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• Published: 04 April 2019

Verbal Abuse Related to Self-Esteem Damage and Unjust Blame Harms Mental Health and Social Interaction in College Population

• Je-Yeon Yun 1 , 2 ,
• Geumsook Shim   ORCID: orcid.org/0000-0001-8793-4719 3 &
• Bumseok Jeong   ORCID: orcid.org/0000-0001-6805-6535 3 , 4 , 5  

Scientific Reports volume  9 , Article number:  5655 ( 2019 ) Cite this article

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Verbal abuse is an emotional abuse intended to inflict intense humiliation-denigration-fear as perceived by exposed person. Network-based approaches have been applied to explore the integrative-segregated patterns of associations among the psychological features and external stimuli for diverse populations; few studies reported for verbal abuse effects in college population. Self-reporting measurements acquired form 5,616 college students were used for network analyses. Escalating cascades of verbal abuse from differential sources (parents, peers, or supervisors; network 1) and directed associations among verbal abuse severity-psychopathology-social interaction (network 2) were estimated using the directed acyclic graphs. Principal connectors of verbal abuse–psychopathology–social interaction were shown using the graph theory metrics calculated from the intra-individual covariance networks (network 3). Directed propagating patterns of verbal abuse phenomena differed by source (network 1). Severe peer-related verbal abuse affected psychomotor changes and influenced irritability (network 2). Verbal abuse of self-esteem damage and unjust blame served as connectors in the verbal abuse-psychopathology-social interaction; influence of smartphone overuse-related distress was stronger in cases with more severe verbal abuse (network 3). Verbal abuse that damages self-esteem and conveys unjust blame harms mental health and social interaction for college population.

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Introduction.

Verbal abuse is a form of emotional abuse intended to inflict intense humiliation, denigration, or extreme fear, as perceived by the victimised person 1 . Perceived parental verbal abuse in childhood and peer-related verbal abuse in adolescence have been associated with a risk of depressive mood, anxiety, anger-hostility, suicidality, dissociation, or drug use in young adulthood 2 , 3 , 4 , 5 , 6 . Moreover, experience of perceived verbal abuse has been associated with changed patterns of brain maturation, including the reduced structural integrity of brain white matter bundles 7 , compromised brain resting state functional connectivity 8 , 9 , and decreased brain grey matter volumes in regions responsible for sensory processing, emotional regulation, and social interaction-related cognitive functioning such as language and memory. All of the above factors have been suggested to reflect the neural underpinning of the psychopathology 10 , 11 , 12 , 13 , 14 , 15 , 16 . Further, perceived verbal abuse in adulthood in relation to intimate partner violence and workplace mistreatment also affects brain morphology and undermines mental health 17 , 18 . However, unlike the extreme clinical syndromes developing after trauma, such as post-traumatic stress disorder 19 , 20 , few studies have explored interactions among perceived, verbal abuse-psychopathology-social interaction patterns in young adult populations.

Using network-based approaches, integrative as well as segregated patterns of interactions among the psychopathology, cognitive functioning, and perceived external stimuli have been explored in various populations 19 , 21 , 22 , 23 . In such networks, each psychological feature is considered to be a node; these nodes are connected with edges that represent strengths (with or without directionalities) of relationships among the nodes that collectively comprise the network. Depending on the data characteristics and the aims of study, several formats of networks are available; the directed acyclic network (DAG; a directed and group-wise Bayesian network) 22 , 24 , a Gaussian graphical model (an undirected, partial correlation network in which edges represent group-wise relationships between ordinal or continuous variables) 25 , 26 , 27 , an Ising model (an undirected network estimating group-wise relationships among the dichotomous variables) 28 , 29 , and an intra-individual covariance network (an undirected network that describes inter-connectedness between psychological constructs within each participant) 23 .

To the best of the authors’ knowledge, this study is the first network-based approach that explored the escalating patterns of verbal abuse according to differential sources in addition to the directional associations between the severity of perceived verbal abuse versus the psychopathology and social interaction pattern. To explore the escalating cascades of perceived verbal abuse ( Network 1 ) and the directed relationships among the perceived verbal abuse–psychopathology–social interaction patterns ( Network 2 ) in college population, we used a dataset of self-reporting measurements acquired form 5,616 undergraduate and graduate students and retrieved the directed acyclic graphs (DAGs; the graphical structures of the Bayesian networks). The DAG defines probabilistic dependencies [shown as directional edges; based on the Markov property of Bayesian networks (=a direct dependence of every nodes only on their parental nodes)] among the components (visualized as nodes) of Bayesian network 30 ; alike in previous studies 19 , 21 , 22 that successfully uncovered the directed associations or causal relations among the diverse psychological features, the current study applied a score-based heuristic local search method of ‘hill-climbing’ as implemented in an R package bnlearn 30 . With hill-climbing algorithm, procedure for learning the graphical structure of Bayesian network (=DAG) starts from the initial solution of network structure and traverse the search space across the nodes by repeated attempts of network structure change - add, delete, or reverse of the directional edges that connect specific nodes with their neighboring nodes - to only reflect the changes of network structure (=edge) that greatly improves the fit of network to dataset 31 . Meanwhile, to keep the DAG from being trapped in local optima during the middle of hill-climbing-based searches, consensus-based solution of DAG is finally retrieved from the several runs of greedy search trials (each initiated from randomly chosen nodes) using hill-climbing; after learning the global probability distribution [=factorization of the joint probability distribution] of network, parameters of the local probability distributions for each nodes (conditional on the learned network structure) are estimated 30 . Further, to identify the principal components among the inter-variable covariation [=degree of similarity between two clinical variables in terms of the deviation from mean values (calculated from the whole participants) for each variable within an individual] of perceived verbal abuse–psychopathology–social interaction at the individual level, we retrieved global and local graph metrics 32 from the intra-individual covariance network 33 employing self-reporting measures for the same 5,616 students.

In this study, we first hypothesised that escalation of verbal abuse severity might differ by source (parents, peers, or supervisors). Also, we hypothesised that an influence cascade would emerge featuring the patterns of social interaction, the severity of perceived verbal abuse, and the intensity of psychological suffering (depressive mood, anxiety, substance abuse and inefficient cognitive style in daily living). Notably, previous studies found that poor perceived self-efficacy and perceived injustice triggered depression 34 , 35 , 36 , 37 , generalised and social anxiety 37 , 38 , 39 , 40 , 41 , addiction to alcohol and smartphone use 42 , 43 , and adult ADHD-like symptoms 44 , 45 , 46 . Certain verbal abuse components (attacks on self-efficacy or perceived injustice) were considered as candidate hubs connecting different components of the perceived, verbal abuse-psychopathology-social interaction patterns; thus, these were useful shortcuts.

Study population

We used de-identified responses for self-reporting questionnaires (please refer to the ‘Measures’ section) completed during annual healthcare screening of 5,616 undergraduate and graduate students between April 2014 and February 2015 at the KAIST Clinic ( https://clinic.kaist.ac.kr ). Participants ranged from 18 to 49 years of age (mean = 23.3 years, S.D. = 4.0 years). We evaluated 4,498 males (80.1%) and 1,118 females (19.9%). The Institutional Review Board at KAIST approved the current study (IRB approval no. KH-2012–16), and written informed consent was obtained from all subjects after the procedures had been fully explained. All procedures were performed in accordance with the ethical standards of the KAIST IRB on human experimentation and the Helsinki Declaration of 1975, as revised in 2008.

To measure the psychopathology of depressive mood (using the patient health questionnaire-9 (PHQ-9)) 47 , 48 , 49 , anxiety (by applying the generalised anxiety disorder 7-item (GAD-7)) 50 , 51 , 52 , substance abuse (alcohol; using the CAGE questionnaire) 53 , 54 , 55 , cognitive style of daily living (by applying the adult attention-deficit/hyperactivity disorder (ADHD) self-report scale (ASRS-v.1.1)) 44 , 45 , 46 , as well as social interaction patterns [of non-confrontational coping, including the anxiety-fear-avoidance for social situation 56 , 57 , 58 , 59 (the Liebowitz social anxiety scale (LSAS)) and preference for non-face-to-face social interaction combined with smartphone overuse 60 , 61 , 62 (the smartphone addiction scale (SAS))], that have been associated with perceived verbal abuse (by applying the verbal abuse questionnaire (VAQ)), we applied several self-reporting questionnaire listed below.

Depressive mood: Patient Health Questionnaire-9 (PHQ-9)

The PHQ-9 is a nine-item module assessing the severity of depressive symptoms, including low-level interest or pleasure, feeling down and hopeless, trouble sleeping, tiredness or having little energy, poor appetite/overeating, guilt, trouble concentrating, moving slowly/restlessness, and suicidal thoughts 63 . Here, the item-level responses for the Korean-validated version of PHQ-9 64 served as the nine depressive mood components (nodes) for network analyses 26 .

Anxiety: Generalised Anxiety Disorder 7-item (GAD-7)

The GAD-7 instrument features seven items exploring nervousness, uncontrollable worry, worrying about different things, trouble relaxing, restlessness, irritability, and the fear that something awful might happen; respondents report the severity of each symptom using a 4-point Likert scale [from 0 = ‘not at all’ to 3 = ‘nearly every day’] 65 . Here, item-level responses for the Korean-validated version of GAD-7 66 served as anxiety components (nodes) for network analyses 26 .

Social Interaction Pattern: Liebowitz Social Anxiety Scale (LSAS) & Smartphone Addiction Scale (SAS)

To explore the detailed aftereffect of verbal abuse on the exposed person’s social interaction pattern 67 , 68 , 69 , this study focused on non-confrontational coping for social interaction including the anxiety-fear-avoidance for social situation 56 , 57 , 58 , 59 (measured using the LSAS) and preference for non-face-to-face social interaction behind the smartphone overuse 60 , 61 , 62 (measured using the SAS).

The LSAS assesses the level of fear/anxiety associated with, and the severity of avoidance of, 24 social situations using a 4-point Likert scale [from 0 = ‘not at all’ to 3 = ‘very much’] 70 , 71 . Here, we used the self-reporting version of LSAS 72 , 73 , 74 and derived eight sub-domains that measured fear/anxiety or avoidance for ‘public speaking’ [items 20, 16, 6, 15, and 5], ‘social interaction with strangers’ [items 10, 11, and 12], ‘assertiveness’ [items 21, 22, 24, 18, and 14], and ‘public interaction’ [items 4, 1, 3, 7, 8, and 19] 75 .

The SAS is comprised of 46 items measuring various aspects of smartphone misuse using a 6-point Likert scale [from 1 = ‘not at all’ to 6 = ‘totally agree’] 76 . We derived four SAS sub-domains reflecting 1) daily life disturbance (due to the smartphone overuse), 2) positive anticipation (of emotional reward from smartphone use), 3) withdrawal (from the restriction of smartphone use), and 4) cyberspace-oriented relationships 76 . In the subsequent network analyses, these four sub-domains of SAS served as four nodes reflecting the smartphone-dependent patterns of social interaction that could be associated with experiential avoidance for real-world interaction 60 , difficulty of cognitive control for emotional processing in the middle of face-to-face interactions 77 , as well as loneliness and needs for social belonging combined with lower self-esteem 78 , 79 , 80 .

Substance abuse: CAGE questionnaire

The four items of the CAGE questionnaire focus on alcohol misuse, including a need to reduce drinking, perception of annoying criticism, guilty feelings, and use of alcohol as an eye-opener 81 , 82 . The total score served as the substance-mediated component of the social interaction feature of network analyses.

Cognitive style in daily living: Adult ADHD Self-Report Scale (ASRS-v1.1) Symptom Checklist

The ASRS-v.1.1 assesses attention-deficit/hyperactivity disorder (ADHD) symptoms using 18 DSM-IV symptom criteria 83 . We evaluated six Part A ASRS-v1.1 items including: 1) trouble finalising a project; 2) difficulty in organisation; 3) problems remembering appointments or obligations; 4) avoiding commencing tasks requiring a lot of thought; 5) fidgeting or squirming (hands or feet) when sitting for a long time; and, 6) feeling overly active and compelled to do things, as if driven by a motor. For each item, respondents reported the frequencies of such experiences over the prior six months, using five options (never, rarely, sometimes, often, or very often) 84 .

Perceived verbal abuse: Verbal Abuse Questionnaire (VAQ)

Lifetime (both earlier and recent) experiences of perceived verbal abuse from parents, supervisors, and peers were measured using the VAQ validated for the Korean college population 85 , 86 . The VAQ is composed of 15 items covering scolding, yelling, swearing, blaming, insulting, threatening, demeaning, ridiculing, criticising, and belittling; perceived severity was reported using a 9-point Likert scale [from 0 = ‘not at all’ to 8 = ‘everyday’] 85 , 86 .

Network 1: Directed acyclic graph of perceived verbal abuse components

To explore the differential patterns of perceived verbal abuse escalation 69 , 87 , 88 , 89 , 90 , 91 , 92 , 93 according to the source of parents, peers, or supervisors, using the hill-climbing algorithm provided by the R package bnlearn 22 , 24 , we derived three Bayesian networks (each comprised of the 15 items from the VAQ-parents, -peers, or -supervisors, respectively) embodied in DAGs. First, using the bootstrapping function, we extracted 10,000 samples (with replacement) and estimated an optimal network structure for a target goodness-of-fit score (e.g., the Bayesian Information Criterion (BIC) provided by bnlearn program for each edge comprising given network; larger absolute BIC value indicate the higher importance of specific edge for the integrity of network in explaining the data) 22 by randomly adding and removing edges connecting different VAQ items and reversing edge directionality 22 . Notably, to eliminate the possibility of a poor local BIC maximum, we repeated network start/estimation five times; each run included 10 perturbations of edge insertion/deletion or directionality reversal 22 . Only the subset of edges that appeared in at least 85% of the 10,000 networks was retained in the final averaged DAG network 22 , 94 . Second, the directionality of each edge in the final network was maintained in over 50% of the 10,000 bootstraps; the probability of edge direction reflects edge thickness (thicker or thinner than average) (Figs  1 – 2 ) 22 . The mean ± S.D. scores for VAQ-parents, VAQ-peers, and VAQ-supervisors were 3.2 ± 7.7 (range 0 to 99), 3.3 ± 7.5 (range 0 to 90), and 2.6 ± 6.4 (range 0 to 78), respectively. All of the procedures for estimation of network 1 were conducted using the modified version of the original R script provided from McNally, et al . (2017) and is provided in the supplementary material.

Directed acyclic networks formed using the 15 verbal abuse questionnaire (VAQ) components by ( A ) parents, ( B ) peers, and ( C ) supervisors. The abbreviations are described in Table  1 .

Directed acyclic network comprised of perceived, verbal abuse severity; psychopathology; and social interaction patterns. Perceived, verbal abuse severity components of parents (VAPa_total), peers (VAPeer_total), and supervisors (VAPro_total) are shown, as are six further components directly connected to these components (red arrows) including: 1) fidgeting when sitting for a long time (AD_05); 2) problems remembering appointments or obligations (AD_03); 3) fear of assertiveness (LSAS1_AST); 4) low levels of interest and/or pleasure (PHQ_01); 5) psychomotor change (PHQ_08); and 6) irritability (GAD_06); all are rimmed with yellow circles. The abbreviations are described in Table  1 .

Network 2: Directed acyclic graph of perceived verbal abuse severity (VAQ-parents, VAQ-peers, and VAQ-supervisors), psychopathology, and social interaction patterns

Using the procedure described above for Network 1 , we drew a group-wise DAG to explore the relationships between perceived verbal abuse severity by parents, peers, or supervisors (total scores for VAQ-parents, VAQ-peers, and VAQ-supervisors regardless of the timing of abuse) and depressive mood (the nine items of PHQ-9), anxiety (the seven items of GAD-7), social interaction patterns (the eight LSAS subscores for fear/anxiety or avoidance of public speaking, social interaction with strangers, assertiveness, and public interaction), the four SAS subscores related to smartphone addiction (daily life disturbance, positive anticipation, withdrawal, and cyberspace-oriented relationships), the four items of CAGE exploring problematic alcohol use, and the six items of ASRS-v.1.1 (part A) (difficulty with completion, forgetfulness, procrastination, and hyperactivity) 26 , 84 , 95 , 96 , 97 , 98 . The mean ± S.Ds. of the 41 items (=nodes) of Network 2 are listed in Table  1 . The network analyses procedures for estimation of network 2 were conducted using the modified version of the original R script provided from McNally, et al . (2017) as shown in the supplementary material.

Network 3: Intra-individual covariance network of perceived verbal abuse components (regardless of source), psychopathology, and social interaction patterns

We constructed intra-individual covariance networks of clinical features 23 that reflect the diverse aspects of perceived verbal abuse (the 15 item-level VAQ scores for parental, peer, and supervisor abuse), depressive mood (the nine item-level scores of PHQ-9), anxiety (the seven item-level scores for GAD-7), and social interaction patterns (the eight LSAS subscores, the four SAS subscores, the total CAGE score [the sum of the four values included in network 2]; and the scores for the six items of ASRS-v.1.1 part A) by way of 50 components (=network nodes; Table  1 ). First, the raw scores were z-transformed using the overall means and standard deviations (derived from the whole group of N  = 5,616) per clinical feature, as the range of score distribution differs across 50 clinical features comprising the intra-individual covariance network 23 . Second, intra-individual covariances (=network edges) between the 50 clinical features in k th participant ( k  = from 1 to 5,616) were calculated using the inverse exponential function that involves the square of the difference between the z-score transformed values of i th clinical feature ( z(i , k) , i  = from 1 to 50) and j th clinical feature ( z(j , k) , j  = from 1 to 50) as below 23 .

