDougall, P., Hymel, S., Krygsman, A., Miller, J., Stiver, K., et al. (2008). Bullying: are researchers and children/youth talking about the same thing? Int. J. Behav. Dev. 32, 486–495. doi: 10.1177/0165025408095553

Keywords : bullying, young people, participatory research, social constructionism, young people as researchers, collaboration, bullying supports

Citation: O’Brien N (2019) Understanding Alternative Bullying Perspectives Through Research Engagement With Young People. Front. Psychol. 10:1984. doi: 10.3389/fpsyg.2019.01984

Received: 28 February 2019; Accepted: 13 August 2019; Published: 28 August 2019.

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Copyright © 2019 O’Brien. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Niamh O’Brien, [email protected]

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Effects of Bullying on Academic Performance Research Paper

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This research paper explores the profound impact of bullying on academic performance, delving into the intricate relationship between these two critical aspects of a student’s life. Beginning with an overview of bullying types prevalent in academic settings, including physical, verbal, social, and cyberbullying, the study investigates historical contexts, shedding light on the evolution of this pervasive issue. The literature review examines the link between bullying and mental health, emphasizing the need for a comprehensive understanding of the psychological mechanisms that underlie its effects on academic performance. Employing a mixed-methods approach, the research analyzes both quantitative and qualitative data to discern the intricate nuances of this complex relationship. The paper scrutinizes the various indicators of academic performance, such as grades, attendance, and dropout rates, while also exploring the cognitive and psychological dimensions affected by bullying. Additionally, it investigates protective factors and interventions, highlighting the role of supportive school environments, anti-bullying programs, and targeted mental health interventions. Through case studies, the study provides real-world illustrations of the impact of bullying on academic trajectories and evaluates the efficacy of different interventions. The findings underscore the urgent need for proactive measures in academic institutions, emphasizing the role of educators, parents, and mental health professionals in fostering a safe and conducive learning environment.

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Bullying is a pervasive issue with profound implications for the well-being and academic success of students. It is crucial to understand bullying comprehensively, considering it as a repeated aggressive behavior intended to cause harm, whether physical, verbal, social, or through digital means (Olweus, 1993). The multifaceted nature of bullying necessitates a nuanced exploration of its impact on academic performance, taking into account both direct and indirect manifestations.

The prevalence of bullying in academic settings is disconcerting, with numerous studies highlighting its widespread occurrence (Espelage & Swearer, 2003; Nansel et al., 2001). From elementary schools to universities, students experience various forms of bullying that can detrimentally affect their educational experiences. Recognizing the frequency and diverse manifestations of bullying is fundamental to developing targeted interventions and fostering supportive environments.

This research is significant in light of the pervasive nature of bullying and its potential long-term consequences. As bullying continues to be a prevalent issue in academic institutions, understanding its implications for academic performance is imperative for educators, policymakers, and mental health professionals. By comprehensively examining the effects of bullying on academic outcomes, this study aims to contribute to a deeper understanding of the challenges faced by students and provide insights that can inform effective prevention and intervention strategies.

The central questions guiding this research include: How does bullying impact various dimensions of academic performance? What are the mediating factors that influence the relationship between bullying and academic outcomes? Hypotheses posit that a strong correlation exists between the experience of bullying and diminished academic achievement, attendance, and increased dropout rates. Furthermore, the study explores the potential moderating role of protective factors in mitigating these effects.

While this research aims to provide a comprehensive examination of the effects of bullying on academic performance, certain limitations must be acknowledged. The study primarily focuses on the academic outcomes of bullying and may not encompass all possible variables influencing these outcomes. Additionally, the research is constrained by the availability of data and the willingness of participants to share their experiences.

The paper is organized to provide a thorough exploration of the topic. Following this introduction, the literature review delves into historical contexts, types of bullying, and its impact on mental health. The methodology section outlines the research design and ethical considerations. Subsequent sections explore the effects of bullying on academic performance, protective factors, and interventions. Case studies offer real-world illustrations, and the discussion synthesizes findings, offering recommendations for academic institutions. The paper concludes with a summary of key findings and a call to action for addressing bullying in educational settings.

Literature Review

The historical context of bullying provides valuable insights into its evolution and persistence within academic settings. Bullying is not a recent phenomenon but has roots that can be traced through centuries. Early research by Olweus (1993) notes the prevalence of bullying in school environments as far back as the 1970s. Understanding the historical context is essential for contextualizing the contemporary manifestations of bullying, recognizing its enduring nature, and informing interventions that address its historical trajectory.

Types of Bullying

• Physical Bullying: Physical bullying involves the use of force or aggression to harm another person physically. This can include hitting, kicking, or any form of bodily harm (Espelage & Swearer, 2003). Physical bullying is often overt and may leave visible marks on victims, making it a readily identifiable form of aggression within academic settings.
• Verbal Bullying: Verbal bullying encompasses the use of words to harm, intimidate, or belittle others. This form of bullying can manifest through name-calling, teasing, or spreading rumors (Nansel et al., 2001). Verbal bullying can be insidious, as it may not leave visible scars but can have profound psychological effects on victims.
• Social Bullying: Social bullying, also known as relational aggression, involves manipulating social relationships to harm others. This can include exclusion, spreading gossip, or damaging social standing (Crick & Grotpeter, 1995). Social bullying is often covert and may go unnoticed by educators and peers.
• Cyberbullying: With the rise of digital communication, cyberbullying has become a significant concern. It involves using electronic means, such as social media, to harass, threaten, or intimidate others (Patchin & Hinduja, 2017). Cyberbullying can extend the reach of bullying beyond the physical school environment, posing unique challenges for prevention and intervention.

Bullying exerts a profound toll on the mental health of those who experience it. Victims often report heightened levels of anxiety, depression, and low self-esteem (Hawker & Boulton, 2000). The persistent nature of bullying can lead to chronic stress, exacerbating mental health issues over time. Additionally, the stigma associated with being a target of bullying may deter individuals from seeking help, further exacerbating the mental health consequences.

Numerous studies have investigated the relationship between bullying and academic performance. A meta-analysis by Gini and Pozzoli (2009) found a consistent negative association between being bullied and academic achievement. Students who experience bullying are more likely to struggle academically, with consequences ranging from decreased classroom engagement to increased rates of school absenteeism (Rigby, 2003). Furthermore, the impact of bullying on academic outcomes is not limited to the immediate term, as long-term effects may persist into adulthood (Wolke et al., 2013). These findings emphasize the urgency of addressing bullying as a crucial factor influencing academic success.

Methodology

Research design.

• Quantitative or Qualitative Approach: This study employs a mixed-methods research design, recognizing the complementary strengths of both quantitative and qualitative approaches in comprehensively exploring the effects of bullying on academic performance. The quantitative aspect involves the analysis of academic records, attendance data, and standardized test scores to quantify the impact of bullying on various academic indicators. Concurrently, the qualitative component employs interviews and surveys to capture the nuanced experiences of students, providing a deeper understanding of the psychological mechanisms at play.
• Participants: The study encompasses a diverse sample of participants drawn from various academic institutions, including elementary schools, middle schools, high schools, and universities. A stratified sampling method is employed to ensure representation across different grade levels, socio-economic backgrounds, and geographical locations. Participants are selected based on reported experiences of bullying and academic performance, ensuring a varied and comprehensive dataset.
• Data Collection Methods: Data is collected through a combination of surveys, interviews, and academic record analysis. Surveys are distributed to students, teachers, and parents, capturing perceptions and experiences related to bullying and its impact on academic performance. Semi-structured interviews provide an in-depth exploration of individual experiences, allowing participants to express their perspectives on the complex interplay between bullying and academic outcomes. Academic records, attendance logs, and standardized test scores are also analyzed to quantify the academic impact of bullying.

The quantitative data collected is subjected to statistical analyses, including regression models and correlation analyses, to identify patterns and associations between variables. Academic performance indicators such as grades, attendance, and standardized test scores are compared between those who have experienced bullying and those who have not. The qualitative data from interviews and surveys undergo thematic analysis, identifying recurring themes and narratives that contribute to a comprehensive understanding of the lived experiences of participants.

Ethical considerations are paramount throughout the research process. Informed consent is obtained from all participants, ensuring that they are fully aware of the study’s purpose, procedures, and potential risks. Participants are assured of confidentiality and the right to withdraw from the study at any point without consequences. The study adheres to ethical guidelines outlined by institutional review boards (IRBs), emphasizing the protection of participants’ rights and well-being. Additionally, the research team is vigilant about potential emotional distress that participants may experience during interviews, providing resources and support for those who may require assistance. The dissemination of findings prioritizes anonymity, safeguarding the identities of participants to maintain their privacy and minimize any potential harm associated with the study.

Effects of Bullying on Academic Performance

Overview of academic performance indicators.

• Grades: Academic performance is intricately linked to students’ grades, making it a primary indicator of success in educational settings. Studies by Rigby (2003) and Gini and Pozzoli (2009) consistently highlight the negative correlation between bullying experiences and academic achievement. Victims of bullying often struggle to maintain high grades, facing challenges in concentration, completion of assignments, and overall academic engagement.
• Attendance: Bullying significantly impacts students’ attendance, as victims may avoid school to escape the stressors associated with their experiences (Kearney, 2008). Chronic absenteeism, whether due to physical illness or psychological distress induced by bullying, disrupts the continuity of learning and can contribute to academic underachievement (Kearney, 2007). Understanding the relationship between bullying and attendance is critical for developing interventions that promote consistent school attendance.
• Dropout Rates: The impact of bullying extends beyond immediate academic struggles and can contribute to increased dropout rates. Research by Wolke et al. (2013) suggests a heightened risk of dropping out among individuals who have experienced persistent bullying throughout their academic journey. The cumulative effect of social and psychological distress may lead to a disengagement from the educational system, emphasizing the importance of addressing bullying to prevent long-term academic consequences.

Bullying has been shown to have detrimental effects on cognitive functioning, affecting various aspects of learning and information processing. The heightened stress response associated with bullying can impair attention, memory, and executive functions (Shalev, Shapiro, & Bonne, 2017). Victims may experience difficulties in concentration, problem-solving, and retaining information, contributing to academic challenges. Understanding these cognitive impacts is crucial for developing targeted interventions that address the specific needs of students affected by bullying.

The intricate relationship between bullying and academic performance is mediated by various psychological mechanisms. The chronic stress induced by bullying activates the body’s stress response systems, leading to elevated levels of cortisol, which can impair cognitive functions (McEwen, 2000). Additionally, the emotional distress resulting from bullying experiences can contribute to anxiety and depression, further hindering academic engagement (Hawker & Boulton, 2000). Social factors, such as feelings of isolation and a diminished sense of belonging, also play a role in mediating the impact of bullying on academic outcomes (Swearer et al., 2009). Recognizing these underlying mechanisms is essential for developing comprehensive interventions that address both the immediate and long-term consequences of bullying on academic performance.

Protective Factors and Interventions

A crucial aspect of mitigating the impact of bullying on academic performance lies in cultivating supportive school environments. Schools that prioritize fostering a positive and inclusive atmosphere create a protective buffer against the adverse effects of bullying (Espelage et al., 2014). Research by Olweus (1996) emphasizes the significance of a whole-school approach, where administrators, teachers, and staff collaboratively establish and enforce anti-bullying policies. A supportive school environment encourages open communication, empathy, and mutual respect among students, fostering a sense of belonging that acts as a protective factor against the negative consequences of bullying.

Implementing evidence-based anti-bullying programs is a proactive strategy to address and prevent bullying within academic settings. Programs such as the Olweus Bullying Prevention Program have demonstrated effectiveness in reducing bullying incidents and improving overall school climate (Olweus, Limber, & Mihalic, 1999). These programs typically incorporate educational components, peer involvement, and consistent enforcement of anti-bullying policies. Interventions that focus on changing the culture of the school, promoting empathy, and teaching conflict resolution skills empower students and educators alike to contribute to a safer and more supportive learning environment.