This formula enables the structural covariance values (=weight of edges in the intra-individual covariance network) to be distributed within the range of 0 and 1, in proportional to the degree of similarity between two different clinical features (=nodes that comprise intra-individual covariance network) per participant. The means ± S.Ds. for the 50 components (nodes) of Network 3 are listed in Table  1 . The Matlab script (=.m file) used for calculation of network 3 is provided as supplementary material.

Graph theory analyses of Network 3 (an intra-individual covariance network)

To identify the most influential components 99 , 100 which mediate the propagation of information as shortcuts in the midst of numerous inter-connected components of verbal abuse-psychopathology-social interaction, the current study estimated a local graph metric named ‘betweenness centrality (=the frequency with which a node is located in the path of a shortcut connecting two different nodes) 99 , 101 ’. The optimal level of network sparsity [ K ; defined as the proportion of non-zero edges relative to the total possible number of connections (= N  × ( N  − 1)/2; N  = number of nodes) in the network] appropriate for deriving the betweenness centrality values were searched under the three criteria 102 , 103 of (1) small-world organisation [balanced network for global integration as well as local segregation 103 , 104 ; satisfied when small-worldness (σ) > 1] 102 , 105 , 106 , (2) modular organisation [network could be subdivided into communities 103 , 107 ; sufficient when modularity (Q) >0.3) 102 ], and (3) network connectedness [over 80% of the total (=50) nodes were connected to other nodes] in more than 95% of participants ( N  = 5,616) 33 .

Accordingly, four global graph metrics including (a) normalised clustering coefficient [γ; first measured per node using ‘clustering_coef_wu.m 108 ’ and then averaged over all 50 nodes in a given network, and finally normalised using the same variable averaged over 10,000 random networks produced from the original network employing ‘randmio_und.m 109 ’]; (b) normalised characteristic path length [λ; retrieved from the distance matrix (in which all edge strengths were inverted compared to the original network by way of ‘distance_wei.m’) using ‘charpath.m 106 ’, and finally normalised using the same variable calculated from 10,000 random networks alike (a)]; (c) small-worldness [ σ  =  γ / λ ] 106 ; and, (d) modularity [ Q ; derived by averaging 500 estimations obtained using ‘modularity_und.m’] were calculated. Finally, in the network sparsity ranges of K  = 0.10–0.21 that satisfied (1) small-world organisation, (2) modular organisation, and (3) network connectedness for more than 95% of participants, the local graph metric of betweenness centrality was calculated (using the ‘betweenness_wei.m 99 , 101 ’) at the connection density level of K  = 0.10; all of the intra-individual covariance networks (network 3) were transformed thresholding (by way of ‘threshold_proportional.m 110 ’) to be fitted to the network sparsity level to K  = 0.10 in which only the subset of edges having strongest edges weights (=connectivity strength) remained.

In a scale-free network, the centrality values do not follow a normal distribution. Therefore, after per-participant rank-transformation of the betweenness centrality values using the ‘tiedrank.m’ function of Matlab R2017a, the top 12%-ranked six (=50 nodes × 0.12) nodes in >25% of participants ( n  = 5,616) were defined as hub nodes (in line with previous studies 111 , 112 that defined hubs as the top 12% of most consistently ranked nodes for centrality value across the group of participants) for intra-individual covariance networks of ‘verbal abuse-psychopathology-social interaction’. Finally, relationships between the severity of perceived verbal abuse (=total score of VAQ) by parents, peers, or supervisors versus the rank-transformed betweenness centralities of 35 nodes [=15 VAQ components were excluded from the original 50 nodes of network 3, as associations (if any) between verbal abuse severity and betweenness centralities of VAQ nodes would be auto-regressive; P  < 0.05/35 = 0.001] comprising the intra-individual covariance networks were explored using the Spearman’s rank correlation coefficients (estimated using the ‘corr(‘type’ = ‘Spearman’)’ function of Matlab R2017a). All of the global and local graph metrics were calculated using the Matlab script (=.m file; mainly written with functions of the Brain Connectivity Toolbox 106 ) provided as supplementary material.

Network 1: A directed acyclic graph of perceived verbal abuse components

These three DAG networks explored the differential cascades 19 , 22 , 113 of perceived verbal abuse escalation 69 , 87 , 88 , 89 , 90 , 91 , 92 , 93 according to the source of parents (Fig.  1A ), peers (Fig.  1B ), or supervisors (Fig.  1C ). In the DAG reflecting perceived parental verbal abuse (Fig.  1A ), verbal aggression commenced with ‘scolded me’ (VAPa_01) and ‘yelled at me’ (VAPa_02). Subsequently, (without any intervention for escalation of parental verbal abuse exposure) the two hub components of ‘insulted me’ (VAPa_05) and ‘told me that I was useless’ (VAPa_13) influenced eight and six other downward DAG network components, respectively; perceived parental verbal abuse finally evolved into ‘said I was stupid’ (VAPa_08).

On the contrary, in the DAG of perceived verbal abuse by peers (Fig.  1B ), verbal aggression started as ‘blamed me for something’ (VAPeer_04) and ‘insulted me’ (VAPeer_05) and subsequently propagated into eight and six components, respectively. When additional exposure to other forms of peer-related perceived abuse such as ‘swore at me’ (VAPeer_03; probabilities of affecting the VAPeer_06 90.1% and the VAPeer_08 78.0%), ‘humiliated me in front of others’ (VAPeer_10; probability of affecting the VAPeer_08 93.7%), and ‘blamed me for what I did not do’ (VAPeer_09; probabilities of affecting the VAPeer_06 85.0% and the VAPeer_08 79.0%) were not interrupted or stopped (either by the targeted person or another), finally the victimised person might suffer more severe verbal threats by peers including ‘threatened to hit me (VAPeer_06) and/or ‘said I was stupid’ (VAPeer_08).

In cases of the supervisor-related verbal abuse (Fig.  1C ), a form of perceived verbal aggression ‘insulted me’ (VAPro_05) might be the initial component of appearance as shown in the DAG. On the one hand, escalated supervisor-related verbal insults were perceived as ‘raising one’s (=supervisor’s) voice’ (VAPro_15); on the other hand, activation of the verbal abuse supplied by supervisor(s) such as ‘blamed me for something’ (VAPro_04) and ‘criticised me’ (VAPro_11) were shortcuts that activated other six and five downstream components of supervisor-related verbal abuse, respectively. Without efforts to prevent the targeted person from being exposed to ‘swore at me’ (VAPro_03; probability of affecting the VAPro_06 87.8%) or ‘yelled at me without reason’ (VAPro_12; probability of activating the VAPro_06 69.3%) or ‘used a nickname that insulted me’ (VAPro_07; probability of affecting the VAPro_06 55.5%), threat of physical harm such as hitting (VAPro_06) might occur.

Network 2: Directed relationships of the perceived verbal abuse–psychopathology-social interaction patterns

Next, the patterns of interaction between perceived verbal abuse (VAQ-parents, -peers, and -supervisors scores, evaluated separately) and depressive mood, anxiety, social interactions, alcohol abuse, and inattention-hyperactivity (as estimated by DAGs) were examined (Fig.  2 ). First, the severity of perceived, parental verbal abuse (VAPa_total) was directly influenced by the intensity of hyperactivity (‘fidgeting or squirming with the hands or feet when you have to sit for a long time’ (AD_05), with a probability of 86.9%. Second, the severity of peer-related, perceived verbal abuse (VAPeer_total) was affected from fear of assertiveness in social situations (LSAS1_AST; probability 96.0%), and inattention and problems remembering appointments or obligations (AD_03; probability of 88.2%), as did perceived, parental verbal abuse (VAPa_total; probability 63.2%). Third, the severity of supervisor-related, perceived verbal abuse (VAPro_total) was accompanied by preceding low interest or pleasure in doing things (PHQ_01; 97.5% probability), as was perceived verbal abuse from other sources (probabilities of 82.4% for ‘VAPa_total’ and 89.8% for ‘VAPeer_total’). Furthermore, the severity of peer-related, perceived verbal abuse affected psychomotor retardation/agitation (PHQ_08; probability 50.1%) and influenced the activation of irritability (GAD_06; probability 65.1%).

Graph theory analyses for Network 3 (intra-individual covariance network)

The graph theory approach of the intra-individual covariance network ( N  = 5,616) comprised of perceived verbal abuse (15 item-level VAG scores averaged for the three sources), psychopathology, and social interaction patterns, retrieved six highly influential components (in terms of mediating the propagation of information as shortcuts among the numerous inter-connected components within the network 99 , 100 ; the top 12% ranked variables in terms of rank-transformed betweenness centrality in >25% of participants at a network sparsity of K  = 10) including low interest or pleasure in doing things (PHQ_01), changed appetite (PHQ_05), nervousness (GAD_01), restlessness (GAD_05), blaming oneself for what one did not do (VAQavg_09), and feeling worthless (VAQavg_14) (Fig.  3 ) . Further, significant relationships between the VAQ total scores and the rank-transformed betweenness centralities of daily life disturbance (Spearman’s rho  = −0.326, −0.339, −0.325; P -values = 2.16 × 10 −139 , 7.26 × 10 −151 , 1.79 × 10 −138; for VAQ-parents, -peers, and -supervisors, respectively) and withdrawal caused by smartphone addiction ( rho  = −0.338, −0.336, −0.316; P- values = 3.61 × 10 −150 , 3.84 × 10 −148 , 9.33 × 10 −131 for VAQ-parents, -peers, and -supervisors, respectively), were also evident (Fig.  4A–F ; significant at P  < 0.05/35 [number of nodes in network 3 that were not VAQ components] = 0.001; fitted curves with linear regression including polynomial terms of squared and cubic predictors (estimated using the ‘polyfit’ function of Matlab R2017a) also illustrated).

Heatmap (upper) and violin plot (lower) of rank-transformed betweenness centrality values calculated from the intra-individual covariance network ( N  = 5,616) featuring perceived verbal abuse components (averaged over parents, peers, and supervisors), psychopathology, and social interaction patterns. In the x-axis of the violin plot, the six most influential components (hubs; the top 12% nodes for rank-transformed betweenness centrality in >25% of participants at a network sparsity level of K  = 0.1) are: 1) low-level interest or pleasure; 2) poor appetite or overeating; 3) nervousness; 4) restlessness; 5) blaming oneself for what one has not done; and, 6) feeling worthless, are coloured brown and marked with asterisks.

Correlations between total scores on the verbal abuse questionnaire versus the rank-transformed betweenness centralities of daily-life disturbance ( A,B,C ) and withdrawal caused by smartphone overuse ( D,E,F ) calculated from the intra-individual covariance networks (at network sparsity level of K  = 0.10) of perceived verbal abuse, psychopathology, and social interaction patterns (all Ps  < 0.001). Brown-colored polynomial curves (degree of polynomial fit = 3) were fitted using the ‘polyfit’ function of Matlab R2017a.

To the best of the authors’ knowledge, this study is the first network-based approach that explored the directed associations among the perceived verbal abuse severity and ‘depressive mood-anxiety-social interaction’ patterns as well as the principal drivers (hubs) in the intra-individual covariance networks of ‘perceived verbal abuse–psychopathology-social interaction’ patterns. The severity of peer-related verbal abuse created a fear of assertiveness and difficulty in remembering appointments or obligations, followed by psychomotor changes and irritability (Fig.  2 ). Moreover, in addition to the four depressive mood-anxiety components, perceived verbal abuse such as ‘blaming me for what I did not do’ and ‘making me feel worthless’ were hubs connecting differential components of the intra-individual covariance networks (Fig.  3 ). Of note, the intensity of perceived verbal abuse correlated with the hubness of daily-life disturbance and withdrawal caused by smartphone misuse (Fig.  4 ). Use of relatively large number of responses ( N  = 5,616) acquired by way of the well-validated self-reporting questionnaires enhanced the power of study results. Further, application of item-level responses or sub-domain scores (comprising the full scales) as nodes in the network-based approach of ‘perceived verbal abuse-psychopathology-social interaction’ improved the resolution of study results so that the most influential psychological items in the network could be profiled as hubs. These study results raise the importance of psychoeducation facilitating nonviolent empathetic communication, training in self-protection from verbal abusers, and timely psychological aid for victimised persons focusing on the dysphoric mood, fear of assertiveness, and smartphone misuse, at least for college population.

Damaged self-esteem and unjust blame: shortcuts of verbal abuse–psychopathology-social interactions

Verbal abuse that makes one feel worthless (damaged self-esteem) or blames a person for something s/he did not do (unjust blame) were shortcuts connecting different components of the intra-individual covariance networks comprised of the ‘verbal abuse-depressive mood-anxiety-social interaction’ patterns (Fig.  3 ). Humiliation is a predictor of depression 114 , as is interpersonal sensitivity 115 . When a humiliating experience leads to a fear of further humiliation, a victimised person may become increasingly sensitive to social threats and social anxiety cues 115 . What is worse, poor assertiveness in social situations may create a defensive silence even when verbal abuse is ongoing 116 , as shown here (Fig.  2 ). In unfamiliar or uneasy social situations, alcohol is used to reduce anxiety 117 and to supply an emotional reward 118 ; however, victimisation by others in social situations may trigger alcohol misuse 119 , 120 . Further, the perception of severe verbal abuse was associated with increased smartphone addiction more so than other ‘verbal abuse-depressive mood-anxiety-social interaction’ patterns (Fig.  4 ), in agreement with the results of previous studies suggesting a possible role for smartphone addiction in avoidance of social exclusion-related distress 121 , as a medium for social rehearsal and monitoring 122 , or as an alternative source of a sense of belonging 78 .

Mood disturbances such as psychomotor changes and irritability: Aftermaths of peer-related verbal abuse

The severity of peer-related verbal abuse affected the extent of psychomotor change and irritability (Fig.  2 ), escalating depressive mood as previously reported 3 . In other words, post-traumatic anger expression may be either externalised as behavioural aggression-irritability or internalised as depressive mood-anxiety 123 ; however, these seemingly contrasting phenotypes might be similarly underpinned neurally via reduced integrity of cingulum bundle white matter in the posterior tail of the left hippocampus 10 . The effect of time cannot be modelled with our cross-sectional data; however, the DAG suggests that causal hypotheses are testable via intervention. In addition to caring for the psychological distress caused by verbal abuse 124 , educational efforts reducing factors preceding such abuse, including the fear of assertiveness and appointment/obligation forgetfulness (Fig.  2 ), are desirable. It is necessary to enhance assertiveness and communication 125 , 126 and to develop behavioural skills enhancing the social-organisational-attentional subdomains 127 .

Limitations

Our study had certain limitations. First, the precise timing of verbal abuse was not considered. Rather, participants in early adulthood were asked to report the lifelong frequencies of diverse, perceived verbal abuses, regardless of exposure times. Although timepoint resolution was thus absent, and verbal abuse may have decreased over time, we derived more generalisable, abstract patterns of verbal abuse propagation and the relationships thereof with psychological health and social interactions; we did not focus on differential total lifetime exposure, timing, or duration 67 , 128 , 129 . Second, the sex ratio of current study population was not balanced (we evaluated 4,498 males (80.1%) and 1,118 females (19.9%)). As a matter of fact, rather than finding the sex-related differences of verbal abuse-psychopathology-social interaction interactions, we combined the study population for network analyses so that both male and female college students integrated to estimate the group-wise DAGs that unveiled the escalating cascades of verbal abuse (network 1) as well as the interacting patterns of verbal abuse-psychopathology-social interaction (network 2). Considering the possible sex-related differences in response to traumatic life events 130 , 131 , further network-based studies for which larger-sized population with equal proportion of male and female satisfied are required. Third, we did not explore the neural correlates of abuse exposure or interactions between such abuse compared to other psychopathologies. Such brain-based information 12 would aid our understanding of the biological mechanisms underlying directed 26 and hierarchical 23 networks featuring traumatic experiences, psychological health, and social interaction patterns. Fourth, we did not explore the relationship between perceived verbal abuse and neurocognitive abilities 132 , 133 . Future studies measuring both factors will reveal network-based interactions not only between perceived verbal abuse and psychological health and social interaction patterns (explored in this study) but also between abuse and both cognitive ability and academic achievement.

We studied 5,616 college students in terms of the propagation patterns of perceived verbal abuse components and found possible directed relationships that hypothetically could propagate from the perceived verbal abuse to the psychomotor changes and irritability 134 . Further, graph theory metrics calculated from the intra-individual covariance networks demonstrated the hubness of some forms of verbal abuse including ‘damaged self-esteem’ and ‘unjust blame’; the hubs served as shortcuts connecting different ‘verbal abuse-depressive mood-anxiety-social interaction’ features. The importance of smartphone misuse-related distress as a shortcut connecting these features was greater for participants who suffered more from perceived verbal abuse. Psychoeducation facilitating nonviolent empathetic communication 135 , 136 , training in self-protection from verbal abusers 137 , and timely psychological aid for victimised persons focusing on the dysphoric mood, fear of assertiveness, and smartphone misuse 138 , are required.

Data Availability

The authors will make materials, data and associated protocols promptly available to readers without undue qualifications in material transfer agreements.

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Acknowledgements

This research was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT (NRF-2016M3C7A1914448, NRF-2017M3C7A1031331) and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03028464).

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J.Y.Y., G.S., and B.S.J. designed the study and wrote the protocol; G.S., and B.S.J. recruited subjects and collected self-report measures; J.Y.Y. managed the literature searches, undertook the whole procedure of data analyses and wrote the first draft of the manuscript. All authors reviewed and approved the final manuscript.

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Yun, JY., Shim, G. & Jeong, B. Verbal Abuse Related to Self-Esteem Damage and Unjust Blame Harms Mental Health and Social Interaction in College Population. Sci Rep 9 , 5655 (2019). https://doi.org/10.1038/s41598-019-42199-6

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• 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.

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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.

Open access funding provided by Karolinska Institute. None to declare.

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Stockholm Prevents Alcohol and Drug Problems (STAD), Center for Addiction Research and Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden

Håkan Källmén

Epidemiology of Psychiatric Conditions, Substance Use and Social Environment (EPiCSS), Department of Global Public Health, Karolinska Institutet, Level 6, Solnavägen 1e, Solna, Sweden

Mats Hallgren

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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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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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Preventing Bullying Through Science, Policy, and Practice (2016)

Chapter: 1 introduction, 1 introduction.

Bullying, long tolerated by many as a rite of passage into adulthood, is now recognized as a major and preventable public health problem, one that can have long-lasting consequences ( McDougall and Vaillancourt, 2015 ; Wolke and Lereya, 2015 ). Those consequences—for those who are bullied, for the perpetrators of bullying, and for witnesses who are present during a bullying event—include poor school performance, anxiety, depression, and future delinquent and aggressive behavior. Federal, state, and local governments have responded by adopting laws and implementing programs to prevent bullying and deal with its consequences. However, many of these responses have been undertaken with little attention to what is known about bullying and its effects. Even the definition of bullying varies among both researchers and lawmakers, though it generally includes physical and verbal behavior, behavior leading to social isolation, and behavior that uses digital communications technology (cyberbullying). This report adopts the term “bullying behavior,” which is frequently used in the research field, to cover all of these behaviors.

Bullying behavior is evident as early as preschool, although it peaks during the middle school years ( Currie et al., 2012 ; Vaillancourt et al., 2010 ). It can occur in diverse social settings, including classrooms, school gyms and cafeterias, on school buses, and online. Bullying behavior affects not only the children and youth who are bullied, who bully, and who are both bullied and bully others but also bystanders to bullying incidents. Given the myriad situations in which bullying can occur and the many people who may be involved, identifying effective prevention programs and policies is challenging, and it is unlikely that any one approach will be ap-

propriate in all situations. Commonly used bullying prevention approaches include policies regarding acceptable behavior in schools and behavioral interventions to promote positive cultural norms.

STUDY CHARGE

Recognizing that bullying behavior is a major public health problem that demands the concerted and coordinated time and attention of parents, educators and school administrators, health care providers, policy makers, families, and others concerned with the care of children, a group of federal agencies and private foundations asked the National Academies of Sciences, Engineering, and Medicine to undertake a study of what is known and what needs to be known to further the field of preventing bullying behavior. The Committee on the Biological and Psychosocial Effects of Peer Victimization:

Lessons for Bullying Prevention was created to carry out this task under the Academies’ Board on Children, Youth, and Families and the Committee on Law and Justice. The study received financial support from the Centers for Disease Control and Prevention (CDC), the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the Health Resources and Services Administration, the Highmark Foundation, the National Institute of Justice, the Robert Wood Johnson Foundation, Semi J. and Ruth W. Begun Foundation, and the Substance Abuse and Mental Health Services Administration. The full statement of task for the committee is presented in Box 1-1 .

Although the committee acknowledges the importance of this topic as it pertains to all children in the United States and in U.S. territories, this report focuses on the 50 states and the District of Columbia. Also, while the committee acknowledges that bullying behavior occurs in the school

environment for youth in foster care, in juvenile justice facilities, and in other residential treatment facilities, this report does not address bullying behavior in those environments because it is beyond the study charge.

CONTEXT FOR THE STUDY

This section of the report highlights relevant work in the field and, later in the chapter under “The Committee’s Approach,” presents the conceptual framework and corresponding definitions of terms that the committee has adopted.

Historical Context

Bullying behavior was first characterized in the scientific literature as part of the childhood experience more than 100 years ago in “Teasing and Bullying,” published in the Pedagogical Seminary ( Burk, 1897 ). The author described bullying behavior, attempted to delineate causes and cures for the tormenting of others, and called for additional research ( Koo, 2007 ). Nearly a century later, Dan Olweus, a Swedish research professor of psychology in Norway, conducted an intensive study on bullying ( Olweus, 1978 ). The efforts of Olweus brought awareness to the issue and motivated other professionals to conduct their own research, thereby expanding and contributing to knowledge of bullying behavior. Since Olweus’s early work, research on bullying has steadily increased (see Farrington and Ttofi, 2009 ; Hymel and Swearer, 2015 ).

Over the past few decades, venues where bullying behavior occurs have expanded with the advent of the Internet, chat rooms, instant messaging, social media, and other forms of digital electronic communication. These modes of communication have provided a new communal avenue for bullying. While the media reports linking bullying to suicide suggest a causal relationship, the available research suggests that there are often multiple factors that contribute to a youth’s suicide-related ideology and behavior. Several studies, however, have demonstrated an association between bullying involvement and suicide-related ideology and behavior (see, e.g., Holt et al., 2015 ; Kim and Leventhal, 2008 ; Sourander, 2010 ; van Geel et al., 2014 ).

In 2013, the Health Resources and Services Administration of the U.S. Department of Health and Human Services requested that the Institute of Medicine 1 and the National Research Council convene an ad hoc planning committee to plan and conduct a 2-day public workshop to highlight relevant information and knowledge that could inform a multidisciplinary

___________________

1 Prior to 2015, the National Academy of Medicine was known as the Institute of Medicine.

road map on next steps for the field of bullying prevention. Content areas that were explored during the April 2014 workshop included the identification of conceptual models and interventions that have proven effective in decreasing bullying and the antecedents to bullying while increasing protective factors that mitigate the negative health impact of bullying. The discussions highlighted the need for a better understanding of the effectiveness of program interventions in realistic settings; the importance of understanding what works for whom and under what circumstances, as well as the influence of different mediators (i.e., what accounts for associations between variables) and moderators (i.e., what affects the direction or strength of associations between variables) in bullying prevention efforts; and the need for coordination among agencies to prevent and respond to bullying. The workshop summary ( Institute of Medicine and National Research Council, 2014c ) informs this committee’s work.

Federal Efforts to Address Bullying and Related Topics

Currently, there is no comprehensive federal statute that explicitly prohibits bullying among children and adolescents, including cyberbullying. However, in the wake of the growing concerns surrounding the implications of bullying, several federal initiatives do address bullying among children and adolescents, and although some of them do not primarily focus on bullying, they permit some funds to be used for bullying prevention purposes.

The earliest federal initiative was in 1999, when three agencies collaborated to establish the Safe Schools/Healthy Students initiative in response to a series of deadly school shootings in the late 1990s. The program is administered by the U.S. Departments of Education, Health and Human Services, and Justice to prevent youth violence and promote the healthy development of youth. It is jointly funded by the Department of Education and by the Department of Health and Human Services’ Substance Abuse and Mental Health Services Administration. The program has provided grantees with both the opportunity to benefit from collaboration and the tools to sustain it through deliberate planning, more cost-effective service delivery, and a broader funding base ( Substance Abuse and Mental Health Services Administration, 2015 ).

The next major effort was in 2010, when the Department of Education awarded $38.8 million in grants under the Safe and Supportive Schools (S3) Program to 11 states to support statewide measurement of conditions for learning and targeted programmatic interventions to improve conditions for learning, in order to help schools improve safety and reduce substance use. The S3 Program was administered by the Safe and Supportive Schools Group, which also administered the Safe and Drug-Free Schools and Communities Act State and Local Grants Program, authorized by the

1994 Elementary and Secondary Education Act. 2 It was one of several programs related to developing and maintaining safe, disciplined, and drug-free schools. In addition to the S3 grants program, the group administered a number of interagency agreements with a focus on (but not limited to) bullying, school recovery research, data collection, and drug and violence prevention activities ( U.S. Department of Education, 2015 ).

A collaborative effort among the U.S. Departments of Agriculture, Defense, Education, Health and Human Services, Interior, and Justice; the Federal Trade Commission; and the White House Initiative on Asian Americans and Pacific Islanders created the Federal Partners in Bullying Prevention (FPBP) Steering Committee. Led by the U.S. Department of Education, the FPBP works to coordinate policy, research, and communications on bullying topics. The FPBP Website provides extensive resources on bullying behavior, including information on what bullying is, its risk factors, its warning signs, and its effects. 3 The FPBP Steering Committee also plans to provide details on how to get help for those who have been bullied. It also was involved in creating the “Be More than a Bystander” Public Service Announcement campaign with the Ad Council to engage students in bullying prevention. To improve school climate and reduce rates of bullying nationwide, FPBP has sponsored four bullying prevention summits attended by education practitioners, policy makers, researchers, and federal officials.

In 2014, the National Institute of Justice—the scientific research arm of the U.S. Department of Justice—launched the Comprehensive School Safety Initiative with a congressional appropriation of $75 million. The funds are to be used for rigorous research to produce practical knowledge that can improve the safety of schools and students, including bullying prevention. The initiative is carried out through partnerships among researchers, educators, and other stakeholders, including law enforcement, behavioral and mental health professionals, courts, and other justice system professionals ( National Institute of Justice, 2015 ).

In 2015, the Every Student Succeeds Act was signed by President Obama, reauthorizing the 50-year-old Elementary and Secondary Education Act, which is committed to providing equal opportunities for all students. Although bullying is neither defined nor prohibited in this act, it is explicitly mentioned in regard to applicability of safe school funding, which it had not been in previous iterations of the Elementary and Secondary Education Act.

The above are examples of federal initiatives aimed at promoting the

2 The Safe and Drug-Free Schools and Communities Act was included as Title IV, Part A, of the 1994 Elementary and Secondary Education Act. See http://www.ojjdp.gov/pubs/gun_violence/sect08-i.html [October 2015].

3 For details, see http://www.stopbullying.gov/ [October 2015].

healthy development of youth, improving the safety of schools and students, and reducing rates of bullying behavior. There are several other federal initiatives that address student bullying directly or allow funds to be used for bullying prevention activities.

Definitional Context

The terms “bullying,” “harassment,” and “peer victimization” have been used in the scientific literature to refer to behavior that is aggressive, is carried out repeatedly and over time, and occurs in an interpersonal relationship where a power imbalance exists ( Eisenberg and Aalsma, 2005 ). Although some of these terms have been used interchangeably in the literature, peer victimization is targeted aggressive behavior of one child against another that causes physical, emotional, social, or psychological harm. While conflict and bullying among siblings are important in their own right ( Tanrikulu and Campbell, 2015 ), this area falls outside of the scope of the committee’s charge. Sibling conflict and aggression falls under the broader concept of interpersonal aggression, which includes dating violence, sexual assault, and sibling violence, in addition to bullying as defined for this report. Olweus (1993) noted that bullying, unlike other forms of peer victimization where the children involved are equally matched, involves a power imbalance between the perpetrator and the target, where the target has difficulty defending him or herself and feels helpless against the aggressor. This power imbalance is typically considered a defining feature of bullying, which distinguishes this particular form of aggression from other forms, and is typically repeated in multiple bullying incidents involving the same individuals over time ( Olweus, 1993 ).

Bullying and violence are subcategories of aggressive behavior that overlap ( Olweus, 1996 ). There are situations in which violence is used in the context of bullying. However, not all forms of bullying (e.g., rumor spreading) involve violent behavior. The committee also acknowledges that perspective about intentions can matter and that in many situations, there may be at least two plausible perceptions involved in the bullying behavior.

A number of factors may influence one’s perception of the term “bullying” ( Smith and Monks, 2008 ). Children and adolescents’ understanding of the term “bullying” may be subject to cultural interpretations or translations of the term ( Hopkins et al., 2013 ). Studies have also shown that influences on children’s understanding of bullying include the child’s experiences as he or she matures and whether the child witnesses the bullying behavior of others ( Hellström et al., 2015 ; Monks and Smith, 2006 ; Smith and Monks, 2008 ).

In 2010, the FPBP Steering Committee convened its first summit, which brought together more than 150 nonprofit and corporate leaders,

researchers, practitioners, parents, and youths to identify challenges in bullying prevention. Discussions at the summit revealed inconsistencies in the definition of bullying behavior and the need to create a uniform definition of bullying. Subsequently, a review of the 2011 CDC publication of assessment tools used to measure bullying among youth ( Hamburger et al., 2011 ) revealed inconsistent definitions of bullying and diverse measurement strategies. Those inconsistencies and diverse measurements make it difficult to compare the prevalence of bullying across studies ( Vivolo et al., 2011 ) and complicate the task of distinguishing bullying from other types of aggression between youths. A uniform definition can support the consistent tracking of bullying behavior over time, facilitate the comparison of bullying prevalence rates and associated risk and protective factors across different data collection systems, and enable the collection of comparable information on the performance of bullying intervention and prevention programs across contexts ( Gladden et al., 2014 ). The CDC and U.S. Department of Education collaborated on the creation of the following uniform definition of bullying (quoted in Gladden et al., 2014, p. 7 ):

Bullying is any unwanted aggressive behavior(s) by another youth or group of youths who are not siblings or current dating partners that involves an observed or perceived power imbalance and is repeated multiple times or is highly likely to be repeated. Bullying may inflict harm or distress on the targeted youth including physical, psychological, social, or educational harm.

This report noted that the definition includes school-age individuals ages 5-18 and explicitly excludes sibling violence and violence that occurs in the context of a dating or intimate relationship ( Gladden et al., 2014 ). This definition also highlighted that there are direct and indirect modes of bullying, as well as different types of bullying. Direct bullying involves “aggressive behavior(s) that occur in the presence of the targeted youth”; indirect bullying includes “aggressive behavior(s) that are not directly communicated to the targeted youth” ( Gladden et al., 2014, p. 7 ). The direct forms of violence (e.g., sibling violence, teen dating violence, intimate partner violence) can include aggression that is physical, sexual, or psychological, but the context and uniquely dynamic nature of the relationship between the target and the perpetrator in which these acts occur is different from that of peer bullying. Examples of direct bullying include pushing, hitting, verbal taunting, or direct written communication. A common form of indirect bullying is spreading rumors. Four different types of bullying are commonly identified—physical, verbal, relational, and damage to property. Some observational studies have shown that the different forms of bullying that youths commonly experience may overlap ( Bradshaw et al., 2015 ;

Godleski et al., 2015 ). The four types of bullying are defined as follows ( Gladden et al., 2014 ):

• Physical bullying involves the use of physical force (e.g., shoving, hitting, spitting, pushing, and tripping).
• Verbal bullying involves oral or written communication that causes harm (e.g., taunting, name calling, offensive notes or hand gestures, verbal threats).
• Relational bullying is behavior “designed to harm the reputation and relationships of the targeted youth (e.g., social isolation, rumor spreading, posting derogatory comments or pictures online).”
• Damage to property is “theft, alteration, or damaging of the target youth’s property by the perpetrator to cause harm.”

In recent years, a new form of aggression or bullying has emerged, labeled “cyberbullying,” in which the aggression occurs through modern technological devices, specifically mobile phones or the Internet ( Slonje and Smith, 2008 ). Cyberbullying may take the form of mean or nasty messages or comments, rumor spreading through posts or creation of groups, and exclusion by groups of peers online.

While the CDC definition identifies bullying that occurs using technology as electronic bullying and views that as a context or location where bullying occurs, one of the major challenges in the field is how to conceptualize and define cyberbullying ( Tokunaga, 2010 ). The extent to which the CDC definition can be applied to cyberbullying is unclear, particularly with respect to several key concepts within the CDC definition. First, whether determination of an interaction as “wanted” or “unwanted” or whether communication was intended to be harmful can be challenging to assess in the absence of important in-person socioemotional cues (e.g., vocal tone, facial expressions). Second, assessing “repetition” is challenging in that a single harmful act on the Internet has the potential to be shared or viewed multiple times ( Sticca and Perren, 2013 ). Third, cyberbullying can involve a less powerful peer using technological tools to bully a peer who is perceived to have more power. In this manner, technology may provide the tools that create a power imbalance, in contrast to traditional bullying, which typically involves an existing power imbalance.

A study that used focus groups with college students to discuss whether the CDC definition applied to cyberbullying found that students were wary of applying the definition due to their perception that cyberbullying often involves less emphasis on aggression, intention, and repetition than other forms of bullying ( Kota et al., 2014 ). Many researchers have responded to this lack of conceptual and definitional clarity by creating their own measures to assess cyberbullying. It is noteworthy that very few of these

definitions and measures include the components of traditional bullying—i.e., repetition, power imbalance, and intent ( Berne et al., 2013 ). A more recent study argues that the term “cyberbullying” should be reserved for incidents that involve key aspects of bullying such as repetition and differential power ( Ybarra et al., 2014 ).