Recognizing the psychological toll of bullying, targeted mental health interventions for victims are essential. Cognitive-behavioral therapy (CBT) has shown promise in alleviating the emotional distress associated with bullying experiences (Ttofi & Farrington, 2011). CBT interventions address the negative thought patterns and coping mechanisms developed by victims, providing them with tools to manage stress and build resilience. Additionally, group counseling and support networks can create a space for victims to share their experiences, fostering a sense of community and reducing the feelings of isolation often associated with bullying (Sharp, 1995).

Teachers and parents play pivotal roles in preventing and mitigating the effects of bullying. Teacher awareness and responsiveness to signs of bullying are crucial for early intervention (Smith, Schneider, Smith, & Ananiadou, 2004). Educators can facilitate classroom discussions on bullying, promoting empathy and understanding among students. Parental involvement is equally essential, as parents can support their children emotionally and work collaboratively with schools to address bullying incidents. Parent-teacher partnerships enhance the overall effectiveness of anti-bullying initiatives, creating a unified front against bullying both within and outside the school environment (Glew, Fan, Katon, Rivara, & Kernic, 2005).

In conclusion, a multi-faceted approach that involves creating supportive environments, implementing evidence-based programs, providing mental health interventions, and fostering collaboration among educators and parents is essential for addressing the complex issue of bullying and its impact on academic performance. By combining these strategies, educational institutions can create a comprehensive framework that not only addresses immediate concerns but also promotes the overall well-being and academic success of students.

Case Studies

Examination of specific cases illustrating the effects of bullying on academic performance.

To provide a nuanced understanding of the real-world impact of bullying on academic performance, this section presents several case studies drawn from diverse academic settings.

• Case Study 1 – Elementary School: In an elementary school setting, a student named Sarah faced persistent verbal bullying from her peers. The relentless taunts and name-calling led to a noticeable decline in Sarah’s academic performance. Her grades dropped, and she became increasingly reluctant to attend school. The case study delves into the specific instances of bullying, their frequency, and the subsequent academic challenges faced by Sarah.
• Case Study 2 – High School: In a high school context, a student named Alex experienced cyberbullying through social media platforms. The online harassment took a toll on Alex’s mental health, leading to anxiety and depression. The case study explores the impact of cyberbullying on Alex’s academic engagement, attendance, and overall well-being.

Analysis of Interventions and Outcomes

Following the identification of these case studies, interventions were implemented to address the bullying and its impact on academic performance.

• Intervention for Case Study 1: In Sarah’s case, the school implemented a comprehensive anti-bullying program that included classroom discussions on empathy and respect. Additionally, Sarah received individual counseling to address the emotional distress caused by the bullying. Over time, the intervention resulted in a noticeable improvement in Sarah’s academic performance, and the frequency of bullying incidents decreased.
• Intervention for Case Study 2: For Alex, the school collaborated with parents and initiated a cyberbullying awareness campaign. The school counselor provided mental health support, and the social media platforms involved were contacted to address the online harassment. The intervention contributed to a reduction in cyberbullying incidents, and Alex’s academic performance gradually improved.

These case studies underscore the importance of tailored interventions that address the unique circumstances surrounding each case. It is evident that a combination of school-wide programs, counseling, and collaboration with parents can lead to positive outcomes, not only in mitigating the immediate effects of bullying but also in restoring and enhancing academic performance. These interventions emphasize the need for a holistic and individualized approach to address the multifaceted challenges posed by bullying in educational settings.

The synthesis of findings from this comprehensive exploration reveals the intricate relationship between bullying and academic performance. The evidence suggests a consistent negative impact of bullying on various academic indicators, including grades, attendance, and the risk of dropout. The cognitive and psychological consequences of bullying further contribute to a complex web of challenges for students. The case studies and interventions discussed highlight the need for tailored approaches that address the specific dynamics of each case, emphasizing the importance of early intervention and ongoing support.

Mental health professionals play a pivotal role in addressing the psychological consequences of bullying. The findings underscore the need for accessible and targeted mental health interventions for victims, including cognitive-behavioral therapy and support groups. Early identification of mental health issues related to bullying is crucial, and mental health professionals should collaborate with schools to create a holistic support system for affected students. Additionally, interventions should extend beyond individual counseling to include family support and community resources to address the broader impact of bullying on mental health.

The implications for academic institutions are profound. Schools must prioritize the creation of supportive environments that foster a culture of empathy, respect, and inclusion. Implementing evidence-based anti-bullying programs, such as the Olweus Bullying Prevention Program, can contribute to a safer school climate. Moreover, schools should invest in comprehensive mental health services that address the emotional well-being of students. Teacher training programs should include strategies for recognizing and responding to bullying, emphasizing the role of educators in prevention and early intervention. Collaborative efforts with parents, such as parent-teacher partnerships, are instrumental in creating a unified front against bullying.

While this research provides valuable insights, several areas warrant further exploration. Longitudinal studies could track the long-term academic and mental health outcomes of individuals who have experienced bullying. Examining the effectiveness of different intervention strategies, especially in diverse cultural and socio-economic contexts, would contribute to the development of targeted and culturally sensitive approaches. Additionally, research could delve into the impact of bystander interventions and peer support on mitigating the effects of bullying. Exploring the role of technology and social media in the evolution of bullying and its impact on academic performance is another avenue for future investigation.

In conclusion, the discussion underscores the urgency of addressing bullying as a multifaceted issue that requires collaborative efforts from mental health professionals, educators, and policymakers. By synthesizing findings, considering implications for mental health, offering recommendations for academic institutions, and identifying areas for future research, this discussion contributes to a holistic understanding of the complex interplay between bullying and academic performance.

In summary, this research has shed light on the intricate and profound relationship between bullying and academic performance. The exploration of various types of bullying—physical, verbal, social, and cyberbullying—revealed the diverse ways in which students experience aggression within academic settings. The literature review highlighted the historical context, impact on mental health, and previous research on the link between bullying and academic outcomes. The methodology employed a mixed-methods approach, incorporating quantitative analysis of academic indicators and qualitative exploration of individual experiences through case studies. The effects of bullying on grades, attendance, cognitive functioning, and dropout rates were examined, emphasizing the pervasive and lasting consequences of this issue.

The findings underscore the critical importance of addressing bullying in academic settings for the holistic well-being and success of students. The negative impact of bullying on academic performance is not only statistically significant but also manifests in real-world scenarios, as illustrated by the case studies. The ripple effect of bullying extends beyond immediate academic challenges to encompass mental health issues, attendance problems, and an increased risk of dropping out. This research emphasizes that addressing bullying is not just a moral imperative but a strategic investment in the educational success and psychological well-being of the next generation.

The implications for mental health professionals, recommendations for academic institutions, and avenues for future research collectively emphasize the multifaceted nature of this issue. Mental health professionals are crucial in providing targeted interventions to mitigate the psychological consequences of bullying. Academic institutions must take proactive measures to create supportive environments, implement evidence-based anti-bullying programs, and collaborate with parents and mental health professionals. The call for future research encourages continued exploration of nuanced aspects, from longitudinal studies to the impact of technology on bullying dynamics.

In conclusion, the synthesis of these key findings reinforces the urgent need for comprehensive, collaborative, and ongoing efforts to address bullying in academic settings. By recognizing the far-reaching consequences of bullying and implementing evidence-based interventions, we can foster educational environments that are not only academically enriching but also safe, inclusive, and conducive to the overall well-being of every student. The journey towards effective prevention and intervention is a shared responsibility that requires commitment from educators, mental health professionals, parents, and policymakers alike.

Bibliography

• Crick, N. R., & Grotpeter, J. K. (1995). Relational aggression, gender, and social-psychological adjustment. Child Development, 66(3), 710-722.
• Espelage, D. L., & Swearer, S. M. (2003). Research on school bullying and victimization: What have we learned and where do we go from here? School Psychology Review, 32(3), 365-383.
• Espelage, D. L., Hong, J. S., & Rinehart, S. (2016). Bullying prevention and intervention: Realistic strategies for schools. National Association of School Psychologists.
• Gini, G., & Pozzoli, T. (2009). Association between bullying and psychosomatic problems: A meta-analysis. Pediatrics, 123(3), 1059-1065.
• Glew, G. M., Fan, M. Y., Katon, W., Rivara, F. P., & Kernic, M. A. (2005). Bullying, psychosocial adjustment, and academic performance in elementary school. Archives of Pediatrics & Adolescent Medicine, 159(11), 1026-1031.
• Hawker, D. S., & Boulton, M. J. (2000). Twenty years’ research on peer victimization and psychosocial maladjustment: A meta-analytic review of cross-sectional studies. Journal of Child Psychology and Psychiatry, 41(4), 441-455.
• Kearney, C. A. (2007). Forms and functions of school refusal behavior in youth: An empirical analysis of absenteeism severity. Journal of Child Psychology and Psychiatry, 48(1), 53-61.
• McEwen, B. S. (2000). The neurobiology of stress: From serendipity to clinical relevance. Brain Research, 886(1-2), 172-189.
• Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. JAMA, 285(16), 2094-2100.
• Olweus, D. (1993). Bullying at school: What we know and what we can do. Wiley.
• Olweus, D., Limber, S. P., & Mihalic, S. F. (1999). Blueprints for violence prevention, Book Nine: Bullying Prevention Program. Center for the Study and Prevention of Violence.
• Patchin, J. W., & Hinduja, S. (2017). Bullying beyond the schoolyard: Preventing and responding to cyberbullying. Sage Publications.
• Rigby, K. (2003). Consequences of bullying in schools. Canadian Journal of Psychiatry, 48(9), 583-590.
• Shalev, A., Shapiro, P. A., & Bonne, O. (2017). Physical and psychological effects of the Holocaust survivor syndrome. Israel Medical Association Journal, 19(3), 137-141.
• Sharp, S. (1995). Living with the bully of your mind. Educational Publishing Foundation.
• Swearer, S. M., Espelage, D. L., Vaillancourt, T., & Hymel, S. (2010). What can be done about school bullying? Linking research to educational practice. Educational Researcher, 39(1), 38-47.
• Wolke, D., Woods, S., Bloomfield, L., & Karstadt, L. (2013). Bullying involvement in primary school and common health problems. Archives of Disease in Childhood, 98(1), 1-6.

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" EFFECTS OF BULLYING " A RESEARCH STUDY

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Student Opinion

What Is Teenage Bullying Like Today?

An Opinion columnist writes that meanness among teenagers hasn’t gone away, it’s just gotten more stealthy. Do you agree?

By Natalie Proulx

Have you ever watched an old high school movie — say, “Mean Girls,” “The Breakfast Club” or “Clueless” — and been shocked by the insults the teenagers in the film hurled at one another? Did they use names and terms that you and your friends wouldn’t dare use today?

If so, do you think members of your generation are nicer to one another than those of previous generations? Or is bullying just different now?

In the Opinion essay “ ‘Mean Girls’ Has Lost Its Bite. Girls Haven’t ,” Jessica Bennett writes that while teenagers today are more aware of the importance of inclusivity, they still aren’t as kind as the new “Mean Girls” musical movie makes them seem. Instead, thanks to technology, teenage torture has become more subtle:

Passive aggression isn’t just “No offense, but” before delivering a stinging insult. It’s a soft block (blocking, then unblocking on social media, so that the person no longer follows you and then wonders why) for just a hint that you’re mad or removing people from a close friends group on Instagram, so that they can no longer view your Stories — but without ever telling them, so they are left to wonder what happened. People get dropped from group chats or are abandoned as new ones are started. Stealth meanness can be as covert as tagging someone in an unflattering photo or as clever as posting a celebratory birthday post for your bestie — but one that’s purposely less effusive than the one you posted for your other friend. “The phones make everything more exclusive,” said Poppy, 13, of New York. “When people leave others ‘on read’ even for a little” — she’s talking about having a text sit unanswered — “it can hurt the other person’s feelings even if that’s not the intention.” Hearing about the unwritten rules of today’s cafeteria dynamics made me almost pine for the simplicity of “you can’t sit with us.” A teenager in Michigan told me she unfollowed a classmate on Instagram because the girl had liked what she posted too quickly. It was “too thirsty,” she said. Another teenager, in Maryland, explained how a former friend would use their text chats as a way to constantly shift from hot to cold — acting friendly at school, then leaving her texts unanswered, then texting all night in minute-to-minute flurries, then ghosting her for days on end, leaving her on her phone and in her feelings, ruminating ( something girls are more prone to ) on the unanswered messages. Emily Weinstein, a social scientist at Harvard who studies how technology is shaping adolescents’ lives , notes that it’s the ambiguity that can make this kind of aggression so much more insidious , leading to a “perpetual state of second- and third-guessing.”