Although the formulation of a uniform definition of bullying appears to be a step in the right direction for the field of bullying prevention, there are some limitations of the CDC definition. For example, some researchers find the focus on school-age youth as well as the repeated nature of bullying to be rather limiting; similarly the exclusion of bullying in the context of sibling relationships or dating relationships may preclude full appreciation of the range of aggressive behaviors that may co-occur with or constitute bullying behavior. As noted above, other researchers have raised concerns about whether cyberbullying should be considered a particular form or mode under the broader heading of bullying as suggested in the CDC definition, or whether a separate defintion is needed. Furthermore, the measurement of bullying prevalence using such a definiton of bullying is rather complex and does not lend itself well to large-scale survey research. The CDC definition was intended to inform public health surveillance efforts, rather than to serve as a definition for policy. However, increased alignment between bullying definitions used by policy makers and researchers would greatly advance the field. Much of the extant research on bullying has not applied a consistent definition or one that aligns with the CDC definition. As a result of these and other challenges to the CDC definition, thus far there has been inconsistent adoption of this particular definition by researchers, practitioners, or policy makers; however, as the definition was created in 2014, less than 2 years is not a sufficient amount of time to assess whether it has been successfully adopted or will be in the future.

THE COMMITTEE’S APPROACH

This report builds on the April 2014 workshop, summarized in Building Capacity to Reduce Bullying: Workshop Summary ( Institute of Medicine and National Research Council, 2014c ). The committee’s work was accomplished over an 18-month period that began in October 2014, after the workshop was held and the formal summary of it had been released. The study committee members represented expertise in communication technology, criminology, developmental and clinical psychology, education, mental health, neurobiological development, pediatrics, public health, school administration, school district policy, and state law and policy. (See Appendix E for biographical sketches of the committee members and staff.) The committee met three times in person and conducted other meetings by teleconferences and electronic communication.

Information Gathering

The committee conducted an extensive review of the literature pertaining to peer victimization and bullying. In some instances, the committee drew upon the broader literature on aggression and violence. The review began with an English-language literature search of online databases, including ERIC, Google Scholar, Lexis Law Reviews Database, Medline, PubMed, Scopus, PsycInfo, and Web of Science, and was expanded as literature and resources from other countries were identified by committee members and project staff as relevant. The committee drew upon the early childhood literature since there is substantial evidence indicating that bullying involvement happens as early as preschool (see Vlachou et al., 2011 ). The committee also drew on the literature on late adolescence and looked at related areas of research such as maltreatment for insights into this emerging field.

The committee used a variety of sources to supplement its review of the literature. The committee held two public information-gathering sessions, one with the study sponsors and the second with experts on the neurobiology of bullying; bullying as a group phenomenon and the role of bystanders; the role of media in bullying prevention; and the intersection of social science, the law, and bullying and peer victimization. See Appendix A for the agendas for these two sessions. To explore different facets of bullying and give perspectives from the field, a subgroup of the committee and study staff also conducted a site visit to a northeastern city, where they convened four stakeholder groups comprised, respectively, of local practitioners, school personnel, private foundation representatives, and young adults. The site visit provided the committee with an opportunity for place-based learning about bullying prevention programs and best practices. Each focus group was transcribed and summarized thematically in accordance with this report’s chapter considerations. Themes related to the chapters are displayed throughout the report in boxes titled “Perspectives from the Field”; these boxes reflect responses synthesized from all four focus groups. See Appendix B for the site visit’s agenda and for summaries of the focus groups.

The committee also benefited from earlier reports by the National Academies of Sciences, Engineering, and Medicine through its Division of Behavioral and Social Sciences and Education and the Institute of Medicine, most notably:

• Reducing Risks for Mental Disorders: Frontiers for Preventive Intervention Research ( Institute of Medicine, 1994 )
• Community Programs to Promote Youth Development ( National Research Council and Institute of Medicine, 2002 )
• Deadly Lessons: Understanding Lethal School Violence ( National Research Council and Institute of Medicine, 2003 )
• Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities ( National Research Council and Institute of Medicine, 2009 )
• The Science of Adolescent Risk-Taking: Workshop Report ( Institute of Medicine and National Research Council, 2011 )
• Communications and Technology for Violence Prevention: Workshop Summary ( Institute of Medicine and National Research Council, 2012 )
• Building Capacity to Reduce Bullying: Workshop Summary ( Institute of Medicine and National Research Council, 2014c )
• The Evidence for Violence Prevention across the Lifespan and Around the World: Workshop Summary ( Institute of Medicine and National Research Council, 2014a )
• Strategies for Scaling Effective Family-Focused Preventive Interventions to Promote Children’s Cognitive, Affective, and Behavioral Health: Workshop Summary ( Institute of Medicine and National Research Council, 2014b )
• Investing in the Health and Well-Being of Young Adults ( Institute of Medicine and National Research Council, 2015 )

Although these past reports and workshop summaries address various forms of violence and victimization, this report is the first consensus study by the National Academies of Sciences, Engineering, and Medicine on the state of the science on the biological and psychosocial consequences of bullying and the risk and protective factors that either increase or decrease bullying behavior and its consequences.

Terminology

Given the variable use of the terms “bullying” and “peer victimization” in both the research-based and practice-based literature, the committee chose to use the current CDC definition quoted above ( Gladden et al., 2014, p. 7 ). While the committee determined that this was the best definition to use, it acknowledges that this definition is not necessarily the most user-friendly definition for students and has the potential to cause problems for students reporting bullying. Not only does this definition provide detail on the common elements of bullying behavior but it also was developed with input from a panel of researchers and practitioners. The committee also followed the CDC in focusing primarily on individuals between the ages of 5 and 18. The committee recognizes that children’s development occurs on a continuum, and so while it relied primarily on the CDC defini-

tion, its work and this report acknowledge the importance of addressing bullying in both early childhood and emerging adulthood. For purposes of this report, the committee used the terms “early childhood” to refer to ages 1-4, “middle childhood” for ages 5 to 10, “early adolescence” for ages 11-14, “middle adolescence” for ages 15-17, and “late adolescence” for ages 18-21. This terminology and the associated age ranges are consistent with the Bright Futures and American Academy of Pediatrics definition of the stages of development. 4

A given instance of bullying behavior involves at least two unequal roles: one or more individuals who perpetrate the behavior (the perpetrator in this instance) and at least one individual who is bullied (the target in this instance). To avoid labeling and potentially further stigmatizing individuals with the terms “bully” and “victim,” which are sometimes viewed as traits of persons rather than role descriptions in a particular instance of behavior, the committee decided to use “individual who is bullied” to refer to the target of a bullying instance or pattern and “individual who bullies” to refer to the perpetrator of a bullying instance or pattern. Thus, “individual who is bullied and bullies others” can refer to one who is either perpetrating a bullying behavior or a target of bullying behavior, depending on the incident. This terminology is consistent with the approach used by the FPBP (see above). Also, bullying is a dynamic social interaction ( Espelage and Swearer, 2003 ) where individuals can play different roles in bullying interactions based on both individual and contextual factors.

The committee used “cyberbullying” to refer to bullying that takes place using technology or digital electronic means. “Digital electronic forms of contact” comprise a broad category that may include e-mail, blogs, social networking Websites, online games, chat rooms, forums, instant messaging, Skype, text messaging, and mobile phone pictures. The committee uses the term “traditional bullying” to refer to bullying behavior that is not cyberbullying (to aid in comparisons), recognizing that the term has been used at times in slightly different senses in the literature.

Where accurate reporting of study findings requires use of the above terms but with senses different from those specified here, the committee has noted the sense in which the source used the term. Similarly, accurate reporting has at times required use of terms such as “victimization” or “victim” that the committee has chosen to avoid in its own statements.

4 For details on these stages of adolescence, see https://brightfutures.aap.org/Bright%20Futures%20Documents/3-Promoting_Child_Development.pdf [October 2015].

ORGANIZATION OF THE REPORT

This report is organized into seven chapters. After this introductory chapter, Chapter 2 provides a broad overview of the scope of the problem.

Chapter 3 focuses on the conceptual frameworks for the study and the developmental trajectory of the child who is bullied, the child who bullies, and the child who is bullied and also bullies. It explores processes that can explain heterogeneity in bullying outcomes by focusing on contextual processes that moderate the effect of individual characteristics on bullying behavior.

Chapter 4 discusses the cyclical nature of bullying and the consequences of bullying behavior. It summarizes what is known about the psychosocial, physical health, neurobiological, academic-performance, and population-level consequences of bullying.

Chapter 5 provides an overview of the landscape in bullying prevention programming. This chapter describes in detail the context for preventive interventions and the specific actions that various stakeholders can take to achieve a coordinated response to bullying behavior. The chapter uses the Institute of Medicine’s multi-tiered framework ( National Research Council and Institute of Medicine, 2009 ) to present the different levels of approaches to preventing bullying behavior.

Chapter 6 reviews what is known about federal, state, and local laws and policies and their impact on bullying.

After a critical review of the relevant research and practice-based literatures, Chapter 7 discusses the committee conclusions and recommendations and provides a path forward for bullying prevention.

The report includes a number of appendixes. Appendix A includes meeting agendas of the committee’s public information-gathering meetings. Appendix B includes the agenda and summaries of the site visit. Appendix C includes summaries of bullying prevalence data from the national surveys discussed in Chapter 2 . Appendix D provides a list of selected federal resources on bullying for parents and teachers. Appendix E provides biographical sketches of the committee members and project staff.

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Bullying has long been tolerated as a rite of passage among children and adolescents. There is an implication that individuals who are bullied must have "asked for" this type of treatment, or deserved it. Sometimes, even the child who is bullied begins to internalize this idea. For many years, there has been a general acceptance and collective shrug when it comes to a child or adolescent with greater social capital or power pushing around a child perceived as subordinate. But bullying is not developmentally appropriate; it should not be considered a normal part of the typical social grouping that occurs throughout a child's life.

Although bullying behavior endures through generations, the milieu is changing. Historically, bulling has occurred at school, the physical setting in which most of childhood is centered and the primary source for peer group formation. In recent years, however, the physical setting is not the only place bullying is occurring. Technology allows for an entirely new type of digital electronic aggression, cyberbullying, which takes place through chat rooms, instant messaging, social media, and other forms of digital electronic communication.

Composition of peer groups, shifting demographics, changing societal norms, and modern technology are contextual factors that must be considered to understand and effectively react to bullying in the United States. Youth are embedded in multiple contexts and each of these contexts interacts with individual characteristics of youth in ways that either exacerbate or attenuate the association between these individual characteristics and bullying perpetration or victimization. Recognizing that bullying behavior is a major public health problem that demands the concerted and coordinated time and attention of parents, educators and school administrators, health care providers, policy makers, families, and others concerned with the care of children, this report evaluates the state of the science on biological and psychosocial consequences of peer victimization and the risk and protective factors that either increase or decrease peer victimization behavior and consequences.

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Committee on the Biological and Psychosocial Effects of Peer Victimization: Lessons for Bullying Prevention; Board on Children, Youth, and Families; Committee on Law and Justice; Division of Behavioral and Social Sciences and Education; Health and Medicine Division; National Academies of Sciences, Engineering, and Medicine; Rivara F, Le Menestrel S, editors. Preventing Bullying Through Science, Policy, and Practice. Washington (DC): National Academies Press (US); 2016 Sep 14.

Preventing Bullying Through Science, Policy, and Practice.

• Hardcopy Version at National Academies Press

4 Consequences of Bullying Behavior

Bullying behavior is a serious problem among school-age children and adolescents; it has short- and long-term effects on the individual who is bullied, the individual who bullies, the individual who is bullied and bullies others, and the bystander present during the bullying event. In this chapter, the committee presents the consequences of bullying behavior for children and youth. As referenced in Chapter 1 , bullying can be either direct or indirect, and children and youth may experience different types of bullying. Specifically the committee examines physical (including neurobiological), mental, and behavioral health consequences. The committee also examines consequences for academic performance and achievement and explores evidence for some of the mechanisms proposed for the psychological effects of bullying. When applicable, we note the limited, correlational nature of much of the available research on the consequences of bullying.

• CONSEQUENCES FOR INDIVIDUALS WHO ARE BULLIED

Mounting evidence on bullying has highlighted the detrimental effects of being bullied on children's health and behavior ( Gini and Pozzoli, 2009 ; Lereya et al., 2015 ; Reijntjes et al., 2010 ; Ttofi et al., 2011 ). In this section, the committee reviews the research on physical, psychosocial, and academic achievement consequences for those children and youth who are bullied.

Perspectives from the Field

Being bullied makes young people incredibly insecure: When you're being bullied, you can feel constantly insecure and on guard. Even if you're not actively being bullied, you're aware it could start anytime. It has a big mental and emotional impact—you feel unaccepted, isolated, angry, and withdrawn. You're always wondering how you can do better and how you can escape a bully's notice. You're also stunted because of the constant tension and because maybe you forego making certain friendships or miss out on taking certain chances that could actually help your development.

—Summary of themes from young adults focus group (See Appendix B for additional highlights from interviews.)

Physical Health Consequences

The physical health consequences of bullying can be immediate, such as physical injury, or they can involve long-term effects, such as headaches, sleep disturbances, or somatization. 1 However, the long-term physical consequences of bullying can be difficult to identify and link with past bullying behavior versus being the result of other causes such as anxiety or other adverse childhood events that can also have physical effects into adulthood ( Hager and Leadbeater, 2016 ). In one of the few longitudinal studies on the physical and mental effects of bullying, Bogart and colleagues (2014) studied 4,297 children and their parents from three urban locales: Birmingham, Alabama; 25 contiguous school districts in Los Angeles County, California; and one of the largest school districts in Houston, Texas. Bogart and her team were interested in the cumulative effects of bullying on an individual. They collected data when the cohort was in fifth grade (2004 to 2006), seventh grade (2006 to 2008), and tenth grade (2008 to 2010). Data consisted of responses to the Peer Experience Questionnaire, the Pediatric Quality of Life Inventory with its Psychosocial Subscale and Physical Health Subscale, and a Self-Perception Profile. The Physical Health Subscale measured perceptions of physical quality of life.

Bogart and colleagues (2014) found that children who were bullied experienced negative physical health compared to non-involved peers. Among seventh grade students with the worst-decile physical health, 6.4 percent were not bullied, 14.8 percent had been bullied in the past only, 23.9 percent had been bullied in the present only, and nearly a third (30.2%) had been bullied in both the past and present. These effects were not as strong when students were in tenth grade. Limitations to this study were that physical health was measured by participants' perceptions of their health-related quality of life, rather than by objectively defined physical symptoms. It is critical to understand that this study, or other studies assessing correlations between behavior and events, cannot state that the events caused the behavior. Future research might build on this large multisite longitudinal study and obtain more in-depth evidence on individuals' physical health as a consequence of bullying.

In their study of 2,232 twins reared together and separately as a part of the Environmental Risk (E-Risk) Longitudinal Twin Study, Baldwin and colleagues (2015) found that children who had experienced chronic bullying showed greater adiposity subsequently, but not at the time of victimization. The study revealed that at age 18, these children had a higher body mass index ( b = 1.11, CI [0.33, 1.88]), waist-hip ratio ( b = 0.017, CI [0.008, 0.026]), and were at a higher risk of being overweight ( OR = 1.80, CI [1.28, 2.53]) than their nonbullied counterparts ( Baldwin et al., 2015 ).

An important future direction for research is to gather more information on physical consequences such as elevated blood pressure, inflammatory markers, and obesity in light of work showing effects on these outcome of harsh language by parents and other types of early life adversity ( Danese and Tan, 2014 ; Danese et al., 2007 ; Evans et al., 2007 ; Miller and Chen, 2010 ).

Somatic Symptoms

Most of the extant evidence on the physical consequences—somatic symptoms in particular—of bullying pertains to the individual who is bullied. The emotional effects of being bullied can be expressed through somatic disturbances, which, similar to somatization, are physical symptoms that originate from stress or an emotional condition. Common stress or anxiety-related symptoms include sleep disorders, gastrointestinal concerns, headaches, palpitations, and chronic pain. The relationship between peer victimization and sleep disturbances has been well documented ( Hunter et al., 2014 ; van Geel et al., 2014 ).

For instance, Hunter and colleagues (2014) examined sleep difficulties (feeling too tired to do things, had trouble getting to sleep, and had trouble staying asleep) among a sample of 5,420 Scottish adolescents. The researchers found that youth who were bullied ( OR = 1.72, 95% CI [1.07, 2.75]) and youth who bully ( OR = 1.80, CI [1.16, 2.81]) were nearly twice as likely as youth who were not involved in bullying to experience sleep difficulties. One limitation of this study is that it was based on self-reports, which have sometimes been criticized as being subject to specific biases. Patients with insomnia may overestimate how long it takes them to fall asleep ( Harvey and Tang, 2012 ). Another limitation is that the study included young people at different stages of adolescence. Sleep patterns and sleep requirements vary across the different stages of adolescence.

A recent meta-analysis based on 21 studies involving an international sample of 363,539 children and adolescents examined the association between peer victimization and sleeping problems. A broader focus on peer victimization was used because of the definitional issues related to bullying. The authors defined peer victimization as “being the victim of relational, verbal or physical aggression by peers” ( van Geel et al., 2015 , p. 89). Children and youth who were victimized reported more sleeping problems than children who did not report victimization ( OR = 2.21, 95% CI [2.01, 2.44]). Moreover, the relationship between peer victimization and sleeping problems was stronger for younger children than it was for older children ( van Geel et al., 2015 ). This study was based on cross-sectional studies that varied widely in how peer victimization and sleeping problems were operationalized and thus cannot make any claims about causal relations between peer victimization and sleeping problems.

Knack and colleagues (2011a) posited that bullying results in meaningful biological alterations that may result in changes in one's sensitivity to pain responses. A recent meta-analysis by Gini and Pozzoli (2013) concluded that children and adolescents who are bullied were at least twice as likely to have psychosomatic disturbances (headache, stomachache, dizziness, bedwetting, etc.) than nonbullied children and adolescents ( OR = 2.39, 95% CI [1.76, 3.24] for longitudinal studies; OR = 2.17, 95% CI [1.91, 2.46] for cross-sectional studies). Although the use of self-report measures are very common in bullying research and are usually considered to be valid and reliable (Ladd and Kochenderfer- Ladd, 2002 ), their use requires adequate self-awareness on the part of the respondent, and some children who are bullied may be in denial about their experience of having been bullied.

There is also evidence of gender differences in the physical effects of being bullied. For example, Kowalski and Limber (2013) examined the relation between experiences with cyberbullying or traditional bullying (i.e., bullying that does not involve digital electronic means of communication) and psychological and physical health, as well as academic performance, of 931 students in grades 6 through 12 living in rural Pennsylvania. Students were asked how often in the past 4 weeks they experienced 10 physical health symptoms, with scores across these 10 symptoms averaged to provide an overall health index (higher scores equal more health problems). Traditional bullying was defined as “aggressive acts that are meant to hurt another person, that happen repeatedly, and that involve an imbalance of power” ( Kowalski and Limber, 2013 , p. S15). The authors found that girls who were traditionally bullied reported more anxiety and overall health problems than boys who were bullied (females: M = 1.65, SD = 0.41; males: M = 1.42, SD = 0.38). A limitation of this study is that it is correlational in nature and the authors cannot conclude that being a victim of traditional bullying caused the psychological or physical problems.

In summary, it is clear that children and youth who have been bullied also experience a range of somatic disturbances. There are also gender differences in the physical health consequences of being bullied.

Neuroendocrinology of Stress

Psychological and physical stressors, such as being the target of bullying, activate the stress system centered on the hypothalamic-pituitary-adrenal (HPA) axis ( Dallman et al., 2003 ; McEwen and McEwen, 2015 ). The role of HPA and other hormones is to promote adaptation and survival, but chronically elevated hormones can also cause problems. Stress has ubiquitous effects on physiology and the brain, alters levels of many hormones and other biomarkers, and ultimately affects behavior. Therefore, both a general understanding of stress during early adolescence and, where known, specific links between stress and bullying can provide insight into the enduring effects of bullying.

The levels of the stress hormone cortisol have been shown to change in targets of repeated bullying, with being bullied associated with a blunted cortisol response ( Booth et al., 2008 ; Kliewer, 2006 ; Knack et al., 2011b ; Ouellet-Morin et al., 2011 ; Vaillancourt et al., 2008 ). To the committee's knowledge, no study has examined bidirectional changes in cortisol, although there is evidence to suggest that cortisol is typically elevated immediately following many types of stress and trauma but blunted after prolonged stress ( Judd et al., 2014 ; Miller et al., 2007 ). Kliewer (2006) did find that cortisol increased from pre-task to post-task (i.e., watching a video clip from the film Boyz 'n the Hood followed by a discussion) among youth who had been bullied, and in a more recent study, Kliewer and colleagues (2012) reported, among African American urban adolescents, that peer victimization was associated with greater sympathetic nervous system (fight or flight reaction) reactivity to a stress task (measured using salivary a-amylase, an enzyme that increases in saliva when the sympathetic nervous system is activated). However, in these studies, the immediate effect of being bullied on stress reactivity was not examined. In contrast, Ouellet-Morin and colleagues (2011) and Knack and colleagues (2011b) did not find an increase in cortisol in bullied youth following a psychosocial stress test but rather found a blunted pattern of response after the test had concluded (see Figures 4-1 and 4-2 ). In order to test whether, in the short-term, bullying produces an increase in cortisol, whereas in the long-term it is associated with a blunted cortisol response (as seen with other types of psychosocial stressors; Judd et al., 2014 ; Miller et al., 2007 ), a longitudinal study is needed to examine bullying chronicity and regulation of the HPA axis. The importance of this future work notwithstanding, there is evidence to support a finding that when stress becomes prolonged, the stress hormone system becomes hypofunctional and a blunted stress response results ( McEwen, 2014 ).

Cortisol reactivity for victimized and nonvictimized adolescents during the Trier Social Stress Test. SOURCE: Adapted from Knack et al. (2011b, Fig. 3, p. 5).

Cortisol responses to a psychosocial stress test (PST) in the total sample and according to maltreatment/bullying victimization. SOURCE: Adapted from Ovellet-Morin et al. (2011, Fig. 1, p. 14).

When stress becomes prolonged, the stress hormone system becomes hypofunctional and a blunted stress response results ( Knack et al., 2012a ; McEwen, 2014 ; Vaillancourt et al., 2013a ). That is, the elevation in cortisol in response to stress fails to occur. Scientists are not exactly sure how this happens, but evidence suggests that the stress system has shut itself down through “negative feedback.” Although on the surface this may seem to be beneficial, it is not. Cortisol has many functions and serves to regulate myriad biological systems; a blunted stress response compromises the orchestration of cortisol's biological functions. The critical importance of the massive over-activation of the stress system producing a blunted stress response is clinically relevant since it is associated with posttraumatic stress disorder and other psychiatric disorders ( Heim et al., 1997 ). It is also relevant for understanding an individual's inability to self-regulate and cope with stress.

Prolonged stress also disrupts the circadian or daily rhythm of cortisol, which is normally elevated in the morning and slowly decreases over the day to result in low levels at bedtime ( Barra et al., 2015 ). An altered circadian rhythm results not only in difficulty awaking in the morning but also in difficulty falling asleep at night. It can cause profound disruption in sleep patterns that can initiate myriad additional problems; sleep deficits are associated with problems with emotional regulation, learning, mood disorders, and a heightened social threat detection and response system ( McEwen and Karatsoreos, 2015 ). Recent research suggests that the consolidation of memories 2 one learns each day continues during sleep ( Barnes and Wilson, 2014 ; Shen et al., 1998 ). Sleep disturbances disrupt memory consolidation, and studies in animals suggest stress during learning engages unique neurochemical and molecular events that cause memory to be encoded by some unique mechanism ( Baratta et al., 2015 ; Belujon and Grace, 2015 ; McGaugh, 2015 ; Rau and Fanselow, 2009 ). Although victims of bullying have sleep problems ( Miller-Graff et al., 2015 ), causal relations between bullying, sleep disorders, learning/memory consolidation, and cortisol dysregulation have not been established. Indeed, these correlations between being a target of bullying and physiological problems may highlight important interactions between events and outcome, but it is also likely that unidentified variables might be the critical causal factors.

It is also noteworthy that the HPA axis showed heightened responsiveness during the peak ages of bullying ( Blakemore, 2012 ; Dahl and Gunnar, 2009 ; Romeo, 2010 ; Spear, 2010 ). For example, cortisol response characteristics in children are such that, when cortisol is activated, the hormonal response is protracted and takes almost twice as much time to leave the blood and brain compared to adults ( Romeo, 2010 , 2015 ). The circadian rhythm of cortisol also seems altered during early adolescence, most notably associated with morning cortisol levels, with levels increasing with age and pubertal development ( Barra et al., 2015 ). Animal models suggest that the extended cortisol response begins in pre-puberty and indicate that recovery from stressful events is more challenging during this age range ( Romeo, 2015 ).

Emotional regulation, including a person's ability to recover from a traumatic or stressful event, involves being able to regulate or normalize stress hormone levels. Before adolescence, children's ability to regulate their stress response can be greatly assisted by parents or other significant caregivers—a process referred to as “social buffering” ( Hostinar et al., 2014 ; Ouellet-Morin et al., 2011 , 2013 ). Specifically, it is well documented in the human and animal research literature that a sensitive caregiver or a strong support system can greatly dampen the stress system's response and actually reduce the amount of stress hormone released, as well as shorten the amount of time the stress hormones circulate within the body and brain. This results in dramatic decreases in stress-related behavior ( Gee et al., 2014 ; Hostinar et al., 2014 ). The social cues actually reduce stress by reducing the activation of the stress system, or HPA axis, at the level of the hypothalamus ( Hennessy et al., 2009 , 2015 ; Moriceau and Sullivan, 2006 ). The social stimuli that buffer children as they transition into adolescence appear to begin to have greater reliance on peers rather than on the caregiver ( Hostinar et al., 2015 ).

Other physiological effects of stress include the activation of the immune system by bullying-induced stress ( Copeland et al., 2014 ; McCormick and Mathews, 2007 ), and a cardiovascular blunting among individuals with a history of being bullied ( Newman, 2014 ). Other hormones and physiological mechanisms are also involved in the stress activation response. For example, cortisol is associated with an increase in testosterone, the male sex hormone associated with aggression in nonhuman animals and with dominance and social challenge in humans, particularly among boys and men ( Archer, 2004 ). In fact, in rodents the combined assessment of testosterone and cortisol provides more predictive value of behavioral variability ( McCormick and Mathews, 2007 ) compared to controls ( Márquez et al., 2013 ). In humans, there is increasing evidence supporting an interaction between testosterone and cortisol in the prediction of social aggression (see Montoya et al., 2012 ). In a study of 12-year-olds, Vaillancourt and colleagues (2009) found that testosterone levels were higher among bullied boys than nonbullied boys, but lower among bullied girls than nonbullied girls. The authors speculated that the androgen dynamics were possibly adrenocortical in origin, highlighting the need to examine testosterone and cortisol in consort. To date, researchers have only investigated cortisol response to being bullied ( Kliewer, 2006 ; Knack et al., 2011b ; Ouellet-Morin et al., 2011 ; Vaillancourt et al., 2008 ), and only one study has examined testosterone and peer victimization ( Vaillancourt et al., 2009 ). There are no studies examining these two important hormones together in relation to bullying perpetration or to being bullied.

Together, the research on both humans and animals suggests that stress is beneficial when it is experienced at low-to-moderate levels, whereas prolonged or repeated stress becomes toxic by engaging a unique neural and molecular cascade within the brain that is thought to initiate a different developmental pathway. Indeed, from animal models, brain architecture is altered by chronic stress, with amygdala activity being enhanced, hippocampal function impaired, and medial prefrontal cortex function being reduced, leading to increased anxiety and aggression and decreased capacity for self-regulation, as well as a more labile mood ( Chattarji et al., 2015 ; McEwen and Morrison, 2013 ; McEwen et al., 2015 ). This stress effect on the brain is particularly strong when experienced during adolescence, but it is even more pronounced if combined with early life adversity ( Gee et al., 2014 ; Hanson et al., 2015 ; Richter-Levin et al., 2015 ; Romeo, 2015 ; Sandi and Haller 2015 ). This could produce behavioral responses that become maladaptive by compromising emotional and cognitive functioning or perhaps it could produce adaptive behavior for a dangerous environment that results in socially inappropriate behavior.

Consequences of Bullying on Brain Function

Being a child or youth who is bullied changes behavior, and neuroscience research suggests this experience may also change the brain ( Bradshaw et al., 2012 ; Vaillancourt et al., 2013a ). The major technique used to monitor brain function in humans is functional magnetic resonance imaging (fMRI), which works by monitoring blood flow to indirectly assess the functioning of thousands of brain cells over an area of the brain. This technique has rarely been used on either the perpetrator or target of a bullying incident during this very particular social interaction, and for that reason little is known about whether or not the brain of a child who bullies or of a child who has been bullied is different before these experiences or is changed by them. These very specific studies are required before one can make definitive statements about the brain for this topic or for how this information might help develop novel interventions or prevention.

Additionally, it is important to consider two limitations for understanding fMRI. First, one cannot scan the brain of a child during the action of bullying or being a target of bullying. Instead, one must rely on the child staying perfectly still as the investigator tries to approximate one or two aspects of the complex experience that occur in this complicated behavioral interaction. For example, the fMRI task used during a brain imaging session might mimic social exclusion as one facet of bullying, without the full social and emotional context of the real bullying process. Although this is an important methodology, these results need to be assessed with caution at this time and not directly applied as an accepted scientific interpretation of bullying. Therefore, the examples used below to assess brain function rely not on monitoring actual instances of bullying behavior but on monitoring components of behaviors that are thought to occur during a bullying incident.

Second, fMRI monitors a large brain area, which is composed of many smaller brain areas, each of which is involved in many, many behaviors, many of which are not yet fully understood. Thus, it is difficult to determine why the brain area one is examining changed, since that brain area is involved in hundreds of diverse behaviors. For this reason, the results reviewed below need to be viewed as preliminary and should not be misinterpreted as explaining any aspect of the experience of bullying. Rather, these preliminary results highlight the importance of brain assessment before and after bullying experiences, including developing monitorable tasks that more closely approximate the bullying experience within the physical constraints of an immobile subject during an fMRI brain scan. The value of neuroscience is that it enables exploration of brain mechanisms controlling behavior that are not obvious from behavioral assessment.

Social Pain

Whereas there are no studies directly examining bullying using neural imaging techniques, there are several studies examining how the brain processes social pain. Social pain describes the “feelings of pain that follow the experiences of peer rejection, ostracism, or loss” ( Vaillancourt et al., 2013a , p. 242). Social pain is consistent with how people describe their feeling about being bullied. For example, one victim of bullying described the emotional toll of his experience by saying, “I feel like, emotionally, they [his bullies] have been beating me with a stick for 42 years” ( Vaillancourt et al., 2013a , p. 242).

Researchers have demonstrated that when people experience social pain, they activate regions in their brain similar to those activated when they experience physical pain ( Eisenberger, 2012 ; Eisenberger and Lieberman, 2004 ; Eisenberger et al., 2003 ; Kross et al., 2011 ; Vaillancourt et al., 2010a ). Specifically, the dorsal anterior cingulate cortex, which is part of the prefrontal cortex, seems to be implicated in the processing of both physical and social pain. The fact that physical and social pain have overlapping neural systems might explain why people tend to use physical pain metaphors (e.g., “It broke my heart when she called me ugly.”) when describing their experiences with being humiliated, oppressed, or rejected ( Eisenberger, 2012 ). Eisenberger and Leiberman (2004) noted that these fMRI results are correlations between pain and the anterior cingulate cortex and could reflect other functions of that brain region, such as detecting conflict or errors, different ideas or goals about the task, or individual differences in the task difficulty. In a recent fMRI study by Rudolph and colleagues (2016) , adolescent girls were socially excluded during a laboratory task (i.e., cyberball; Williams et al., 2000 ). Results indicated that activation of the social pain network—the dorsal anterior cingulate cortex, subgenual anterior cingulate cortex, and anterior insula—was associated with internalizing symptoms. Of note, this effect was particularly pronounced among adolescent girls with a history of peer victimization. 3

In addition to studies on social pain, there are some studies examining how the brains of children who had been bullied reacted subsequently to different stimuli. Experiences of being bullied can alter an individual's view of the world. While no brain imaging study has directly addressed this issue, a longitudinal study investigating the risk factors of depression found that being a child who was bullied at ages 11 and 12 was associated with a decreased response to reward in the medial prefrontal cortex at age 16, although it was unclear if these brain differences were present before the bullying experiences or developed after them ( Casement et al., 2014 ). The medial prefrontal cortex, which is a brain area involved in memory and learning, was found to be disrupted in children who have been bullied ( Vaillancourt et al., 2011 ). Because it also has countless other functions including decision making, risk taking, and conflict monitoring, disruption of this region compromises one's ability to interpret results with respect to bullying ( Euston et al., 2012 ; Vaillancourt et al., 2011 ).

In another fMRI study involving children, 10-12 years old, who were presented with a task that examined their response to negative feedback stimuli of emotional faces, greater and more extensive brain activation was found in the amygdala, orbitofrontal cortex, and ventrolateral prefrontal cortex of children who had been rejected by their peers, compared with children in a control group who had not been rejected by peers ( Lee et al., 2014 ), a condition that is highly correlated with being bullied by peers ( r = .57; Knack et al., 2012a ). The prefrontal cortex is a very large brain area with many subareas, all of which serve diverse functions in many different behaviors, not just executive function. Indeed, the prefrontal cortex processes pain, self-regulation, stress integration, and safety signals and has been implicated in psychiatric disorders, higher order learning, extinction (active process to suppress a memory), personality, social behavior, planning, decision making, and many other behaviors and percepts including social exclusion, social/physical pain, and empathy ( Casey and Jones, 2010 ; Spear, 2013 ). These few studies are consistent with other imaging studies demonstrating functional brain differences among individuals who were maltreated in childhood ( Lim et al., 2014 , 2015 ). Taken together, this work supports a finding that being exposed to such adversity during maturation has enduring effects on brain function, although additional research is needed to establish the parameters controlling these effects (and qualifying the generalization).