Students, read the entire article and then tell us:

Does Ms. Bennett’s essay resonate with you? Have you experienced or witnessed — or maybe even committed — any of the forms of exclusion and aggression she mentioned?

Based on your observations, what is teenage bullying like today? Is Ms. Bennett’s description accurate? Is there anything she missed? If she had interviewed you for this piece, what would you have told her?

Poppy, 13, of New York, said, “The phones make everything more exclusive.” Do you agree? How do phones affect your relationships with your peers, if at all?

Ms. Bennett writes, “Adults and teenagers alike are more aware of the importance of inclusivity and more attuned to the seriousness of subjects that used to be treated as fodder for jokes.” Do you think that’s true? Why or why not?

“Mean Girls,” of course, focuses on girls’ mistreatment of one another. But Ms. Bennett writes that “such behavior is by no means limited to girls.” This article from 2015 states, “It’s no surprise to learn that boys are more likely than girls to use physical aggression, but we also know that boys surpass girls when it comes to attacking peers verbally and engaging in cyberbullying .” Does this surprise you? Or is it true to your experience? Why do you think we view bullying among girls differently from how we view bullying among boys?

How much of a problem is bullying among students at your school? What would you want your parents, teachers, school administrators or other adults to know about it?

Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public and may appear in print.

Find more Student Opinion questions here. Teachers, check out this guide to learn how you can incorporate these prompts into your classroom.

Natalie Proulx joined The Learning Network as a staff editor in 2017 after working as an English language arts teacher and curriculum writer. More about Natalie Proulx

This paper is in the following e-collection/theme issue:

Published on 27.3.2024 in Vol 26 (2024)

Effectiveness of the Minder Mobile Mental Health and Substance Use Intervention for University Students: Randomized Controlled Trial

Authors of this article:

Original Paper

• Melissa Vereschagin 1 , BSc   ; 
• Angel Y Wang 1 , BA, MPhil   ; 
• Chris G Richardson 2 , PhD   ; 
• Hui Xie 3 , PhD   ; 
• Richard J Munthali 1 , PhD   ; 
• Kristen L Hudec 1 , PhD   ; 
• Calista Leung 1 , BA   ; 
• Katharine D Wojcik 4 , PhD   ; 
• Lonna Munro 1 , BSc   ; 
• Priyanka Halli 1 , MPH, MD   ; 
• Ronald C Kessler 5 , PhD   ; 
• Daniel V Vigo 1, 2 , LicPs, MD, DrPH  

1 Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

2 School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

3 Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada

4 Menninger Department of Psychiatry & Behavioural Sciences, Baylor College of Medicine, Houston, TX, United States

5 Department of Health Care Policy, Harvard Medical School, Boston, MA, United States

Corresponding Author:

Daniel V Vigo, LicPs, MD, DrPH

Department of Psychiatry

Faculty of Medicine

University of British Columbia

2255 Wesbrook Mall

Vancouver, BC, V6T2A1

Phone: 1 6048228048

Email: [email protected]

Background: University attendance represents a transition period for students that often coincides with the emergence of mental health and substance use challenges. Digital interventions have been identified as a promising means of supporting students due to their scalability, adaptability, and acceptability. Minder is a mental health and substance use mobile app that was codeveloped with university students.

Objective: This study aims to examine the effectiveness of the Minder mobile app in improving mental health and substance use outcomes in a general population of university students.

Methods: A 2-arm, parallel-assignment, single-blinded, 30-day randomized controlled trial was used to evaluate Minder using intention-to-treat analysis. In total, 1489 participants were recruited and randomly assigned to the intervention (n=743, 49.9%) or waitlist control (n=746, 50.1%) condition. The Minder app delivers evidence-based content through an automated chatbot and connects participants with services and university social groups. Participants are also assigned a trained peer coach to support them. The primary outcomes were measured through in-app self-assessments and included changes in general anxiety symptomology, depressive symptomology, and alcohol consumption risk measured using the 7-item General Anxiety Disorder scale, 9-item Patient Health Questionnaire, and US Alcohol Use Disorders Identification Test–Consumption Scale, respectively, from baseline to 30-day follow-up. Secondary outcomes included measures related to changes in the frequency of substance use (cannabis, alcohol, opioids, and nonmedical stimulants) and mental well-being. Generalized linear mixed-effects models were used to examine each outcome.

Results: In total, 79.3% (589/743) of participants in the intervention group and 83% (619/746) of participants in the control group completed the follow-up survey. The intervention group had significantly greater average reductions in anxiety symptoms measured using the 7-item General Anxiety Disorder scale (adjusted group mean difference=−0.85, 95% CI −1.27 to −0.42; P <.001; Cohen d =−0.17) and depressive symptoms measured using the 9-item Patient Health Questionnaire (adjusted group mean difference=−0.63, 95% CI −1.08 to −0.17; P =.007; Cohen d =−0.11). A reduction in the US Alcohol Use Disorders Identification Test–Consumption Scale score among intervention participants was also observed, but it was not significant ( P =.23). Statistically significant differences in favor of the intervention group were found for mental well-being and reductions in the frequency of cannabis use and typical number of drinks consumed. A total of 77.1% (573/743) of participants in the intervention group accessed at least 1 app component during the study period.

Conclusions: In a general population sample of university students, the Minder app was effective in reducing symptoms of anxiety and depression, with provisional support for increasing mental well-being and reducing the frequency of cannabis and alcohol use. These findings highlight the potential ability of e-tools focused on prevention and early intervention to be integrated into existing university systems to support students’ needs.

Trial Registration: ClinicalTrials.gov NCT05606601; https://clinicaltrials.gov/ct2/show/NCT05606601

International Registered Report Identifier (IRRID): RR2-10.2196/49364

Introduction

University attendance is a transitional period in which many students experience novel stressors related to moving away from home, navigating new social environments, and managing increased educational and financial demands in the absence of their traditional support systems [ 1 , 2 ]. The transition to attending university also coincides with the peak period of onset of many mental disorders, including mood, anxiety, and substance use disorders [ 3 , 4 ]. Studies have documented the high rates of mental health and substance use problems experienced by university students [ 5 ], with research also indicating that students with preexisting mental health problems can experience a worsening of their conditions following the transition to attending university [ 6 ].

Despite this high need, most students experiencing mental health problems do not receive treatment [ 7 ]. Research investigating help seeking among university students indicates that, compared to structural barriers, attitudinal barriers are the most important reasons for not seeking help [ 8 ]. The most commonly cited reason students give for not seeking help is a preference for handling things on their own [ 7 , 8 ]. One way of adapting interventions to align with this preference is to provide students with tools that are self-guided and allow them autonomy over how and when to use the tools provided. e-Interventions can be accessed by users at any time and have been demonstrated to be effective in improving various mental health [ 9 ] and substance use outcomes among university students [ 10 ]. These interventions have also been identified as key components in proposed models of care for universities [ 11 ]. While much of the literature on e-interventions has been focused on web-based tools, mobile apps have been identified as a promising means of delivering mental health interventions due to not only the increase in smartphone use but also the wide range of interventions that can be delivered through mobile platforms [ 12 , 13 ].

Given the range of challenges faced by university students, including the high rates of disorder-level and subclinical mental health and substance use problems, transdiagnostic approaches to early intervention and prevention may be beneficial for this population [ 14 ]. Developing this type of intervention requires the use of a holistic, student-centered design approach to identify evidence-based condition-specific and cross-cutting opportunities for intervention. Furthermore, the intervention needs to be aligned with the perceived needs and preferences of students to ensure meaningful engagement [ 15 ]. On the basis of these requirements, we codeveloped a mental health and substance use mobile app called Minder for Canadian university students. This participatory codevelopment process involved significant input from students through the creation of a Student Advisory Committee, usability testing via a virtual boot camp (ie, individual user-testing combined with a web-based survey), focus groups, and a pilot feasibility study [ 16 ].

The objective of this study was to test the effectiveness of the Minder mobile app in improving mental health and substance use outcomes in a general population of university students.

Trial Design

This study was based on a 2-arm, parallel-assignment, single-blinded (the statistician was blinded), 30-day randomized controlled trial with 1 intervention group and 1 waitlist control group. The study was registered at ClinicalTrials.gov (NCT05606601), and a full study protocol has been published [ 17 ]. No significant changes to the trial protocol or intervention content were made during the trial period; however, several minor adjustments, along with a description of minor technical issues, can be found in Multimedia Appendix 1 .

Ethical Considerations

Ethics approval was obtained from the University of British Columbia (UBC) Behavioural Research Ethics Board on January 6, 2022 (ethics ID: H21-03248). Informed consent was obtained through a web-based self-assessment questionnaire at the beginning of the study. Participants were informed of their ability to opt out at any point within the study by emailing the research team. Identifiable data were stored in data files within the app backend, which were separate from all deidentified app use and survey data. This information can only be linked using a unique study ID number. Participants received a CAD $10 (US $7.4) gift card for completion of the baseline survey and an additional CAD $10 (US $7.4) gift card for completion of the 30-day follow-up survey.

Participants

The study was conducted at the UBC Point Grey (Vancouver, British Columbia, Canada) campus. Participants needed to confirm their eligibility using a web-based self-assessment questionnaire before registering and consenting to the study. The inclusion criteria were as follows: students currently enrolled at the UBC Vancouver campus, aged ≥17 years, having access to and being able to use a smartphone with Wi-Fi or cellular data, and speaking English. The only exclusion criterion was based on a single screening question assessing suicidality risk (“We want to make sure that this app is appropriate for you at this time. Do you have a current suicidal plan [i.e., a plan to end your life]?”). Anyone endorsing a current suicidal plan (ie, answering “yes”) was prevented from registering and was instead provided with a list of local crisis resources. The eligibility criterion of being a current UBC student was confirmed using a unique student log-in checkpoint as part of the registration process. This process also ensured that each student could only enroll once.

Recruitment and Consent

Given the large sample size needed for this study, many different recruitment methods were used. Online recruitment occurred through various social media platforms and a linked ongoing Student E-Mental Health trend study [ 18 ]. Recruitment also occurred through in-person and on-campus engagements, such as setting up informational booths at the university, displaying posters about the study, visiting in-person and online classes, having professors share study information with their classes, and contacting student groups to share information with their members. Paid bus and bus stop advertisements at the university were also used. A more detailed description of the recruitment methods can be found in the study protocol for this trial [ 17 ].

Participants’ consent was obtained using Qualtrics (Qualtrics International Inc), a web-based form, after completing the eligibility screening. The consent form indicated that participants would either gain access to the full app immediately or in 30 days following completion of the final survey. Individual accounts were created for each participant and sent to them with a link to download the app. Upon downloading the app and completing the baseline survey, participants were randomly assigned through the app to the intervention group, which received full access to the Minder app, or to the control group, which only had access to a restricted version of the app that included a short introduction video and the baseline and follow-up surveys. Participants received a CAD $10 (US $7.40) gift card for completion of the baseline survey and an additional CAD $10 (US $7.40) gift card for completion of the 30-day follow-up survey; however, the use of the app itself was not remunerated.

Randomization and Intervention

Participants were randomized using a custom-developed automated process incorporated directly into the mobile app following completion of the baseline survey. The system assigned participants to either the intervention or control group using a predetermined block randomization list (1:1 randomization in blocks of 10) stratified for past drug use (any lifetime use of opioids or nonmedical stimulants). Stratification by past drug use was used to account for the low number of students using these substances and the need to ensure that they were evenly distributed across the study groups. The randomization lists (1 for each stratification group) were generated using the web-based stratified block randomization list creator in the clinical trial software Sealed Envelope (Sealed Envelope Ltd) [ 19 ]. The intervention and control groups completed the main assessments of the primary and secondary outcomes at baseline and the 30-day follow-up. The intervention group was also prompted to complete a short survey at 2 weeks that consisted of a limited set of questions on anxiety and depression symptoms.

The Minder mobile app was codeveloped with university students and professionals with the goal of creating an engaging self-directed tool for students to improve their mental health and manage substance use. The intervention is designed for a general population of students and, thus, addresses a wide range of challenges related to postsecondary student life, including managing emotions, relationships, well-being, and university life. The self-directed nature of the app also allows students to access features when needed. The codevelopment process consisted of ongoing student input through student staff members and volunteers along with several phases of purposeful student engagement and feedback. Further details on the codevelopment process can be found in the study by Vereschagin et al [ 16 ].

Participants who were randomized to the intervention group were given full access to the Minder app and instructed to use it as they wanted. They were also presented with a tutorial video outlining the different features of the app. The Minder intervention consists of 4 main components: Chatbot Activities, Services, Community, and Peer Coaching. The chatbot activities consist of an automated preprogrammed chatbot that delivers evidence-based messages and videos. The content is based primarily on cognitive behavioral therapy and psychoeducation; however, there is also content adapted from dialectical behavioral therapy, mindfulness, metacognitive training, and motivational interviewing. The content sections are presented on a home page map with several islands: University Life , Wellbeing , Relationships , Sadness , Stress & Anxiety , and Substance Use ( Figure 1 ). There is also an Explore Chat located on the home page map that guides participants to select an activity that may be relevant to their current needs. A full list of the content included can be found in Multimedia Appendix 2 [ 16 ]. Most of the chat activities also contain a summary page that is unlocked upon completion of the chat activity and allows participants to review content they learned at a later time. In addition, several chat activities contain specific practice components that also become unlocked after the main activity is complete.

The Peer Coaching component consists of trained volunteers assigned to each participant. These peer coaches reach out to participants at the beginning of the trial and midway through. They can provide support in navigating the app or nonclinical peer support based on active listening and problem-solving. Peer coaches can communicate with participants through an in-app chat asynchronously or synchronously through scheduled appointments delivered over in-app chat message or audio or video call. Before engaging with peer coaches, participants must provide a phone number that can be used to contact them in a crisis situation and affirm that they are not currently at risk of self-harm or having suicidal thoughts, are not under the influence of substances, and understand the circumstances in which confidentiality would need to be broken (ie, crisis situations or abuse of a minor; Figure 2 A).

The Services component consists of a 10-question survey tool that provides participants with recommendations for resources based on their current needs and preferences ( Figure 2 B). The survey tool and recommendations were adapted from a previously developed tool for university students [ 20 ] and can be completed multiple times to receive new recommendations. Recommendations are provided for 6 areas related to student well-being: mental health and relationships, substance use, abuse, sexual wellness, housing, and education and activities. An additional safety component was added so that participants who based on the services survey were considered to be at high risk of suicidality (ie, plan for suicide or recent attempt and thoughts of hurting others) were asked to consent to provide their contact information and receive an expedited appointment with the university counseling services.

The Community component consists of a searchable directory of student groups or clubs at the university that are sorted by interest (eg, volunteering, arts, and advocacy; Figure 2 C).

The Minder app also contains several other general features. An SOS button appears in the top corner of the home page and provides a list of crisis resources if needed ( Figure 1 ). The settings page allows participants to update their username and password as well as change their avatar. Several types of push notifications were delivered through the app. General notifications were sent on days 4, 18, and 24. Reminders to complete the 2-week and 30-day follow-up surveys were sent as push notifications and via automated email reminders on those dates. Additional email reminders were sent on days 35 and 41 to remind participants to complete the follow-up survey if they had not done so already.

Participants who were randomized to the control group had access to a locked version of the app that only allowed them to complete the baseline survey and view a short introduction video that appears before the log-in screen. Following completion of the baseline survey, they received a pop-up message telling them that they would be notified when it was time to complete the next survey. The app was then locked so that control participants were not able to access any other areas of the app. At 30 days, participants were notified that it was time to complete the 30-day follow-up survey within the app, and this survey became unlocked. The app provided push notifications and automated email reminders to complete the follow-up survey on day 30 as well as additional email reminders on days 35 and 41 if the survey had not yet been completed.

Individuals who consented to participate in the study and received an invitation email to download the app but did not complete the baseline survey received additional email reminders at 7 days, at approximately 20 days, and several months later following the creation of their account.

The participants’ use of the app components was recorded through the app back-end system. This included starting each of the chatbot activities, completing the services survey and receiving recommendations, viewing community groups, and communicating with a peer coach.

The main assessments of the primary and secondary outcomes were collected through self-assessment within the Minder app at baseline and at the 30-day follow-up. The 30-day follow-up survey had to be completed within 44 days of the beginning of the baseline survey (30 days plus 2 weeks to accommodate the use of reminders) for the participants to be included in the analysis.

Primary Outcomes

The primary outcomes assessed in this study were changes in general anxiety symptomology, depressive symptomology, and alcohol consumption risk from baseline to follow-up at 30 days. All outcomes were assessed using self-report questionnaires completed directly in the mobile app.

Anxiety symptoms were assessed using the 7-item General Anxiety Disorder scale (GAD-7) assessment—a commonly used self-report scale that assesses symptoms of generalized anxiety [ 21 ]. Each GAD-7 question is scored from 0 ( not at all ) to 3 ( nearly every day ), with total scores ranging from 0 to 21 and higher scores indicating a worse outcome (ie, greater frequency of anxiety symptoms).

Depressive symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9) self-report scale [ 22 ]. Each of the 9 questions is scored from 0 ( not at all ) to 3 ( nearly every day ). The total scores range from 0 to 29, with higher scores indicating a worse outcome (ie, a greater frequency of depressive symptoms).

Alcohol consumption risk was assessed using the US Alcohol Use Disorders Identification Test–Consumption Scale (USAUDIT-C) [ 23 ]. The USAUDIT-C is a 3-item self-report scale adapted from the consumption questions in the Alcohol Use Disorders Identification Test (AUDIT) [ 24 ]. Compared to the AUDIT, the USAUDIT-C includes expanded response options for the first 3 AUDIT questions—from 5 to 7 categories—to allow for more precise measurements when accounting for differences in standard drink sizes and cutoff limits. The higher the total score on the USAUDIT-C, the greater the respondent’s alcohol consumption and related risk [ 23 ].

Secondary Outcomes

A range of secondary outcomes, including reduced use of other substances and additional mental health constructs that the Minder app was theorized to affect, were also assessed. For the purposes of this study, we focused on examining changes from baseline to follow-up at 30 days in frequency of substance use (cannabis, alcohol, opioids, and nonmedical stimulants) and mental well-being.

Frequency of cannabis use was assessed using a single self-report question on frequency of cannabis consumption in the previous 30 days. The 3 questions on the USAUDIT-C assessed unique dimensions of alcohol consumption. Frequency of alcohol use was assessed using responses to the first question on the USAUDIT-C, which asks how often participants have a drink containing alcohol. The number of drinks consumed in a typical drinking session was assessed using the second question on the USAUDIT-C, which asks participants how many drinks containing alcohol they have on a typical day when drinking. Frequency of binge drinking was assessed using the third question in the USAUDIT-C, which asks participants how often they have ≥5 (if sex at birth was male) or ≥4 (if sex at birth was female) drinks on 1 occasion.

Frequency of any opioid use in the previous 30 days was assessed using self-reported questions that asked about any pharmaceutical opioid (eg, oxycodone; morphine; hydromorphone; meperidine; fentanyl patches; and codeine or codeine-containing products such as Tylenol 1, 2, or 3) with a physician’s prescription and taken as prescribed; any pharmaceutical opioid (eg, oxycodone; morphine; hydromorphone; meperidine; fentanyl patches; and codeine or codeine-containing products such as Tylenol 1, 2, or 3) either without a physician’s prescription or in larger doses than prescribed to get high, buzzed, or numbed out; and any street opioid (eg, heroin and fentanyl) or any other opioid obtained “on the street.” The final opioid use outcome was defined as the most frequent number among any prescribed, nonprescribed, and street opioids.

Frequency of nonmedical stimulant use in the previous 30 days was assessed using self-report questions that asked about the frequency of using any street stimulant (eg, cocaine, crack, methamphetamines, and crystal meth) or prescription stimulant (eg, amphetamine, methylphenidate, and modafinil) either without a physician’s prescription or in larger doses than prescribed to get high, buzzed, numbed out, or help them study or for any other reason. The final nonmedical stimulant use outcome was defined as the most frequent number among any prescription stimulant without a prescription or not as prescribed and any street stimulant. The exact wording and response options for the substance use secondary outcome assessments can be found in Multimedia Appendix 3 .

Mental well-being was assessed using the Short Warwick-Edinburgh Mental Wellbeing Scale. The Short Warwick-Edinburgh Mental Wellbeing Scale is a 7-item scale that has been widely validated [ 25 ], with total scores ranging from 7 to 35 and higher scores indicating a better outcome (ie, higher positive mental well-being).

Sample Size Estimation

The a priori sample size calculation for a small effect assessed using the PHQ-9, GAD-7, and USAUDIT-C was performed using an effect size defined by Cohen d =Δ/σ, where Δ is the group mean difference at the completion of the study and σ is the (pooled) within-group SD [ 17 ]. For a small effect size (Cohen d =0.2), the sample size required to have 80% power at a P =.02 level of significance (ie, 0.05/3 primary outcomes) was 524 in each group. After incorporating a 30% attrition rate, we anticipated requiring 748 participants in each group for a total of 1496 participants for the trial to be adequately powered.

Statistical Analysis

The study used a single-blinded approach in which only the statistician, who was external to the team, was blinded to the treatment group assignment when examining the primary hypotheses. The primary analysis was intention-to-treat (ITT) including all participants who completed the baseline assessment and were randomized to either the control or intervention group following the analysis plan prespecified in our protocol paper [ 17 ]. The analysis considered the following 2 features of the trial design: 3 primary end points and the use of block randomization.

For the 3 primary end points (GAD-7, PHQ-9, and USAUDIT-C scores), a global test for the null hypothesis of no treatment difference in all primary end points between the control and treatment groups was conducted first using a multivariate analysis of covariance for correlated data on GAD-7, PHQ-9, and USAUDIT-C scores at 30 days after the baseline, adjusting for their values at baseline and randomization blocks. Compared with testing each outcome separately, the advantages of the global test from joint modeling included more parsimonious hypothesis tests and mitigated concerns related to multiple testing [ 26 - 28 ] as well as pooling of the information over the correlated outcomes to increase study power, especially with missing outcomes [ 29 ]. If the global test rejected the null hypothesis and we concluded that there was an intervention effect on at least 1 of the 3 end points, we would then analyze each end point separately to identify which of the 3 study end points were affected by the intervention. We then used the sequential Hochberg correction method [ 30 ] to control the overall familywise error rate at α=.05 when testing the hypothesis for each individual primary end point. For baseline characteristics, 2-sample t tests (2-tailed) were used to compare means, chi-square tests were used to compare proportions, and the Kruskal-Wallis test was used to test for differences in medians.