There is also evidence that stressful events, such as might occur with bullying experiences, impact emotional brain circuits, an inference that is supported by changes in amygdala architecture and function described earlier in animal models in adulthood but more robust changes in brain structure are produced by stress during early life and around adolescence ( Chattarji et al., 2015 ; McEwen and Morrison, 2013 ; McEwen et al., 2015 ). This point is critical because the stress system of adolescents seems to have a heightened sensitivity, and experiencing bullying can increase stress hormones ( Romeo, 2010 , 2015 ; Spear, 2013 ; Vaillancourt et al., 2011 ). Human brain scanning experiments suggest the prefrontal cortex is affected by stress through attenuating the connectivity to the hippocampus and amygdala, which are brain areas critical for emotional regulation and emotional memories ( Ganzel et al., 2008 ; Liston et al., 2009 ). Animal research shows that this connectivity loss is caused by stress-induced atrophy of the prefrontal cortex ( Radley et al., 2006 ), although this brain region does show the ability to recover once the stress has terminated ( Liston et al., 2009 ). One aspect of being a target of bullying is that the memory of the experience seems to be enduring; the unique function of the prefrontal cortex and emotional circuits during preadolescence and adolescence may provide insight into the enduring memories of being bullied. Specifically, one function of the prefrontal cortex is to help suppress memories that are no longer important or true. Typically, memories are not simply forgotten or unlearned. Rather, as we update information in our brain, the old memory is suppressed by overlaying a new memory to attenuate the old memory, an active brain process called extinction ( Milaid and Quirk, 2012 ). With respect to memories of trauma, of being bullied, or of experiencing a threat, the prefrontal cortex is important for attenuating (extinguishing) memories in emotional brain areas, such as the amygdala. Importantly, dramatic changes occur in the extinction system during adolescence, where fear extinction learning is attenuated relative to children and adults ( Pattwell et al., 2012 , 2013 ). This learning mode has been modeled in animals to understand how the process occurs in the adolescent brain ( Kim and Richardson, 2010 ; Nair and Gonzalez-Lima, 1999 ; Pattwell et al., 2012 ). The research suggests that around the time of adolescence, it is more difficult to decrease emotionally aversive memories, such as experiences of being bullied, than at other times in the life cycle. Furthermore, anxious teens (anxiety is sometimes comorbid with experience of being bullied) show even greater difficulties with processing extinction of fear memory ( Jovanovic et al., 2013 ).

In conclusion, the available evidence indicates that the brain functioning of individuals who are bullied is altered (see reviews by Bradshaw et al., 2012 ; Vaillancourt et al., 2013a ). However, it is difficult to ascertain fully what it means when fMRI scans detect an alteration in brain activity. In terms of understanding the prolonged and repeated stress associated with bullying, this research suggests that greater experience with being bullied and repeated exposure as a target of bullying produces a neural signature in the brain that could underlie some of the behavioral outcomes associated with being bullied.

Psychosocial Consequences

In this section, the committee examines what is known about the psychosocial consequences of being bullied. A common method of examining mental health issues separates internalizing and externalizing problems ( Sigurdson et al., 2015 ). Internalizing symptoms include problems directed within the individual, such as depression, anxiety, fear, and withdrawal from social contacts. Externalizing symptoms reflect behavior that is typically directed outwards toward others, such as anger, aggression, and conduct problems, including a tendency to engage in risky and impulsive behavior, as well as criminal behavior. Externalizing problems also include the use and abuse of substances.

Psychological problems are common after being bullied (see review by Hawker and Boulton, 2000 ) and include internalizing problems, such as depression, anxiety, and, especially for girls, self-harming behavior ( Kidger et al., 2015 ; Klomek et al., 2009 , 2015 ). There can also be subsequent externalizing problems, especially for boys (see review by McDougall and Vaillancourt, 2015 ). Rueger and colleagues (2011) found consistent concurrent association with timing of peer victimization and maladjustment. Both psychological and academic outcomes were particularly strong for students who experienced sustained victimization over the school year.

“And these are the kids that are at risk for anxiety and depression and bipolar disorder to begin with, and it almost seems like it's a cycle that makes it worse. So they are isolated and they are angry, they are fearful. Many of them end up severely depressed, attempting suicide, utilizing NSSIs [nonsuicidal self-injuries] for comfort. Some turn to gangs because that is the group that would accept them. So that's when we get involved and we have to start working backwards.”

—Quote from community-based provider discussing bullying during focus group (See Appendix B for additional highlights from interviews.)

Internalizing Problems

A robust literature documents that youth who are bullied often have low self-esteem and feel depressed, anxious, and lonely ( Juvonen and Graham, 2014 ). Data from developmental psychopathology research indicate that stressful life events can lead to the onset and maintenance of depression, anxiety, and other psychiatric symptoms and that for many youth, being bullied is a major life stressor ( Swearer and Hymel, 2015 ). Based on sociometric nominations, targets of bullying also are disliked by the general peer group ( Knack et al., 2012b ).

Several meta-analyses have specifically explored the relation between depression and being bullied at school ( Ttofi et al., 2011 ) and victimized by peers 4 ( Hawker and Boulton, 2000 ; Reijntjes et al., 2010 ). Individuals who had been cyberbullied reported higher levels of depression and suicidal ideation, as well as increased emotional distress, externalized hostility, and delinquency, compared with peers who were not bullied ( Patchin, 2006 ; Ybarra et al., 2006 ). Furthermore, severity of depression in youth who have been cyberbullied has been shown to correlate with the degree and severity of cyberbullying ( Didden et al., 2009 ).

Two meta-analyses found that across several different longitudinal studies using different study populations, internalizing emotional problems increases both the risk and the harmful consequences of being bullied ( Cook et al., 2010 ; Reijntjes et al., 2010 ). Internalizing problems can thus function as both antecedents and consequences of bullying ( Reijntjes et al., 2010 ; Vaillancourt et al., 2013b ). Although most longitudinal studies suggest that psychological problems result from being bullied (see review by McDougall and Vaillancourt, 2015 ) and meta-analyses ( Reijntjes et al., 2010 ; Ttofi et al., 2011 ) support this directionality, there is some evidence that for some youth, the temporal pattern begins with mental health problems ( Kochel et al., 2012 ; Vaillancourt et al., 2013b ).

In a large cohort of Canadian children followed every year from grade 5 to grade 8, Vaillancourt and colleagues (2013b) found that internalizing problems in grades 5 and 7 predicted increased self-reported bullying behavior the following year. They noted that these findings provide evidence for the “symptom-driven pathway” across time with increased internalizing problems predicting greater self-reported peer victimization. This “symptom-drive pathway” was noted from grade 5 to grade 6 and again from grade 7 to grade 8 and was consistent with other published work. For instance, Kochel et al. (2012) reported a symptom-driven pathway in which depressive symptoms predicted peer victimization 5 1 year later (grade 4 to grade 5 and grade 5 to grade 6) and argued that this pathway may result from depressed youth displaying “social deficits,” selecting “maladaptive relationships,” and/or displaying a behavioral style that is perceived poorly by the peer group ( Kochel et al., 2012 , p. 638). Vaillancourt and colleagues (2013b) have also argued that depressed youth could be more “treat sensitive.” That is, these youth may select information from their environment that is consistent with their negative self-opinion. The idea that certain individuals may be more sensitive to environmental cues or make more hostile interpretation of ambiguous social data has been well documented in the literature ( Crick and Dodge, 1994 ; Dodge, 1986 ). This work is consistent with studies showing that social information processing differs in children based on their experience with being bullied and that hypersensitivity can impact their interpretation of social behavior and their self-reports of subsequent incidents of being bullied ( Camodeca et al., 2003 ; Smalley and Banerjee, 2013 ).

Most longitudinal studies to date are of relatively short duration (i.e., less than 2 years) and focus on a narrow developmental period such as childhood or adolescence ( McDougall and Vaillancourt, 2015 ). Nevertheless, there are several recently published studies examining the long-term adult outcomes of childhood bullying. These studies indicate that being bullied does affect future mental health functioning, as reviewed in the following paragraphs.

Most long-term studies of childhood bullying have focused on links to internalizing problems in adulthood, demonstrating robust long-standing effects ( Gibb et al., 2011 ; Olweus, 1993b ; Sourander et al., 2007 ; Stapinski et al., 2014 ). For example, Bowes and colleagues (2015) examined depression in a large sample of participants who reported being the target of bullying at age 13 and found higher rates of depression at age 18 compared to peers who had not been bullied. Specifically, they reported that 14.8 percent of participants who reported being frequently bullied in childhood at age 13 were clinically depressed at age 18 ( OR = 2.96, 95% CI [2.21, 3.97]) and that the population attributable fraction was 29.2 percent, suggesting that close to a third of the variance in depression could be explained by being bullied in childhood ( Bowes et al., 2015 ).

In another longitudinal study using two large population-based cohorts from the United Kingdom (the ALSPAC Cohort) and the United States (the GSMS Cohort), Lereya and colleagues (2015) reported that the effects of childhood bullying on adult mental health were stronger in magnitude than the effects of being maltreated by a caregiver in childhood. Being bullied only (and not maltreated) placed individuals at higher risk for mental health difficulties than being maltreated only (and not bullied) ( OR = 1.6, 95% CI [1.1, 2.2] for ALSPAC cohort; OR = 3.8, 95% CI [1.8, 7.9] for GSMS cohort). Children who were bullied were more likely than maltreated children to be anxious ( OR = 4.9, 95% CI [2.0, 12.0] for GSMS cohort), depressed ( OR = 1.7, 95% CI [1.1, 2.7] for ALSPAC cohort), and to engage in self-harming behavior ( OR = 1.7, 95% CI [1.1-2.6] for ALSPAC cohort) in adulthood ( Lereya et al., 2015 ).

Similarly, Stapinski and colleagues (2014) found that adolescents who experienced frequent peer victimization 6 were two to three times more likely to develop an anxiety disorder 5 years later at age 18 than nonvictimized adolescents ( OR = 2.49, 95% CI [1.62, 3.85]). The association remained after adjusting for potentially confounding individual and family factors and was not attributable to diagnostic overlap with depression. Frequently victimized adolescents were also more likely to develop multiple internalizing problems in adulthood ( Stapinski et al., 2014 ). After controlling for childhood psychiatric problems or family hardship, Copeland and colleagues (2013) found that individuals who were bullied continued to have higher prevalence of generalized anxiety ( OR = 2.7, 95% CI [1.1, 6.3]).

These findings suggest that being bullied and internalizing problems such as depression are mutually reinforcing, with the experience of one increasing the risk of the other in a harmful cycle that contributes to the high stability of being both bullied and experiencing other internalizing problems. These studies also suggest that the long-term consequences of being bullied, which extend into adulthood, can be more severe than being maltreated as a child by a caregiver.

Externalizing Problems

Alcohol and drug abuse and dependence have been associated with being bullied as a child ( Radliff et al., 2012 ). A longitudinal study of adolescents found that those who reported being bullied were more likely to report use of alcohol, cigarettes, and inhalants 12 months later ( Tharp-Taylor et al., 2009 ), compared to those who did not report being bullied. More longitudinal research that tracks children through adulthood is needed to fully understand the link between being bullied and substance abuse (see review by McDougall and Vaillancourt, 2015 ).

Several studies show links between being bullied and violence or crime, especially for men ( Gibb et al., 2011 ; McGee et al., 2011 ; Sourander et al., 2007 , 2011 ). A meta-analysis by Reijntjes and colleagues (2011) that included studies with data on 5,825 participants showed that after controlling for externalizing symptoms at baseline, peer victimization—under which they included being the target of teasing, deliberate exclusion, and being the target of physical threats and malicious gossip—was associated over time with exhibiting externalizing problems such as aggression, truancy, and delinquency ( r = .14, 95% CI [.09, .19]). This research team also found that externalizing problems predicted changes in peer victimization over time ( r = .13, 95% CI [.04, .21]) and concluded that there is a bidirectional relationship between peer victimization and externalizing problems.

Psychotic Symptoms

Evidence from the broader research on childhood trauma and stress indicates that earlier adverse life experiences, such as child abuse, are associated with the development of psychotic symptoms later in life ( Institute of Medicine and National Research Council, 2014b ). Until recently, the association between bullying and psychotic symptoms has been understudied ( van Dam et al., 2012 ). Two recent meta-analyses support the association between bullying and the development of psychotic symptoms later in life ( Cunningham et al., 2015 ; van Dam et al., 2012 ). van Dam and colleagues (2012) conducted a meta-analysis of 14 studies to assess whether being bullied in childhood is related to the development of psychotic (either clinical or nonclinical) symptoms. (Nonclinical psychotic symptoms 7 place individuals at risk for the development of psychotic disorders ( Cougnard et al., 2007 ).) Results from the analyses of studies that examined the association between bullying and nonclinical symptoms (six studies) were more definitive (adjusted OR = 2.3; 95% CI [1.5, 3.4]), with stronger associations when there was an increased frequency, severity, and persistence of bullying ( Cougnard et al., 2007 ). Although some research has found this association, a recent longitudinal study from New Zealand found that the link between bullying and the development of psychosis later in life is likely not causal but instead reflects the fact that individuals who display disordered behaviors across childhood and adolescences are more likely to become bullying targets ( Boden et al., 2016 ) An analysis of studies that examined the association between bullying and psychosis in clinical samples was inconclusive ( van Dam et al., 2012 ).

A recent meta-analysis conducted by Cunningham and colleagues (2015) examined ten European prospective studies, four from the Avon Longitudinal Study of Parents and Children. This analysis found that individuals who were bullied were more than twice as likely to develop later psychotic symptoms, compared to those who were not bullied ( OR = 2.1, 95% CI [1.1, 4.0]). These results were consistent in all but one of the studies included in the meta-analysis. More longitudinal research is needed to more fully understand the mechanisms through which trauma such as bullying may lead to the development of psychotic symptoms ( Cunningham et al., 2015 ; van Dam et al., 2012 ). Importantly, this research will need to be prospective and examine the development of bullying and psychotic symptoms in order to truly identify the temporal priority. The inclusion criteria for the Cunningham and colleagues (2015) meta-analysis included that the study had to be prospective and had to include a measure of psychosis and that bullying needed to be reported before the age of 18. Although the authors stated that “bullying appears to cause later development of psychosis,” such a conclusion requires that mental health functioning be assessed early and over time, as it is possible that premorbid characteristics may make individuals targets for poor peer treatment (see Kochel et al., 2012 ; Vaillancourt et al., 2013b , regarding depression leading to peer victimization).

Academic Performance Consequences

A growing literature has documented that targets of bullying suffer diminished academic achievement whether measured by grades or standardized test scores ( Espelage et al., 2013 ; Nakamoto and Schwartz, 2010 ). Cross-sectional research indicates that children who are bullied are at increased risk for poor academic achievement ( Beran, 2009 ; Beran and Lupart, 2009 ; Beran et al., 2008 ; Glew et al., 2005 ; Neary and Joseph, 1994 ; see also meta-analysis by Nakamoto and Schwartz, 2010 ) and increased absenteeism ( Juvonen et al., 2000 ; Kochenderfer and Ladd, 1996 ; Vaillancourt et al., 2013b ).

The negative relation between being bullied and academic achievement is evident as early as kindergarten ( Kochenderfer and Ladd, 1996 ) and continues into high school ( Espinoza et al., 2013 ; Glew et al., 2008 ). In a 2-week daily diary study with ninth and tenth grade Latino students, Espinoza and colleagues (2013) reported that on days when adolescents' reports of being bullied were greater than what was typical for them, they also reported more academic challenges such as doing poorly on a quiz, test, or homework and felt like less of a good student. Thus, even episodic encounters of being bullied can interfere with a student's ability to concentrate on any given day. In a cross-sectional study of more than 5,000 students in grades 7, 9, and 11, Glew and colleagues (2008) found that for every 1-point increase in grade point average (GPA), the odds of being a child who was bullied (versus a bystander) decreased by 10 percent. However, due to the cross-sectional nature of this study, this association does not establish whether lower academic achievement among children who were bullied was a consequence of having been bullied.

Several short-term (one academic year) longitudinal studies indicate that being bullied predicts academic problems rather than academic problems predicting being a target of bullying ( Kochenderfer and Ladd, 1996 ; Schwartz et al., 2005 ). Given the impairments in brain architecture associated with self-regulation and memory in animal models and the currently limited imaging data in human subjects, this is a reasonable inference, although reverse causation is possible. For instance, early life abuse and neglect impair these same abilities, lower self-esteem, and may make an individual more likely to be a target of bullying. In one of the few longitudinal studies to extend beyond one year, Juvonen and colleagues (2011) examined the relation between victimization 8 and academic achievement across the three years of middle school. Academic adjustment was measured by both year-end grades and teacher reports of engagement. These authors found that more self-reported victimization was related to lower school achievement from sixth to eighth grade. For every 1-unit increase in victimization (on a 1-4 scale), GPA declined by 0.3 points.

Other short-term longitudinal studies found similar results. For example, Nansel and colleagues (2003) found that being bullied in a given year (grade 6 or 7) predicted poor academic outcomes the following year, after controlling for prior school adjustment and if they were previously targets of bullying or not. Similarly, Schwartz and colleagues (2005) reported a negative association for third and fourth grade children between victimization 9 and achievement 1 year later. In addition, Baly and colleagues (2014) found that the cumulative impact of being bullied over 3 years from sixth grade to eighth grade had a negative impact on GPA and standardized test scores.