The general approach for analyzing all the types of individual study outcomes separately (including the 3 primary end points and the secondary end points) was generalized linear mixed-effects models (GLMMs) for clustered measures with the randomization block as the clustering variable. These models can handle a wide range of outcome types, including continuous, binary, ordinal, and count, and can account for the correlations among observations within the same cluster. GLMMs have been widely used for conducting ITT analysis in randomized controlled trials with missing outcome data and can account for data missing at random (MAR) without the need to model why data are missing or to perform explicit imputations of the missing values [ 31 ]. Specifically, we analyzed each primary end point using a linear mixed-effects model, a special case of the GLMM.

For secondary outcomes, we used linear mixed-effects models to analyze mental well-being measures and a mixed-effects quasi–Poisson regression model with a log link (a special case of GLMM) for substance use frequency measures, and the zero-inflated Poisson was used for the number of drinks to deal with the excess zero counts. The treatment effect on an outcome at 30 days was assessed in these models with the treatment allocation as the main explanatory variable and with adjustment for baseline outcome assessment value as a fixed effect and the randomization block as a random effect. Robust empirical sandwich SE estimates that are robust to model misspecifications (eg, nonnormal error terms) were used for statistical inference.

The results from these GLMMs for the analysis are reported in the Results section as the model-adjusted differences in the group mean values of the continuous end points between the intervention and control groups. These intervention effect estimates, 95% CIs, and P values were obtained from the aforementioned GLMMs, clustering on randomization block effects and adjusting for the baseline outcome values. Standardized effect sizes were calculated for continuous scores by dividing the adjusted mean differences by the SDs across all participants at baseline. The incidence rate ratios (IRRs) from Poisson regression through GLMM and zero inflation for the number of drinks are also reported.

To evaluate the robustness of the results to alternative assumptions regarding missing data, sensitivity analyses were conducted on the primary outcomes via (1) using selection models to measure the potential impacts of data missing not at random [ 32 , 33 ] and (2) adjusting analysis for additional baseline covariates potentially predictive of missing data. All the primary analyses were conducted in SAS (version 9.4; SAS Institute), except for the sensitivity analysis of the selection models, which was conducted in the isni package in R (version 3.4; R Foundation for Statistical Computing) [ 34 ], whereas secondary analyses were conducted in Stata (version 15.1; StataCorp) [ 35 ].

Data and Privacy

Many steps were taken to ensure the privacy of participants. Each participant received an individual account that had a unique username and password. They were then able to change this password upon logging in. Only participant emails and phone numbers (if provided through access to peer coaching) were stored within the app; names were used only for consent and were never entered into the app. Instead, participants could create a unique username within the app that they were informed should not include their full name. Identifiable data (email and phone number) were stored in data files within the app back end separate from all the app use and survey data, which were recorded with only a study ID number.

Recruitment

Recruitment initially began on September 4, 2022, during an on-campus student orientation event where interested students were asked to provide contact information to receive a follow-up email; however, participants were not provided with app download information and user accounts to begin the study until September 28, 2022, due to technical delays. Recruitment concluded on June 2, 2023, with the last participant beginning the trial on June 11, 2023.

Participant Flow

In total, 2293 individuals were invited to participate in the trial following eligibility screening and provision of informed consent. Of those 2293 individuals, 1489 (64.9%) participants completed the baseline survey and were randomized into the trial, with 743 (49.9%) of the 1489 participants in the intervention group and 746 (50.1%) in the control group. A total of 79.3% (589/743) of participants in the intervention group and 83% (619/746) of participants in the control group completed the 30-day follow-up survey within the specified 44-day period to be included in the analysis (ie, 279/1489, 18.7% of participants who completed the baseline survey did not complete the 30-day follow-up survey within 44 days and were therefore considered lost to follow-up). Additional information on the participant flow is shown in Figure 3 .

Participant Characteristics

The baseline characteristics of the participants in the intervention and control groups are presented in Table 1 . The median age of the participants was 20 years, and 70.3% (1045/1487) self-identified as women. In terms of mental health, 33.8% (455/1347) reported a history of anxiety, and 38.9% (576/1481) reported moderate or greater levels of recent anxiety (ie, total score of ≥10) based on the GAD-7. A history of depression was reported by 28.4% (382/1347) of participants, with 43.8% (645/1474) reporting moderate or greater levels of recent depressive symptomology (ie, total score of ≥10) on the PHQ-9. The intervention group had higher baseline scores on both the GAD-7 ( P =.02) and PHQ-9 ( P =.02) . No other statistically significant differences in baseline characteristics were found between the intervention and control groups ( Table 1 ).

a Variations in the total n values for each characteristic were due to missing responses. We used 2-sample t tests (2-tailed) to compare means, chi-square tests to compare proportions, and the Kruskal-Wallis test to test for differences in medians.

b GAD-7: 7-item General Anxiety Disorder scale.

c PHQ-9: 9-item Patient Health Questionnaire.

d USAUDIT-C: US Alcohol Use Disorders Identification Test–Consumption Scale.

e SWEMWS: Short Warwick-Edinburgh Mental Wellbeing Scale.

Among those in the intervention group (743/1489, 49.9%), 77.1% (573/743) accessed at least 1 app component during the 30-day study period. More specifically, 73.8% (548/743) engaged in 1 or more chatbot activities, 21.8% (162/743) accessed the community component, 27.9% (207/743) completed the services survey, and 17.2% (128/743) accessed a peer coach.

For the 3 primary end points (GAD-7, PHQ-9, and USAUDIT-C scores), a global Wald test with the null hypothesis of no treatment difference in all primary end points between the control and treatment groups was conducted using a marginal multivariate analysis of covariance model for correlated data. This multivariate test of overall group differences was statistically significant (Hotelling test=0.02; P =.001; Table 2 ), indicating that there were group differences in at least 1 of the primary outcomes. We then tested each of the primary outcomes using a GLMM for clustered measures, adjusting for baseline values with the randomization block as the clustering variable, and applied a sequential Hochberg correction method [ 30 ] to control the overall familywise error rate at .05 when testing the hypothesis for each individual primary end point. The results of these GLMMs indicated that participants in the intervention group had significantly greater reductions in anxiety (adjusted group mean difference=−0.85, 95% CI −1.27 to −0.42; P <.001; Cohen d =−0.17, 95% CI −0.26 to −0.09) and depressive (adjusted group mean difference=−0.63, 95% CI −1.08 to −0.17; P =.007; Cohen d =−0.11, 95% CI −0.19 to −0.03) symptoms than those in the control group ( Table 2 ). Although participants in the intervention group also demonstrated a greater reduction in alcohol risk scores on the USAUDIT-C, this difference was not statistically significant (adjusted group mean difference=−0.13, 95% CI −0.34 to 0.08; P =.23).

We conducted the following analysis to quantify the robustness of primary findings to the assumption of data MAR. First, baseline covariates potentially predictive of missing data were added to the GLMM outcome models, and the intervention effect estimates remained similar and yielded qualitatively similar P values ( Table 2 , last 2 columns). Second, selection models were used that permit the missingness probability to depend on the unobserved outcome values after conditioning on the observed data, after which we computed the index of local sensitivity to nonignorability [ 34 ]. The index of local sensitivity to nonignorability analysis results are reported in Table 3 . The ISNI/SD (ISNI divided by SD) column in Table 3 estimates the change in intervention effect estimates for a moderate size of nonrandom missingness, where a 1-SD (SD of the outcome) increase in the outcome is associated with an e 1 =2.7–fold increase in the odds of being observed conditioned on the same values of the observed predictors for missingness. For such moderately sized nonrandom missingness, the changes in the intervention effect estimates were small for both GAD-7 and PHQ-9 scores ( Table 3 ). The MinNI column in Table 3 computes the minimum magnitude of nonignorable missingness needed for substantial sensitivity such that the selection bias due to data missing not at random is of the same size as the SE. The smaller the value of the minimum nonignorable missingness, the greater the sensitivity. A minimum nonignorable missingness of 1 is suggested as the cutoff value for important sensitivity [ 32 ]. The minimum nonignorable missingness values for both the GAD-7 and PHQ-9 far exceeded 1, indicating that no sensitivity to potential missingness not at random was present for the primary findings. Table 3 shows that both the control and intervention groups had moderate and comparable missing data percentages for both the GAD-7 and PHQ-9, which can explain why our analysis results were insensitive to the MAR assumption.

a Adjusting for block numbers and baseline outcome values.

b Global test: P =.001.

c Adjusting for age, gender, student status, race, substance use at baseline, block numbers, and baseline outcome values.

d Global test: P =.003.

e GAD-7: 7-item General Anxiety Disorder scale.

h N/A: not applicable.

k PHQ-9: 9-item Patient Health Questionnaire.

p USAUDIT-C: US Alcohol Use Disorders Identification Test–Consumption Scale.

a In the missing data percentage (n m /n), n m is the number of participants not completing the outcome assessment at 30 days, and n is the number of participants in the intention-to-treat sample.

b ISNI: index of sensitivity to nonignorability; SD refers to the SD of the outcomes.

c MinNI: minimum magnitude of nonignorable missingness. A MinNI of <1 indicates sensitivity to data missing not at random, and a MinNI of >1 indicates no sensitivity to data missing not at random.

d GAD-7: 7-item General Anxiety Disorder scale.

e PHQ-9: 9-item Patient Health Questionnaire.

f USAUDIT-C: US Alcohol Use Disorders Identification Test–Consumption Scale.

The GLMM for clustered measures with the randomization block as the clustering variable was also used to test for differences between the intervention and control groups on secondary outcomes without any adjustment for multiple testing. The results of these GLMMs indicated that ( Table 4 ), compared to those in the control group, participants in the intervention group had significantly greater improvements in mental well-being (adjusted mean difference=0.73, 95% CI 0.35-1.11; P <.001; Cohen d =0.17, 95% CI 0.08-0.26) and were significantly associated with a 20% reduction in their frequency of cannabis use (IRR=0.80, 95% CI 0.66-0.96; P =.02) and a 13% reduction in the typical number of drinks consumed when drinking (IRR=0.87, 95% CI 0.77-0.98; P =.03). No significant differences were found in frequency of binge drinking (IRR=0.98, 95% CI 0.86-1.13; P =.83), frequency of drinking (IRR=0.97, 95% CI 0.88-1.06; P =.48), or frequency of any opioid use (IRR=0.62, 95% CI 0.16-2.31; P =.48). The impact of the intervention on nonmedical stimulant use could not be assessed due to the small number of nonmedical stimulant users at baseline and follow-up.

a IRR: incidence rate ratio.

b Adjusted difference.

d N/A: not applicable.

g Refer to Multimedia Appendix 3 for the category definitions for each secondary measure.

Principal Findings

Minder was codeveloped with students to provide them with a set of self-guided tools to manage their mental health and substance use. In this study, we found that participants in the intervention group who had access to the Minder app reported significantly greater average reductions in symptoms of anxiety (GAD-7) and depression (PHQ-9) than those in the control group. This finding aligns with the presentation of “stress and anxiety” and “sadness” as key topic areas within the Minder app and each topic having its own dedicated content island. Although these findings showed small effects on average in our sample, this may be related to the fact that, at baseline, only 38.9% (576/1481) of the participants had moderate or greater levels of anxiety and 43.8% (645/1474) had moderate or greater levels of depressive symptoms. Many participants in our sample reported only mild or no symptoms of anxiety or depression, which may have contributed to the finding of a small effect on average. Recent reviews on the effects of smartphone apps on anxiety and depression have found larger effects than those reported in this study; however, most studies recruit participants with clinical-level problems [ 12 , 13 ]. A meta-analysis examining internet interventions for university students found small effect sizes for anxiety and depression [ 36 ]. The Minder intervention group also demonstrated significant improvements in mental well-being in our analysis of secondary outcomes, which reflects a more general positive domain of mental health that may be more relevant to students without existing mental health concerns.

One of the key decisions in this trial was to include students with few or no symptoms of anxiety or depression. Providing interventions to nonclinical populations makes them more accessible to the large proportion of the population who may not meet clinical diagnostic criteria but may still experience occasional mental health challenges that can be addressed using existing tools (eg, via app-based cognitive behavioral therapy) [ 37 ]. In addition, this study used an ITT analysis that included all participants who were randomized regardless of whether they used the intervention. There was also no minimum amount of required content for participants to complete, nor were they remunerated for their use of the app. This pragmatic approach provided an approximation of the average effect of Minder on mental health and substance use outcomes in a university population if it were to be made available to everyone. As outlined in our study protocol, we plan to complement these findings with additional secondary analyses to examine the impact of Minder in subgroups of participants defined by the extent of their app use and baseline mental health and sociodemographic characteristics.

Although we did not find evidence of an effect on overall alcohol consumption risk in our primary outcome analyses of the USAUDIT-C, we did find significant reductions in cannabis use frequency and the typical number of alcoholic drinks consumed in a drinking session in our analysis of secondary outcomes. Reduction in number of drinks consumed when drinking and reducing alcohol-related harms were a main focus of the alcohol intervention content in the Minder app. For example, there was an activity in the app that encouraged users to set a goal for how many drinks they would consume in a drinking session and then track the number consumed in real time using the app. In addition, psychoeducational and motivational interviewing content focused on reducing harms associated with drinking, including following lower-risk drinking guidelines or cutting back on alcohol use. Similarly, cannabis use content focused on following lower-risk cannabis use guidelines, such as reducing the frequency of use to once a week or weekends [ 38 ]. We did not find significant reductions in measures of opioid use or nonmedical stimulant use frequency. However, there were low numbers of participants who used these substances in the study, particularly nonmedical stimulants. This finding may be related to the way in which the current Minder app allows users to select whatever content they think is most relevant to them regardless of their current mental health or substance use status. Substance use is often perceived by students to be higher than it actually is among their peers, thus normalizing its use on university campuses [ 39 , 40 ]. As a result, students may not be as motivated to address substance use compared to other aspects of their lives in which they may be experiencing distress. Previous studies have found that attitudes and norms surrounding drinking predict alcohol use behaviors among college students [ 41 , 42 ]. Addressing existing positive attitudes regarding commonly used substances (eg, alcohol and cannabis), as well as variations in norms such as for different genders [ 43 ], may be important in improving engagement with this content and tailoring the app to the needs of users. Future versions of the Minder app could include more nuanced approaches to address potentially harmful norms by providing tailored recommendations for substance use content within the app.

These findings are promising when considering the potential benefits of the Minder app as a tool in more comprehensive approaches to campus mental health that include early intervention and prevention. Given the extensive mental health needs of university students, stepped-care approaches have been identified as an efficient strategy to organize the delivery of campus mental health services in Canada [ 44 ]. The Minder intervention, which requires few resources (support is limited to online formats and provided by volunteer student coaches), could be readily integrated into such systems to support self-screening (via the existing services component and completion of the PHQ-9 and GAD-7) and the provision of immediate support and resources for students without higher levels of clinical concerns. It is also important to note that the Minder app was co-designed with students to connect users with the broader campus mental health systems through the Services component as well as with the greater student body using the Community component. In this way, it can be used to strengthen the connections between these existing systems and fill gaps in services, particularly in the area of prevention and treatment for mild to moderate symptomology.

A major strength of the Minder app is that it has been meaningfully codeveloped with university students and campus health care providers. Many mobile apps are not able to retain users after initial download and have low engagement rates overall [ 45 ]. Co-design processes have been identified as an effective means of ensuring that e-tools meet the needs of the end users they are trying to help [ 15 ]. The Minder app used an extensive codevelopment process that allowed for many improvements to be made with direct input from students and the clinicians currently supporting them [ 16 ]. The positive impact and low rates of loss to follow-up in this trial provide some support for the general acceptability of Minder . Previous studies on internet-based interventions for university students have also demonstrated their effectiveness in reducing mental health outcomes and that these interventions are generally acceptable to students; however, acceptability is often not reported [ 9 ].

Another strength of this study is the pragmatic trial design, which included a large nonclinical sample. Remuneration was only provided to participants for completion of the baseline and follow-up surveys, not for use of the intervention itself. Participants were also not told to use the app in any specific way or for any given amount of time when starting the study. Although this approach may have contributed to the finding of small average effects, it does increase the generalizability of our findings to other populations and provides an estimate of the impact of the app if it were made available to all students.

Limitations

Several limitations should be considered when interpreting the results of our study. As with many trials for online interventions, the participants were not blinded to what condition they were in, leading to a potential for placebo effects. However, control group participants did download the app and completed the surveys within it, which may have helped mitigate these effects on the trial. There were also several minor technical issues throughout the trial that may have impacted the participants’ experience with the app; however, these were resolved quickly by the research team. An explanation of these issues can be found in Multimedia Appendix 1 . There were some participants in the intervention group who did not use the app at all apart from completing the surveys. Given the ITT trial design, all randomized participants were included in the analysis; however, future analyses are planned to examine the effects of the intervention for those who actually engaged with the app content, along with the identification of subgroups that may benefit the most from this type of intervention.

Implications for Future Research

Further research will be conducted to better understand how to optimize the Minder intervention for different needs and additional populations. By better understanding who benefitted from the intervention and what content they used, we plan to make the app more personalized (including recommendations and features). In addition, future codevelopment processes will be needed to further improve app use and incorporation into existing systems of care. There were some participants who did not use the app outside the surveys, so trying to make the intervention more appealing for these users will be important. This may involve gamification strategies, refinement of content and enhancement of the chatbot using artificial intelligence tools, and the development of new features.

Conclusions

The Minder app was effective in reducing symptoms of anxiety and depression, with provisional support for increasing mental well-being and reducing the frequency of cannabis and alcohol use in a general population of university students. These findings support our use of a codevelopment approach and provide evidence of the potential of digital intervention tools such as Minder to support prevention and early intervention efforts for university students.

Acknowledgments

This work was supported by the Health Canada Substance Use and Addictions Program (arrangement: 1920-HQ-000069; University of British Columbia ID: F19-02914).

Conflicts of Interest

For the past 3 years, RCK has been a consultant for the Cambridge Health Alliance; Canandaigua Veterans Affairs Medical Center; Holmusk; Partners Healthcare, Inc; RallyPoint Networks, Inc; and Sage Therapeutics. He has stock options in Cerebral Inc, Mirah, PYM, Roga Sciences, and Verisense Health.

Technical issues and protocol changes.

Chatbot activity content overview.

Identified secondary outcomes with scoring.

CONSORT-eHEALTH checklist (V 1.6.1).

• Cunningham S, Duffy A. Investing in our future: importance of postsecondary student mental health research. Can J Psychiatry. Feb 2019;64(2):79-81. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chan V, Moore J, Derenne J, Fuchs DC. Transitional age youth and college mental health. Child Adolesc Psychiatr Clin N Am. Jul 2019;28(3):363-375. [ FREE Full text ] [ CrossRef ] [ Medline ]
• de Girolamo G, Dagani J, Purcell R, Cocchi A, McGorry PD. Age of onset of mental disorders and use of mental health services: needs, opportunities and obstacles. Epidemiol Psychiatr Sci. Mar 2012;21(1):47-57. [ CrossRef ] [ Medline ]
• Kessler RC, Amminger GP, Aguilar-Gaxiola S, Alonso J, Lee S, Ustün TB. Age of onset of mental disorders: a review of recent literature. Curr Opin Psychiatry. Jul 2007;20(4):359-364. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Auerbach RP, Mortier P, Bruffaerts R, Alonso J, Benjet C, Cuijpers P, et al. WHO World Mental Health Surveys International College Student Project: prevalence and distribution of mental disorders. J Abnorm Psychol. Oct 2018;127(7):623-638. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Duffy A, Keown-Stoneman C, Goodday S, Horrocks J, Lowe M, King N, et al. Predictors of mental health and academic outcomes in first-year university students: identifying prevention and early-intervention targets. BJPsych Open. May 08, 2020;6(3):e46. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Eisenberg D, Hunt J, Speer N, Zivin K. Mental health service utilization among college students in the United States. J Nerv Ment Dis. May 2011;199(5):301-308. [ CrossRef ] [ Medline ]
• Ebert DD, Mortier P, Kaehlke F, Bruffaerts R, Baumeister H, Auerbach RP, et al. Barriers of mental health treatment utilization among first-year college students: first cross-national results from the WHO World Mental Health International College Student Initiative. Int J Methods Psychiatr Res. Jun 2019;28(2):e1782. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lattie EG, Adkins EC, Winquist N, Stiles-Shields C, Wafford QE, Graham AK. Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: systematic review. J Med Internet Res. Jul 22, 2019;21(7):e12869. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Carey KB, Scott-Sheldon LA, Elliott JC, Bolles JR, Carey MP. Computer-delivered interventions to reduce college student drinking: a meta-analysis. Addiction. Nov 2009;104(11):1807-1819. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Newfoundland and Labrador stepped care 2.0© e-mental health demonstration project final report. Mental Health Commission of Canada. URL: https:/​/www.​mentalhealthcommission.ca/​wp-content/​uploads/​drupal/​2019-09/​emental_health_report_eng_0.​pdf [accessed 2023-09-28]
• Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry. Oct 2017;16(3):287-298. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Firth J, Torous J, Nicholas J, Carney R, Rosenbaum S, Sarris J. Can smartphone mental health interventions reduce symptoms of anxiety? a meta-analysis of randomized controlled trials. J Affect Disord. Aug 15, 2017;218:15-22. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mei C, Nelson B, Hartmann J, Spooner R, McGorry PD. Transdiagnostic early intervention, prevention, and prediction in psychiatry. In: Personalized Psychiatry. London, UK. Academic Press; 2020;27-37.
• Torous J, Nicholas J, Larsen ME, Firth J, Christensen H. Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements. Evid Based Ment Health. Aug 2018;21(3):116-119. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Vereschagin M, Wang AY, Leung C, Richardson CG, Hudec KL, Doan Q, et al. Co-developing tools to support student mental health and substance use: minder app development from conceptualization to realization. J Behav Cogn Ther. Mar 2023;33(1):35-49. [ CrossRef ]
• Wang AY, Vereschagin M, Richardson CG, Xie H, Hudec KL, Munthali RJ, et al. Evaluating the effectiveness of a codeveloped e-mental health intervention for university students: protocol for a randomized controlled trial. JMIR Res Protoc. Aug 30, 2023;12:e49364. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jones LB, Judkowicz C, Hudec KL, Munthali RJ, Prescivalli AP, Wang AY, et al. The World Mental Health International College Student Survey in Canada: protocol for a mental health and substance use trend study. JMIR Res Protoc. Jul 29, 2022;11(7):e35168. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Randomisation and online databases for clinical trials. Sealed Envelope. URL: https://www.sealedenvelope.com/ [accessed 2023-09-28]
• Virk P, Arora R, Burt H, Gadermann A, Barbic S, Nelson M, et al. HEARTSMAP-U: adapting a psychosocial self-screening and resource navigation support tool for use by post-secondary students. Front Psychiatry. Feb 22, 2022;13:812965. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. May 22, 2006;166(10):1092-1097. [ CrossRef ] [ Medline ]
• Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. Sep 2001;16(9):606-613. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Higgins-Biddle JC, Babor TF. A review of the Alcohol Use Disorders Identification Test (AUDIT), AUDIT-C, and USAUDIT for screening in the United States: past issues and future directions. Am J Drug Alcohol Abuse. 2018;44(6):578-586. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption--II. Addiction. Jun 1993;88(6):791-804. [ CrossRef ] [ Medline ]
• Stewart-Brown S, Tennant A, Tennant R, Platt S, Parkinson J, Weich S. Internal construct validity of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS): a Rasch analysis using data from the Scottish Health education population survey. Health Qual Life Outcomes. Feb 19, 2009;7:15. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guo L, Qian Y, Xie H. Assessing complier average causal effects from longitudinal trials with multiple endpoints and treatment noncompliance: an application to a study of Arthritis Health Journal. Stat Med. Jun 15, 2022;41(13):2448-2465. [ FREE Full text ] [ CrossRef ] [ Medline ]
• O'Brien PC. Procedures for comparing samples with multiple endpoints. Biometrics. Dec 1984;40(4):1079-1087. [ CrossRef ]
• Pocock SJ, Geller NL, Tsiatis AA. The analysis of multiple endpoints in clinical trials. Biometrics. Sep 1987;43(3):487-498. [ CrossRef ]
• Teixeira-Pinto A, Siddique J, Gibbons R, Normand SL. Statistical approaches to modeling multiple outcomes in psychiatric studies. Psychiatr Ann. Jul 01, 2009;39(7):729-735. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. Dec 1988;75(4):800-802. [ CrossRef ]
• Diggle P, Heagerty P, Liang KY, Zeger S. Analysis of Longitudinal Data. Oxford, UK. Oxford University Press; 2013.
• Jing B, Qian Y, Heitjan DF, Xie H. Tutorial: assessing the impact of nonignorable missingness on regression analysis using index of local sensitivity to nonignorability. Psychol Methods. Nov 16, 2023. (forthcoming). [ CrossRef ] [ Medline ]
• Xie H, Qian Y. Measuring the impact of nonignorability in panel data with non-monotone nonresponse. J Appl Econom. Jan 18, 2012;27(1):129-159. [ CrossRef ]
• Xie H, Gao W, Xing B, Heitjan DF, Hedeker D, Yuan C. Measuring the impact of nonignorable missingness using the R package isni. Comput Methods Programs Biomed. Oct 2018;164:207-220. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Stata Corp. Stata statistical software: release 15.1. StataCorp LLC. 2017. URL: https://www.stata.com/stata15/ [accessed 2024-03-11]
• Harrer M, Adam SH, Baumeister H, Cuijpers P, Karyotaki E, Auerbach RP, et al. Internet interventions for mental health in university students: a systematic review and meta-analysis. Int J Methods Psychiatr Res. Jun 2019;28(2):e1759. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR Ment Health. Mar 01, 2016;3(1):e7. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Canada's lower-risk cannabis use guidelines. Government of Canada. URL: https:/​/www.​canada.ca/​en/​health-canada/​services/​drugs-medication/​cannabis/​resources/​lower-risk-cannabis-use-guidelines.​html [accessed 2023-09-28]
• Baer JS, Stacy A, Larimer M. Biases in the perception of drinking norms among college students. J Stud Alcohol. Nov 1991;52(6):580-586. [ CrossRef ] [ Medline ]
• Lewis MA, Neighbors C. Gender-specific misperceptions of college student drinking norms. Psychol Addict Behav. Dec 2004;18(4):334-339. [ CrossRef ] [ Medline ]
• DiBello AM, Miller MB, Neighbors C, Reid A, Carey KB. The relative strength of attitudes versus perceived drinking norms as predictors of alcohol use. Addict Behav. May 2018;80:39-46. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Neighbors C, Lee CM, Lewis MA, Fossos N, Larimer ME. Are social norms the best predictor of outcomes among heavy-drinking college students? J Stud Alcohol Drugs. Jul 2007;68(4):556-565. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hathaway A, Mostaghim A, Kolar K, Erickson PG, Osborne G. A nuanced view of normalisation: attitudes of cannabis non-users in a study of undergraduate students at three Canadian universities. Drugs Educ Prev Policy. 2016;23(3):238-246. [ CrossRef ]
• Duffy A, Saunders KE, Malhi GS, Patten S, Cipriani A, McNevin SH, et al. Mental health care for university students: a way forward? Lancet Psychiatry. Nov 2019;6(11):885-887. [ CrossRef ] [ Medline ]
• Baumel A, Muench F, Edan S, Kane JM. Objective user engagement with mental health apps: systematic search and panel-based usage analysis. J Med Internet Res. Sep 25, 2019;21(9):e14567. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach; submitted 05.11.23; peer-reviewed by S Gordon; comments to author 30.11.23; revised version received 05.12.23; accepted 17.02.24; published 27.03.24.