However, other studies have not found such associations. For instance, Kochenderfer and Ladd (1996) found no relation between being bullied and subsequent academic achievement in their study of students assessed in the fall and spring of kindergarten, nor did Rueger and Jenkins (2014) in their study of seventh and eighth graders assessed in the fall and spring of one academic year. Feldman and colleagues (2014) also reported no association between being a target of bullying and academic achievement in their 5-year longitudinal study of youth ages 11-14. Poor academic performance can also be a predictor of peer victimization ( Vaillancourt et al., 2013b ). The authors found that poor writing performance in third grade predicted increased bullying behavior in fifth grade that was stable until the end of eighth grade.

The longitudinal associations between peer victimization and school attendance are also equivocal, with some showing positive associations ( Baly et al., 2014 ; Buhs et al., 2006 ; Gastic, 2008 ; Kochenderfer and Ladd, 1996 ; Smith et al., 2004 ) and others not finding a statistically significant association ( Forero et al., 1999 ; Glew et al., 2008 ; Rueger et al., 2011 ; Vaillancourt et al., 2013b ). 10

In summary, there have been a number of cross-sectional and longitudinal studies that have provided support for a relation between being bullied and increased risk for poor academic achievement. However, given the inconsistent results found with longitudinal studies, more research is warranted in this area to more fully ascertain the relation between being bullied and academic achievement over time.

• CONSEQUENCES FOR INDIVIDUALS WHO BULLY

There is evidence that supports a finding that individuals who bully others have contradictory attributes ( Institute of Medicine and National Research Council, 2014a ; Vaillancourt et al., 2010b ). Research suggests that there are children and adolescents who bully others because they have some form of maladjustment ( Olweus, 1993a ) or, as mentioned in Chapter 3 , are motivated by establishing their status in a social network ( Faris and Ennett, 2012 ; Rodkin et al., 2015 ; Sijtsema et al., 2009 ; Vaillancourt et al., 2003 ). Consequently, the relation between bullying, being bullied, acceptance, and rejection is complex ( Veenstra et al., 2010 ). This complexity is also linked to a stereotype held by the general public about individuals who bully. This stereotype casts children and youth who bully others as being high on psychopathology, low on social skills, and possessing few assets and competencies that the peer group values ( Vaillancourt et al., 2010b ). Although some occurrence of this “stereotypical bully” or “classic bully” is supported by research ( Kumpulainen et al., 2001 ; Olweus, 1993a ; Sourander et al., 2007 ), when researchers consider social status in relation to perpetration of bullying behavior, a different profile emerges. These studies suggest that most children and youth who bully others wield considerable power within their peer network and that high-status perpetrators tend to be perceived by peers as being popular, socially skilled, and leaders ( de Bruyn et al., 2010 ; Dijkstra et al., 2008 ; Peeters et al., 2010 ; Thunfors and Cornell, 2008 ; Vaillancourt et al., 2003 ). High-status bullies have also been found to rank high on assets and competencies that the peer group values such as being attractive or being good athletes ( Farmer et al., 2003 ; Vaillancourt et al., 2003 ); they have also been found to rank low on psychopathology and to use aggression instrumentally to achieve and maintain hegemony (for reviews, see Rodkin et al., 2015 , and Vaillancourt et al., 2010b ). Considering these findings of contrasting characteristics of perpetrators of bullying behavior, it makes sense that the research on outcomes of perpetrating is mixed. Unfortunately, most research on the short- and long-term outcomes of perpetrating bullying behavior has not taken into account this heterogeneity when considering the impact to children and youth who have bullied their peers.

Psychosomatic Consequences

Findings from cross-sectional studies that reported data on individuals who bullied others have shown that these individuals are at risk of developing psychosomatic problems ( Gini, 2008 ; Srabstein et al., 2006 ). Gini and Pozzoli (2009) conducted a meta-analysis to test whether children involved in bullying behavior in any role are at risk for psychosomatic problems. They included studies ( n = 11) that examined the association between bullying involvement and psychosomatic complaints in children and adolescents between the ages of 7 and 16. The studies included in the meta-analysis used self-report questionnaires; reports from peers, parents, or teachers; and clinical interviews that resulted in a clinical rating of the subject's behaviors and health problems. The included studies also had enough information to calculate effect sizes. An analysis of six studies that met the selection criteria revealed that children who bully had a higher risk ( OR = 1.65, 95% CI [1.34, 2.04]) of exhibiting psychosomatic problems than their uninvolved peers.

This meta-analysis was limited because of its inclusion of cross-sectional and observational studies. Such studies do not allow firm conclusions on cause and effect; hence, the association between bullying perpetration and psychosomatic problems may be difficult to interpret. The methodologies used in the studies make them susceptible to bias and misclassification due to the reluctance of individuals who bully to identify themselves as perpetrators of bullying behavior. Also, the different forms of victimization included in the underlying studies were not reported in this meta-analysis. Additional research is needed to examine the involvement in perpetrating bullying behavior and its short- and long-term psychosomatic consequences.

Psychotic Problems

Using a population-based cohort study, Wolke and colleagues (2014) examined whether bullying perpetration and being a target of bullying in elementary school predicted psychotic experiences 11 in adolescence. The authors assessed 4,720 individuals between the ages of 8 and 11 who were involved in bullying either as perpetrators or targets. At age 18, suspected or definite psychotic experiences were assessed using semistructured interviews. After controlling for the child's gender, intelligence quotient at age 8, and childhood behavioral and emotional problems, the researchers found that both individuals who are bullied (child report at age 10: OR = 2.4, 95% CI [1.6, 3.4]; mother report: OR = 1.6, 95% CI [1.1, 2.3]) and individuals who bullied others (child report at age 10: OR = 4.9, 95% CI [1.3, 17.7]; mother report: OR = 1.2, 95% CI [0.46, 3.1]) had a higher prevalence of psychotic experiences at age 18. The authors concluded that “involvement in any role in bullying may increase the risk of developing psychotic experiences in adolescence” ( Wolke et al., 2014 , p. 2208).

In summary, several studies have focused on the consequences of bullying for individuals who are bullied and have also reported more broadly on consequences for perpetrators of aggressive behavior (see Gini and Pozzoli, 2009 ; Lereya et al., 2015 ; Reijntjes et al., 2010 ; Ttofi et al., 2011 ), but the consequences of bullying involvement for individuals who perpetrate bullying behavior have been rarely studied to date. That is, although there is a rich literature on aggressors and the outcomes of being aggressive, there are few studies examining bullying perpetration specifically, taking into account the power imbalance, repetition, and intentionality that differentiates aggression from bullying from other forms of peer aggression. As discussed in Chapter 2 , the available research on the prevalence of bullying behavior focuses almost entirely on the children who are bullied. More research, in particular longitudinal research, is needed to understand the short- and long-term physical health, psychosocial, and academic consequences of bullying involvement on the individuals who have a pattern of bullying others, when those individuals are distinguished from children who engage in general aggressive behavior.

• CONSEQUENCES FOR INDIVIDUALS WHO BULLY AND ARE ALSO BULLIED

Individuals who bully and are also bullied experience a particular combination of consequences that both children who are only perpetrators and children who are only targets also experience, such as comorbidity of both externalizing and internalizing problems, negative perception of self and others, poor social skills, and rejection by the peer group. However, at the same time this combination of roles in bullying is negatively influenced by the peers with whom they are interacting ( Cook et al., 2010 ). After controlling for adjustment problems existing prior to incidents of bullying others or being bullied, a nationally representative cohort study found that young children who have been both perpetrators and targets of bullying tended to develop more pervasive and severe psychological and behavioral outcomes than individuals who were only bullied ( Arseneault et al., 2006 ).

Adolescents who were involved in cyberbullying as both perpetrators and targets have been found to be most at risk for negative mental and physical health consequences, compared to those who were only perpetrators, those who were only targets, or those who only witnessed bullying ( Kowalski and Limber, 2013 ; Nixon, 2014 ). For example, the results from a study by Kowalski and Limber (2013) that examined the relation between children's and adolescents' experiences with cyberbullying or traditional bullying and outcomes of psychological health, physical health, and academic performance showed that students who were both perpetrators and targets had the most negative scores on most measures of psychological health, physical health, and academic performance, when compared to those who were only perpetrators, only targets, or only witnesses of bullying incidents.

Wolke and colleagues (2001) examined the association of direct and relational bullying experience with common health problems and found that students ages 6-9 who bullied others and were also bullied by others had more physical health symptoms than children who were only perpetrators or were not involved in bullying behavior. Hunter and colleagues (2014) evaluated whether adolescents who were involved in bullying experienced sleep difficulties more than adolescents who were not involved. They analyzed surveys that were originally collected on behalf of the UK National Health Service and had been completed by adolescents ages 11-17. Controlling for gender, school-stage, socioeconomic status, ethnicity, and other factors known to be associated with sleep difficulties—alcohol consumption, tea or coffee consumption, and illegal drug use—the authors found that individuals who were both perpetrators and targets in bullying incidents were almost three times more likely ( OR = 2.90, 95% CI [1.17, 4.92]) to experience these sleep difficulties, compared to uninvolved young people. Additional research is needed to identify the mechanisms underlying short- and long-term physical health outcomes of individuals who bully and are also bullied.

There is evidence that individuals who are both perpetrators and targets of bullying have the poorest psychosocial profile among individuals with any involvement in bullying behavior; their psychosocial maladjustment, peer relationships, and health problems are similar to individuals who are only bullied, while their school bonding and substance use is similar to individuals who are only perpetrators ( Graham et al., 2006 ; Nansel et al., 2001 , 2004 ). Individuals who both bully and are also bullied by others experience a greater variety of both internalizing and externalizing symptoms than those who only bully or those who are only bullied ( Kim et al., 2006 ).

Some meta-analyses have examined the association between involvement in bullying and internalizing problems in the school-age population and concluded that that individuals who are both perpetrators and targets of bullying had a significantly higher risk for psychosomatic problems than individuals who were only perpetrators or who were only targets ( Gini and Pozzoli, 2009 ; Reijntjes et al., 2010 ). In their meta-analysis, Gini and Pozzoli (2009) reviewed studies that examined the association between involvement in bullying and psychosomatic complaints in children and adolescents. Analysis of a subgroup of studies ( N = 5) that reported analyses for individuals who bully and are also bullied by others showed that these individuals have a significantly higher risk for psychosomatic problems than uninvolved peers ( OR = 2.22, 95% CI [1.77, 2.77]).

Studies suggest that individuals who bully and who are also bullied by others are especially at risk for suicidal ideation and behavior, due to increased mental health problems (see Holt et al., 2015 , and Box 4-1 ).

Suicidality: A Summary of the Available Meta-Analyses.

Similar to individuals who bully, individuals who bully and are also bullied by others often demonstrate heightened aggression compared with non-involved peers. Compared to these other groups, they are by far the most socially ostracized by their peers, most likely to display conduct problems, and least engaged in school, compared with those who are either just perpetrators or just targets; they also report elevated levels of depression and loneliness ( Juvonen et al., 2003 ). Additional research is needed that examines the unique consequences of those children and youth characterized as “bully-victims” because often they are not separated out from “pure victims” (those who are bullied only) in studies. School shootings are a violent externalizing behavior that has been associated with consequences of bullying behavior in the popular media (see Box 4-2 for additional detail).

Bullying and School Shootings.

Several studies have examined the associations between bullying involvement in adolescence and mental health problems in adulthood and have found that individuals who have bullied others and have also been bullied had increased risk of high levels of critical symptoms of psychosis compared to non-involved peers ( Gini, 2008 ; Sigurdson et al., 2015 ). Research is limited in this area, and the topic warrants further investigation.

• CONSEQUENCES OF BULLYING FOR BYSTANDERS

Bullying cannot be viewed as an isolated phenomenon; it is intertwined within the particular peer ecology that emerges, an ecology constituted of social processes that serve particular functions for the individual and for the group ( Rodkin, 2004 ). Bullying frequently occurs in the presence of children and youth who are bystanders or witnesses. Research indicates that bullying can have significant adverse effects on these bystanders ( Polanin et al., 2012 ).

Bystanders have reported feelings of anxiety and insecurity ( Rigby and Slee, 1993 ) which stemmed, in part, from fears of retaliation ( Musher-Eizenman et al., 2004 ) and which often prevented bystanders from seeking help ( Unnever and Cornell, 2003 ). In a study to explore the impact of bullying on the mental health of students who witness it, Rivers and colleagues (2009) surveyed 2,002 students, ages 12-16 and attending 14 schools in the United Kingdom, using a questionnaire that included measures of bullying at school, substance abuse, and mental health risk. They found that witnessing bullying significantly predicted elevated mental health risks even after controlling for the effect of also being a perpetrator or victim (range of = .07 to .15). They also found that being a witness to the bullying predicted elevated levels (= .06) of substance use. Rivers and Noret (2013) found that, compared to students who were not involved in bullying, those who observed bullying reported more symptoms of interpersonal sensitivity (e.g., feelings of being hurt or inferior), helplessness, and potential suicide ideation.

In conclusion, there is very limited research available on the consequences of witnessing bullying for those children and youth who are the bystanders. Studies of bystander behavior have traditionally sought to understand their motives for participation in bullying ( Salmivalli, 2010 ), their roles ( Lodge and Frydenberg, 2005 ; Salmivalli et al., 1996 ), their behavior (either reinforcing the bully or defending the victim) in bullying situations ( Salmivalli et al., 2011 ), and why observers intervene or do not intervene ( Thornberg et al., 2012 ) from a social dynamic perspective, without exploring the emotional and psychological impact of witnessing bullying. More research is needed to understand these consequences.

MULTIPLE EXPOSURES TO VIOLENCE 12

One subpopulation of school-aged youth that may be at increased risk for detrimental short- and long-term outcomes associated with bullying victimization is poly-victims. Finkelhor and colleagues (2007) coined the terms “poly-victim” and “poly-victimization” to represent a subset of youth who experience multiple victimizations of different kinds—such as exposure to (1) violent and property crimes (e.g., assault, sexual assault, theft, burglary), (2) child welfare violations (child abuse, family abduction), (3) the violence of warfare and civil disturbances, and (4) being targets of bullying behavior—and who manifest high levels of traumatic symptomatology. The identification of a poly-victim is grounded not only in the frequency of the victimization but also in victimization across multiple contexts and perpetrators ( Finkelhor et al., 2007 , 2009 ).

Ford and colleagues (2010) determined that poly-victims were more likely to meet criteria for psychiatric disorder, including being two times more likely to report depressive symptoms, three times more likely to report posttraumatic stress disorder, up to five times more likely to use alcohol or drugs, and up to eight times more likely to have comorbid disorders, compared to youth that did not meet criteria for poly-victimization. Poly-victims often engaged in delinquent behavior, associated with deviant peers ( Ford et al., 2010 ), and were entrenched within the juvenile justice system ( Ford et al., 2013 ). Students who were poly-victims in the juvenile justice system reported higher levels of traumatic symptomatology ( Finkelhor et al., 2005 ). However, it is currently unclear whether being bullied plays a major or minor role in poly-victimization.

• MECHANISMS FOR THE PSYCHOLOGICAL EFFECTS OF BULLYING

In the following sections, the committee describes five potential mechanisms for the psychological effects of bullying behavior for both the children who are bullied and children who bully. These include self-blame, social cognition, emotional dysregulation, genetic predisposition to mental health outcomes and bullying, and telomere erosion. 13

One important mechanism for the psychological effects of bullying is how the targets of bullying construe the reason for their plight ( Graham, 2006 ). For example, a history of bullying and the perception of being singled out as a target might lead an individual to ask “Why me ?” In the absence of disconfirming evidence, some might come to blame themselves for their peer relationship problems. Self-blame and accompanying negative affect can then lead to many negative outcomes, including low self-esteem, anxiety, and depression ( Graham and Juvonen, 1998 ).

The adult rape literature (another form of victimization) highlights a correlation between experiencing rape and self-attributions that imply personal deservingness, labeled characterological self-blame, since they may lead to the person thinking of themselves as chronic victims ( Janoff-Bulman, 1979 ). From an attributional perspective, characterological self-blame is internal and therefore reflects on the self; it is stable and therefore leads to an expectation that harassment will be chronic; and it is uncontrollable, suggesting an inability to prevent future harassment. Attributing negative outcomes to internal, stable, and uncontrollable causes leads individuals to feel both hopeless and helpless ( Weiner, 1986 ). In contrast, behavioral self-blame (e.g., “I was in the wrong place at the wrong time”) implies a cause that is both unstable (the harassment is not expected to occur again) and controllable (there are responses in one's repertoire to prevent future harassment). Several researchers in the adult literature have documented that individuals who make characterological self-blaming attributions for negative outcomes cope more poorly, feel worse about themselves, and are more depressed than individuals who make attributions to their behavior (see Anderson et al., 1994 ). Research with early adolescents also revealed that characterological self-blame for academic and social failure resulted in heightened depression ( Cole et al., 1996 ; Tilghman-Osborne et al., 2008 ).