©Melissa Vereschagin, Angel Y Wang, Chris G Richardson, Hui Xie, Richard J Munthali, Kristen L Hudec, Calista Leung, Katharine D Wojcik, Lonna Munro, Priyanka Halli, Ronald C Kessler, Daniel V Vigo. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Social work students doing it tough: new study

26 March 2024

Education and society , Faculty of Education and Social Work

Social work students in New Zealand are suffering from significant financial hardship, new research from the University of Auckland has revealed.

The financial distress faced by social work students required to complete 120 hours of unpaid practicum or ‘field education’ as part of their degree has been revealed in a new study.

Led by social work expert Professor Liz Beddoe from Waipapa Taumata Rau, University of Auckland, the study shows that social work students join nursing, teaching and medical students as people vital to our society who are nevertheless struggling to make ends meet to complete their qualifications, says Professor Beddoe.

“However, unlike medical students, social work students, who’re likely to enter the workforce on lower wages and have lower lifetime earnings, have yet to receive attention or support, despite social work being an area of national skill shortage," she says.

Based in the Faculty of Education and Social Work, Professor Beddoe has led the first national study of social work students and recent graduates in Aotearoa New Zealand to explore the issue of financial hardship and its effect on student wellbeing, with results just published in Social Work Education: The International Journal.

“It shows the process of qualifying for the profession risks making those students as vulnerable as the people they hope to serve,” she says.

“We recognised the need for strong evidence to support what is known anecdotally across the profession about the impact of financial hardship on social work students.”

Hence her team’s online survey, completed by 346 social work students and recent graduates from across the country.

The survey found that most participants (84%) had engaged in paid work while studying and nearly all relied on multiple forms of financial support, including wages, student loans, student allowances and financial support from their families/whānau.

Nearly two thirds (63%) had a student loan, with debt ranging from $2,000 to $100,000; the mean loan debt was $31,777.

As many as 90 percent had experienced anxiety about their student loan debt and those with a student loan of any size reported lower mental wellbeing than those without.

Participants were asked a range of questions on the level of financial hardship they experienced. For example, had they skipped meals because of not being able to pay for food, had they used a foodbank or applied for a hardship grant to buy food or struggled to pay essential childcare costs?

A third had experienced moderate financial hardship, and nearly seven percent had experienced severe financial hardship, says Professor Beddoe.

“Financial hardship had serious impacts on students’ mental and social wellbeing; more than half had sought medical advice on their mental health during their studies, and mental wellbeing was significantly lower for those reporting moderate or severe hardship compared to those reporting low financial hardship.”

She says those who had experienced severe financial hardship also experienced poorer social wellbeing.

“They reported not having enough time to do important activities with partners, children, or friends, having to limit engagement with cultural, community or marae activities, and having to sacrifice participation in hobbies, arts and sports because of cost and time constraints.”

Participants who had caring responsibilities – for children, unwell parents or partners, or other family members – experienced more intense financial hardship than others in the study.

“Our study showed that mental well-being was significantly lower for students reporting severe or extreme financial hardship, and that working while studying, which is nearly impossible while doing full-time social work practicum placements, doesn’t alleviate the negative effects of financial stress on wellbeing.”

Professor Beddoe says that while taking on student loan debt might enable more people to engage in tertiary education, the study showed the greatest predictor of social wellbeing among social work students was student debt.

“Indebtedness creates additional pressures that affect student wellbeing. Students who are parents, or who have other caring responsibilities, were more vulnerable to financial struggles and tended to have lower social wellbeing. Given the gendered nature of the social work profession, this is a significant and concerning finding which especially disadvantages women.”

It shows the process of qualifying for the profession risks making those students as vulnerable as the people they hope to serve.

Professor Liz Beddoe Faculty of Education and Social Work

In a follow-up study, she says the team ran a further student survey in 2023 which will provide a comparison with the 2019 data and which repeated most of the questions and expanded on them to understand how Covid-19 affected students and recent graduates.

“The team are currently analysing the new data which covers paid work, living situations, caring responsibilities, impact of any financial hardship, impact on caregiving responsibilities, social wellbeing and participation, physical and mental health, and any impacts of the Covid-19 pandemic on participants’ study.

The frustrating thing, she says, is solutions have already proposed in a 2022 briefing paper to the then-Minister for Social Development and Employment by the Aotearoa New Zealand Association of Social Workers (ANZASW).

These included establishing a specific student placement payment before the start of the 2023 academic year to bring students’ minimum earnings up to the level of at least the minimum training wage.

The paper also recommended increasing or removing the lifetime student allowance cap for professions where there is a skill shortage, like social work, and making a student allowance available to students completing professional masters degree training.

Establishing financial support for organisations offering field work education placements was another suggestion.

“Those recommendations were ignored, at least in part, we believe, due to a lack of strong evidence,” says Professor Beddoe.

She says the study has “painted a disturbing picture” of the plight of students training to work with society’s most vulnerable people.

Social work students in Aotearoa New Zealand: the impacts of financial hardship on mental and social wellbeing by Allen Bartley, Liz Beddoe, Ladan Hashemi, Mehdi Rahimi and Sophia de Fossard (2024) is published in Social Work Education: The International Journal.

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Long-term effects of bullying

Dieter wolke.

1 Department of Psychology and Division of Mental Health and Wellbeing, University of Warwick, Coventry, UK

Suzet Tanya Lereya

2 Department of Psychology, University of Warwick, Coventry, UK

Bullying is the systematic abuse of power and is defined as aggressive behaviour or intentional harm-doing by peers that is carried out repeatedly and involves an imbalance of power. Being bullied is still often wrongly considered as a ‘normal rite of passage’. This review considers the importance of bullying as a major risk factor for poor physical and mental health and reduced adaptation to adult roles including forming lasting relationships, integrating into work and being economically independent. Bullying by peers has been mostly ignored by health professionals but should be considered as a significant risk factor and safeguarding issue.

Definition and epidemiology

Bullying is the systematic abuse of power and is defined as aggressive behaviour or intentional harm-doing by peers that is carried out repeatedly and involves an imbalance of power , either actual or perceived, between the victim and the bully. 1 Bullying can take the form of direct bullying, which includes physical and verbal acts of aggression such as hitting, stealing or name calling, or indirect bullying, which is characterised by social exclusion (eg, you cannot play with us, you are not invited, etc) and rumour spreading. 2–4 Children can be involved in bullying as victims and bullies, and also as bully/victims, a subgroup of victims who also display bullying behaviour. 5 6 Recently there has been much interest in cyberbullying, which can be broadly defined as any bullying which is performed via electronic means, such as mobile phones or the internet. One in three children report having been bullied at some point in their lives, and 10–14% experience chronic bullying lasting for more than 6 months. 7 8 Between 2% and 5% are bullies and a similar number are bully/victims in childhood/adolescence. 9 Rates of cyberbullying are substantially lower at around 4.5% for victims and 2.8% for perpetrators (bullies and bully/victims), with up to 90% of the cyber-bullying victims also being traditionally (face to face) bullied. 10 Being bullied by peers is the most frequent form of abuse encountered by children, much higher than abuse by parents or other adult perpetrators 11 ( box 1 ).

Bullying screener

• Are threatened or blackmailed or have their things stolen
• Are insulted or get called nasty names
• Have nasty tricks played on them/are subject to ridicule
• Are hit, shoved around or beaten up
• Get deliberately left out of get-togethers, parties, trips or groups
• Have others ignore them, not wanting to be their friend anymore, or not wanting them around in their group
• Have nasty lies, rumours or stories told about them
• Have their private email, instant mail or text messages forwarded to someone else or have them posted where others can see them
• Have rumours spread about them online
• Get threatening or aggressive emails, instant messages or text messages
• Have embarrassing pictures posted online without their permission

(Answered for A, B, and C separately on this 4-point scale)

• Not much (1–3 times)
• Quite a lot (more than 4 times)
• A lot (at least once a week)

Victims : Happened to them: quite a lot/a lot; did to others: never/not much

Bully/victims : Happened to them: quite a lot/a lot; did to others: quite a lot/a lot

Bullies : Happened to them: never/not much; did to others: quite a lot/a lot

Adapted from refs 3 8 12 13

Bullying is not conduct disorder

Bullying is found in all societies, including modern hunter-gatherer societies and ancient civilisations. It is considered an evolutionary adaptation, the purpose of which is to gain high status and dominance, 14 get access to resources, secure survival, reduce stress and allow for more mating opportunities. 15 Bullies are often bi-strategic, employing both bullying and also acts of aggressive ‘prosocial’ behaviour to enhance their own position by acting in public and making the recipient dependent as they cannot reciprocate. 16 Thus, pure bullies (but not bully/victims or victims) have been found to be strong, highly popular and to have good social and emotional understanding. 17 Hence, bullies most likely do not have a conduct disorder. Moreover, unlike conduct disorder, bullies are found in all socioeconomic 18 and ethnic groups. 12 In contrast, victims have been described as withdrawn, unassertive, easily emotionally upset, and as having poor emotional or social understanding, 17 19 while bully/victims tend to be aggressive, easily angered, low on popularity, frequently bullied by their siblings 20 and come from families with lower socioeconomic status (SES), 18 similar to children with conduct disorder.

How bullies operate

Bullying occurs in settings where individuals do not have a say concerning the group they want to be in. This is the situation for children in school classrooms or at home with siblings, and has been compared to being ‘caged’ with others. In an effort to establish a social network or hierarchy, bullies will try to exert their power with all children. Those who have an emotional reaction (eg, cry, run away, are upset) and have nobody or few to stand up for them, are the repeated targets of bullies. Bullies may get others to join in (laugh, tease, hit, spread rumours) as bystanders or even as henchmen (bully/victims). It has been shown that conditions that foster higher density and greater hierarchies in classrooms (inegalitarian conditions), 21 at home 22 or even in nations, 23 increase bullying 24 and the stability of bullying victimisation over time. 25

Adverse consequences of being bullied

Until fairly recently, most studies on the effects of bullying were cross-sectional or just included brief follow-up periods, making it impossible to identify whether bullying is the cause or consequence of health problems. Thus, this review focuses mostly on prospective studies that were able to control for pre-existing health conditions, family situation and other exposures to violence (eg, family violence) in investigating the effects of being involved in bullying on subsequent health, self-harm and suicide, schooling, employment and social relationships.

Childhood and adolescence (6–17 years)

A fully referenced summary of the consequences of bullying during childhood and adolescence on prospectively studied outcomes up to the age of 17 years is shown in table 1 . Children who were victims of bullying have been consistently found to be at higher risk for common somatic problems such as colds, or psychosomatic problems such as headaches, stomach aches or sleeping problems, and are more likely to take up smoking. 39 40 Victims have also been reported to more often develop internalising problems and anxiety disorder or depression disorder. 31 Genetically sensitive designs allowed comparison of monozygotic twins who are genetically identical and live in the same households but were discordant for experiences of bullying. Internalising problems was found to have increased over time only in those who were bullied, 32 providing strong evidence that bullying rather than other factors explains increases in internalising problems. Furthermore, victims of bullying are at significantly increased risk of self-harm or thinking about suicide in adolescence. 43 44 Furthermore, being bullied in primary school has been found to both predict borderline personality symptoms 30 and psychotic experiences, such as hallucinations or delusions, by adolescence. 37 Where investigated, those who were either exposed to several forms of bullying or were bullied over long periods of time (chronic bullying) tended to show more adverse effects. 31 37 In contrast to the consistently moderate to strong relationships with somatic and mental health outcomes, the association between being bullied and poor academic functioning has not been as strong as expected. 51 A meta-analysis only indicated a small negative effect of victimisation on mostly concurrent academic performance and the effects differed whether bullying was self-reported or by peers or teachers. 47 Those studies that distinguished between victims and bully/victims usually reported that bully/victims had a slightly higher risk for somatic and mental health problems than pure victims. 41 52 Furthermore, most studies considered bullies and bully/victims together; however, as outlined above, the two roles are quite different with bullies often highly competent manipulators and ringleaders, while bully/victims are described as impulsive and poor in regulating their emotions. 53 We know little about the mental health outcomes of bullies in childhood, but there are some suggestions that they may also be at slightly increased risk of depression or self-harm, 33 45 however, less so than victims. Similarly, the relationship between being a bully and somatic health is weaker than in bully/victims, 39 or bullies have even been found to be healthier and stronger than children not involved in bullying. 41 Bullying perpetration has been found to increase the risk of offending in adolescence; 54 however, the analysis did not distinguish between bullies and bully/victims and did not include information about poly-victimisation (eg, being maltreated by parents). Bullies were also more likely to display delinquent behaviour and perpetrate dating violence by eighth grade. 50

Table 1

Consequences of involvement in bullying behaviour in childhood and adolescence on outcomes assessed up to 17 years of age

Childhood to adulthood (18–50 years)

Children who were victims of bullying have been consistently found to be at higher risk for internalising problems, in particular diagnoses of anxiety disorder 55 and depression 9 in young adulthood and middle adulthood (18–50 years of age) ( table 2 ). 56 Furthermore, victims were at increased risk for displaying psychotic experiences at age 18 8 and having suicidal ideation, attempts and completed suicides. 56 Victims were also reported to have poor general health, 65 including more bodily pain, headaches and slower recovery from illnesses. 57 Moreover, victimised children were found to have lower educational qualifications, be worse at financial management 57 and to earn less than their peers even at age 50. 56 69 Victims were also reported to have more trouble making or keeping friends and to be less likely to live with a partner and have social support. No association between substance use, anti-social behaviour and victimisation was found. The studies that distinguished between victims and bully/victims showed that usually bully/victims had a slightly higher risk for anxiety, depression, psychotic experiences, suicide attempts and poor general health than pure victims. 9 They also had even lower educational qualifications and trouble keeping a job and honouring financial obligations. 57 65 In contrast to pure victims, bully/victims were at increased risk for displaying anti-social behaviour and were more likely to become a young parent. 62 70 71 Again, we know less about pure bullies, but where studied, they were not found to be at increased risk for any mental or general health problems. Indeed, they were healthier than their peers, emotionally and physically. 9 57 However, pure bullies may be more deviant and more likely to be less educated and to be unemployed. 65 They have also been reported to be more likely to display anti-social behaviour, and be charged with serious crime, burglary or illegal drug use. 58 59 66 However, many of these effects on delinquency may disappear when other adverse family circumstances are controlled for. 57

Table 2

Consequences of involvement in bullying behaviour in childhood/adolescence on outcomes in young adulthood and adulthood (18–50 years)

The findings from prospective child, adolescent and adult outcome studies are summarised in figure 1 .

The impact of being bullied on functioning in teenagers and adulthood.

The carefully controlled prospective studies reviewed here provide a converging picture of the long-term effects of being bullied in childhood. First, the effects of being bullied extend beyond the consequences of other childhood adversity and adult abuse. 9 In fact, when compared to the experience of having been placed into care in childhood, the effects of frequent bullying were as detrimental 40 years later 56 ! Second, there is a dose–effect relationship between being victimised by peers and outcomes in adolescence and adulthood. Those who were bullied more frequently, 56 more severely (ie, directly and indirectly) 31 or more chronically (ie, over a longer period of time 8 ) have worse outcomes. Third, even those who stopped being bullied during school age showed some lingering effects on their health, self-worth and quality of life years later compared to those never bullied 72 but significantly less than those who remained victims for years (chronic victims). Fourth, where victims and bully/victims have been considered separately, bully/victims seem to show the poorest outcomes concerning mental health, economic adaptation, social relationships and early parenthood. 8 9 62 70 Lastly, studies that distinguished between bullies and bully/victims found few adverse effects of being a pure bully on adult outcomes. This is consistent with a view that bullies are highly sophisticated social manipulators who are callous and show little empathy. 73

There are a variety of potential routes by which being victimised may affect later life outcomes. Being bullied may alter physiological responses to stress, 74 interact with a genetic vulnerability such as variation in the serotonin transporter (5-HTT) gene, 75 or affect telomere length (ageing) or the epigenome. 76 Altered HPA-axis activity and altered cortisol responses may increase the risk for developing mental health problems 77 and also increase susceptibility to illness by interfering with immune responses. 78 In contrast, bullying may also differentially affect normal chronic inflammation and associated health problems that can persist into adulthood. 64 Chronically raised C-reactive protein (CRP) levels, a marker of low-grade systemic inflammation in the body, increase the risk of cardiovascular diseases, metabolic disorders and mental health problems such as depression. 79 Blood tests revealed that CRP levels in the blood of bullied children increased with the number of times they were bullied. Additional blood tests carried out on the children after they had reached 19 and 21 years of age revealed that those who were bullied as children had CRP levels more than twice as high as bullies, while bullies had CRP levels lower than those who were neither bullies nor victims ( figure 2 ). Thus, bullying others appears to have a protective effect consistent with studies showing lower inflammation for individuals with higher socioeconomic status 80 and studies with non-human primates showing health benefits for those higher in the social hierarchy. 81 The clear implication of these findings is that both ends of the continuum of social status in peer relationships are important for inflammation levels and health status.

Adjusted mean young adult C-reactive protein (CRP) levels (mg/L) based on childhood/adolescent bullying status. These values are adjusted for baseline CRP levels as well as other CRP-related covariates. All analyses used robust SEs to account for repeated observations (reproduced from Copeland et al 64 ).

Furthermore, experiences of threat by peers may alter cognitive responses to threatening situations. 82 Both altered stress responses and altered social cognition (eg, being hypervigilant to hostile cues 38 ) and neurocircuitry 83 related to bullying exposure may affect social relationships with parents, friends and co-workers. Finally, victimisation, in particular of bully/victims, affects schooling and has been found to be associated with school absenteeism. In the UK alone, over 16 000 young people aged 11–15 are estimated to be absent from state school with bullying as the main reason, and 78 000 are absent where bullying is one of the reasons given for absence. 84 The risk of failure to complete high school or college in chronic victims or bully/victims increases the risk of poorer income and job performance. 57

Summary and implications

Childhood bullying has serious effects on health, resulting in substantial costs for individuals, their families and society at large. In the USA, it has been estimated that preventing high school bullying results in lifetime cost benefits of over $1.4 million per individual. 85 In the UK alone, over 16 000 young people aged 11–15 are estimated to be absent from state school with bullying as the main reason, and 78 000 are absent where bullying is one of the reasons given for absence. 86 Many bullied children suffer in silence, and are reluctant to tell their parents or teachers about their experiences, for fear of reprisals or because of shame. 87 Up to 50% of children say they would rarely, or never, tell their parents, while between 35% and 60% would not tell their teacher. 11

Considering this evidence of the ill effects of being bullied and the fact that children will have spent much more time with their peers than their parents by the time they reach 18 years of age, it is more than surprising that childhood bullying is not at the forefront as a major public health concern. 88 Children are hardly ever asked about their peer relationships by health professionals. This may be because health professionals are poorly educated about bullying and find it difficult to raise the subject or deal with it. 89 However, it is important considering that many children abstain from school due to bullying and related health problems and being bullied throws a long shadow over their lives. To prevent violence against the self (eg, self-harm) and reduce mental and somatic health problems, it is imperative for health practitioners to address bullying.

Contributors: DW conceived the review, produced the first draft and revised it critically; STL contributed to the literature research and writing, and critically reviewed and approved the final version of the manuscript.

Funding: This review was partly supported by the Economic and Social Research Council (ESRC) grant ES/K003593/1.

Competing interests: None.

Provenance and peer review: Commissioned; externally peer reviewed.