In the first attribution study focused specifically on bullying, Graham and Juvonen (1998) documented that sixth grade students with reputations as targets made more characterological self-blaming attributions for harassment than behavioral self-blaming attributions. Characterological self-blame, in turn, partly mediated the relationship between victim status and psychological maladjustment as measured by depression and social anxiety. Many studies since then have documented the relation between being targets of bullying, characterological self-blame, and maladjustment ( Graham et al., 2006 , 2009 ; Perren et al., 2012 ; Prinstein et al., 2005 ). Furthermore, bullied youth who endorsed characterological self-blame were likely to develop negative expectations about the future, which may also increase risk for continued bullying. For example, Schacter and colleagues (2014) reported that characterological self-blame endorsed in the fall of sixth grade predicted increases in reports of being bullied in the spring of sixth grade. Self-blame can then instigate psychological distress over time as well as increases in experiences of being bullied.

Such findings have implications for interventions targeted at bullied youth. The goal would be to change targets' maladaptive thoughts about the causes of their plight. For example, one could seek more adaptive attributions that could replace characterological self-blame. In some cases, change efforts might target behavioral explanations for being bullied (e.g., “I was in the wrong place at the wrong time”). In such cases, the goal would be to help targeted youth recognize that they have responses in their repertoire to prevent future encounters with harassing peers—that is, the cause is unstable and controllable ( Graham and Bellmore, 2007 ). External attributions also can be adaptive because they protect self-esteem ( Weiner, 1986 ). Knowing that others are also victims or that there are some aggressive youth who randomly single out unsuspecting targets can help lessen the tendency to self-blame ( Graham and Bellmore, 2007 ; Nishina and Juvonen, 2005 ). This approach of altering dysfunctional thoughts about oneself to produce changes in affect and behavior has produced a rich empirical literature on attribution therapy in educational and clinical settings (see Wilson et al., 2002 ). The guiding assumption of that research can be applied to alleviating the plight of targets of bullying.

Social Cognition

The most commonly cited models of social cognitive processes often connect back to work by Bandura (1973) , as well as to more recent conceptualizations by Crick and Dodge (1994) . These models have been applied to understanding aggressive behavior, but there has been less research applying these models to bullying behavior specifically. Related research by Anderson and Bushman (2002) on their general aggression model allows for a more focused understanding of the thoughts, feelings, and behaviors that contribute to the development of the negative outcome. This framework characterizes the inputs, the routes, the proximal processes, and the outcomes associated with aggressive behavior and either being targeted by or perpetrating bullying behavior ( Kowalski and Limber, 2013 ; Vannucci et al., 2012 ). Although these theories pertain to aggressive behavior more broadly, given that bullying is considered by most researchers to be a specific form of aggressive behavior, these broader theories may also improve understanding of the etiology and development of bullying. For example, research on hostile attribution bias suggests that aggressive youth are particularly sensitive to ambiguous and potentially hostile peer behaviors. Similar hypersensitivity to threat is also likely present in youth who bully.

Another particular element of social cognitive processes that has been linked with aggressive behavior is normative beliefs about aggressive retaliation ( Crick and Dodge, 1994 ; Huesmann and Guerra, 1997 ). Such beliefs include the belief that aggressive retaliation is normative, acceptable, or justified, given the context of provocation. There has been exploration of links between these beliefs and both reactive and proactive aggression. However, there has been relatively limited research specifically focused on bullying behavior. Yet, the available literature suggests that although it may seem as if targets of bullying would most likely endorse such attitudes, it is the perpetrators of bullying, including those who are involved in bullying as both a perpetrator and a target, who are mostly likely to support aggressive retaliation ( Bradshaw et al., 2009 , 2013 ; O'Brennan et al., 2009 ).

Emotion Dysregulation

Attempts to identify mechanisms linking bullying to adverse outcomes have largely focused on social-cognitive processes ( Dodge et al., 1990 ) as described above. More recently, researchers have begun to examine emotion dysregulation as an additional mechanism that explains associations between peer victimization and adverse outcomes. Emotion regulation refers to the strategies that people use to “increase, maintain, or decrease one or more components of an emotional response” ( Gross, 2001 , p. 215). One's choices among such strategies have implications not only for how robustly one responds to a stressor but also for how quickly one can recover from a stressful experience. Several studies have shown that emotion regulation difficulties—also called emotion dysregulation —increase youths' risk of exposure to peer victimization ( Hanish et al., 2004 14 ) and to bullying ( Mahady Wilton et al., 2000 ). However, it is important to understand whether peer victimization itself causes emotion regulation difficulties, which in turn predict the adverse outcomes that result from peer victimization (e.g., depression, aggressive behaviors).

Several lines of evidence support the hypothesis that emotion dysregulation may account for the relationship between peer victimization and adverse outcomes among adolescents. First, constructs that are related to peer victimization—including social exclusion ( Baumeister et al., 2005 ) and stigma ( Inzlicht et al., 2006 )—impair self-regulation. Second, chronic stress during childhood and adolescence leads to deficits in emotion regulation ( Repetti et al., 2002 ). Bullying has been conceptualized as a chronic stressor for children who are the perpetrators and the targets ( Swearer and Hymel, 2015 ), which in turn may disrupt emotion regulation processes. Third, laboratory-based studies have indicated that peer victimization is associated with emotion dysregulation (e.g., self-directed negative emotion, emotional arousal and reactivity) in the context of a novel peer interaction ( Rudolph et al., 2009 ) and in a contrived play-group procedure ( Schwartz et al., 1993 ). Over time, the effort required to manage the increased arousal and negative affect associated with peer victimization 15 may eventually diminish individuals' coping resources and therefore their ability to understand and adaptively manage their emotions, leaving them more vulnerable to adverse outcomes ( McLaughlin et al., 2009 ).

Several studies have provided empirical support for emotion dysregulation as a mediator of the association between peer victimization and adverse outcomes among adolescents. In one of the first longitudinal demonstrations of mediation, McLaughlin and colleagues (2009) , using data from a large, prospective study of adolescents (ages 11-14), showed that peer victimization at baseline predicted increases in emotion dysregulation four months later, controlling for initial levels of emotion dysregulation. In turn, emotion dysregulation predicted subsequent psychological distress (depressive and anxious symptoms), thereby mediating the prospective relationship between peer victimization (relational and reputational forms) and internalizing symptoms ( McLaughlin et al., 2009 ). Subsequent research from this same sample of adolescents showed that emotion dysregulation also mediated the prospective relationship between peer victimization and subsequent aggressive behavior ( Herts et al., 2012 ).

There is also emerging evidence that emotion regulation mediates relationships between bullying and adverse outcomes. In one example of this work, Cosma et al. (2012) examined associations between bullying and several emotion regulation strategies, including rumination, catastrophizing, and other-blaming, in a sample of adolescents. Although bullying was predictive for each of these emotion regulation strategies, only one (catastrophizing) mediated the relationship between being a target of bullying and subsequent emotional problems. Thus, while more research is needed, existing evidence suggests that both social-cognitive and emotion regulation processes may be important targets for preventive interventions among youths exposed to peer victimization and bullying.

Genetic Predisposition to Mental Health Outcomes and Bullying

Longitudinal research suggests that being the victim or perpetrator of bullying does not lead to the same pathological or nonpathological outcomes in every person ( McDougall and Vaillancourt, 2015 ). There are many factors that contribute to how a person responds to the experience of being victimized, with very strong links already established with life experiences, as reviewed above. Most studies examining heterogeneity in outcomes associated with bullying have focused on environmental characteristics, such as individual, family, and school-level features to explain why some individuals fare better or worse when involved with bullying ( Vaillancourt et al., in press ). For example, the moderating role of the family has been examined with results indicating that bullied children and youth with better home environments tend to fare better than those living with more complicated families ( Flouri and Buchanan, 2003 ; also see Chapter 3 of this report). Far fewer studies have examined the role of potential genetic influences as mediators between life experiences such as bullying and mental health outcomes. Identifying potential genetic influences is critical for improving understanding of the rich behavioral and epidemiological data already gathered. At the present time, evidence-based understanding of physiology and neuroscience is very limited, and insufficient data have been gathered to produce informed hypothesis testing.

There is a growing body of literature examining the relative role of genes' interaction with the environment in relation to experiences with trauma. However, there are fewer studies exploring potential relations between genes and being the target or perpetrator of bullying. At first glance these studies may appear to suggest that a person's involvement with bullying is predetermined based on his/her genetic profile. Yet, it is important to bear in mind that heritable factors are also associated with specific environments—meaning it is difficult to separate genetic effects from environmental effects. This is a phenomenon termed gene-environment correlations , abbreviated as rGE ( Brendgen, 2012 ; Plomin et al., 1977 ; Scarr and McCartney, 1983 ). For example, aggression, which is highly heritable ( Niv et al., 2013 ), can be linked to the selection of environments in different ways (for review, see Brendgen, 2012 ). Aggressive children may choose friends who are similar in their genetically influenced behavioral characteristic of being aggressive, and this type of selection influences the characteristics of their peer group ( Brendgen, 2012 , p. 420). This is an example of selective rGE. A child's genetically influenced characteristic to be aggressive can also produce a negative reaction from others, such as being disliked. This environmental variable of being rejected now “becomes correlated with the aggressive genotype” ( Brendgen, 2012 , p. 421). This is an example of evocative rGE. Another way that a person's genetic predisposition can be correlated with their environment is through a more passive process, called a passive rGE ( Brendgen, 2012 ). For example, aggressive parents may be more likely to live in high-crime neighborhoods, which influence the probability that their child will be associating with antisocial peers. These important rGE processes and confounds of interaction notwithstanding, it is worth mentioning that the research on the genetics of being a target or perpetrator of bullying is still in its infancy, and caution is needed when evaluating the results, as replication is much needed in this area. Before considering these studies, the committee first reviews the concept of how genetic differences influence behavior because it is important to clarify new concepts in this burgeoning area of science (see Box 4-3 ).

How Do Genes Influence Behavior?

With this backdrop in mind, the committee focused on twin studies of familial (family environment) versus genetic influence, gene by environment interaction, and a newer area of inquiry, epigenetics: the study of cellular and physiological phenotypic trait variations caused by external or environmental factors.

Twin Studies

Twin studies are routinely used to examine the relative influence of genetics and the environment on a particular phenomenon, such as being the target or perpetrator of bullying. In these studies, the causes of phenotypic variation (for example the variation in being a target or perpetrator of bullying) is separated into three components: (1) the additive genetic component or the heritable factor; (2) the shared environment component or the aspect of the environment twins share such as poor family functioning; and (3) the nonshared environment component or the aspect of the environment that is unique to each twin, such as the classroom if twins are in different classes.

Studies that decompose the unique effects of the environment and genetics on bullying behavior are best illustrated by two examples. Using data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a study of high-risk 16 British twins reared together and apart, Ball and colleagues (2008) examined children's involvement in bullying and the genetic versus environmental contributions associated with their involvement. The twins in this study were assessed at ages 7 and 10 on their experiences with bullying, using teacher and parent reports. Results indicated that 73 percent of the variation in being the target of bullying and 61 percent of the variation in bullying perpetration were accounted for by genetic factors. In another study of Canadian twins reared together and assessed at age 7, using teacher and peer reports to assess peer victimization and aggression, Brendgen and colleagues (2008) found that for girls, 60 percent of the variation in aggression was accounted for by genetic factors and for boys, the variation estimate was 66 percent. For peer victimization, the Canadian study found that genetics did not play a role in the prediction of being targeted by peers. In fact, almost all of the variance was accounted for by environmental factors—29 percent of the variance in peer victimization was from the shared environment and 71 percent from the nonshared environment. The authors concluded that “genetic modeling showed that peer victimization is an environmentally driven variable that is unrelated to children's genetic disposition” ( Brendgen et al., 2008 , p. 455).

These two studies address the role genetics might play in the expression of aggressive behavior but conflict on the heritability of being a target of bullying. Most studies examining the heritability of externalizing problems, which includes studies on perpetrating aggression and bullying, report high heritability estimates. In fact, a recent meta-analysis found that aggression and rule-breaking were highly influenced by genetics, estimating the heritability rate at 41 percent ( Niv et al., 2013 ). Moreover, studies have found that the heritability estimates tend to be higher for more serious forms of antisocial behavior. For example, the heritability of psychopathy in 7-year-old British twin children reared together and apart and studied in the Twins Early Development Study was reported to be 81 percent ( Viding et al., 2005 ). However, estimates of the heritability of peer victimization vary across studies, as illustrated by the above results from Ball and colleagues (2008) contrasted with those from Brendgen and colleagues (2008) , and even within studies ( Brendgen et al., 2008 , 2013 ).

Brendgen and colleagues have since revised their assessment about the role genetics play in the prediction of being the target of bullying. In a more recent study, following the same children highlighted in the 2008 paper ( Brendgen et al., 2008 ) across three assessment periods (kindergarten, grade 1, and grade 4), Boivin and colleagues (2013) reported that at each grade, among twins who were reared together and apart, genetic factors accounted for a notable percentage of the variance in children's difficulties with peers. Peer difficulties were assessed as a latent factor derived from self-, teacher-, and peer-reports of peer victimization 17 and peer rejection. Specifically, in kindergarten and grade 1, 73 percent of the variance was accounted by genetic factors and in grade 4, genetic factors account for 94 percent of the variance in peer rejection and victimization.

There are several reasons for discrepancies between and within studies of the genetic contribution to bullying behavior. One reason is related to how peer victimization is assessed. Parent-, teacher-, peer-, and self-reports of bullying victimization have been shown to vary considerably across reporters ( Ostrov and Kamper, 2015 ; Patton et al., 2015 ; Shakoor et al., 2011 ); thus, the method used to assess involvement with bullying may lead to different results. Another reason for the differences may be related to development. The influence of the environment is expected to change as children age. Young children are particularly sensitive to family influences, while the influence of peers tends to matter more during adolescence ( Harris, 1995 ). Moreover, the type of environment a person is exposed to (i.e., harsh or nurturing) interacts with genes to produce a brain that is tailored to deal with the particular demands of that environment.

Taken together, the genetic studies reviewed suggest that aggression, which characterizes the perpetrator role in bullying ( Vaillancourt et al., 2008 ), might have heritable components, but the findings on being the target of bullying or other aggressive behavior are mixed. Thus, the role of genetic influences on both perpetrating and being a target of bullying requires more empirical attention before conclusions can be drawn.

Gene-by-Environment Interactions

Researchers also question whether specific genotypic markers of vulnerability (e.g., candidate genes) influence developmental outcomes in the face of adversity (i.e., environment). Importantly, there is some indication that genetics influences the mental health issues related to bullying highlighted above, such as depression and heightened aggression. For example, in gene-environment studies, candidate genes have been examined as moderators of the exposure to a toxic stressor such as child maltreatment and health outcomes such as depression. When the body experiences repeated bouts of stress that fail to resolve quickly, the heightened state of vigilance and preparedness depletes it of resources and the stress hormone cortisol begins to produce adverse effects. Specifically, prolonged stress disrupts brain functions and results in compromised decision making, faulty cognitive assessment, compromised learning and memory, and a heightened sense of threat that alters behavior ( Lupien et al., 2005 ; McEwen, 2014 ). There is evidence that the impact of changes in cortisol (either too high or too low) on learning may contribute, in part, to bullied children's decline in academic performance ( Vaillancourt et al., 2011 ), overeating/metabolic disorder, or emotional dysregulation, but this research is relatively new and needs to be explicitly explored within the context of bullying ( McEwen, 2014 ).

A paradigmatic example of this type of study is one by Caspi and colleagues (2003) , in which the moderating role of a functional polymorphism in the promoter region of the serotonin transporter gene 5-HTTLPR was examined in relation to exposure to maltreatment in childhood and depression in adulthood. Results indicated that depression rates were far greater among abused individuals if they had two copies of the short allele. 18 Among individuals with a long allele, depression rates were lower, suggesting that the long allele was protective, while the short allele was a risk factor for depression in the face of adversity. Although the exact role of this serotonin-related gene has been a subject of controversy, a meta-analysis concluded that overall, the results are consistent across studies ( Karg et al., 2011 ). Nevertheless, skepticism and controversy remain regarding studies of gene-environment interactions ( Dick et al., 2015 ; Duncan, 2013 ; Duncan and Keller, 2011 ; Duncan et al., 2014 ). This important debate notwithstanding, there is evidence that variations in genotype might moderate the relation between exposure to being bullied and health outcomes. For example, Sugden and colleagues (2010) found that bullied children who carried two short versions of the 5-HTTLPR gene were more likely to develop emotional problems than bullied children who carried the long allele. Importantly, this moderating effect was present even when pre-victimization emotional problems were accounted for statistically. In addition to this study, three other studies have demonstrated the moderating effect of the 5-HTTLPR gene in the bullying-health link ( Banny et al., 2013 ; Benjet et al., 2010 ; Iyer et al., 2013 ), with depression being worse for carriers of the short/short genotype (both alleles are the short version) than carriers of the short/long and long/long genotypes.

Although the evidence suggests that genotypes moderate the relation between being a target of bullying and poorer mental health functioning like depression, it is important to acknowledge that this relation is more complex. Indeed, some individuals may be particularly biologically sensitive to negative environmental influences such as being bullied, but this genetic vulnerability can also be linked to better outcomes in the context of a more supportive and enriched environment (see Vaillancourt et al., in press ). This phenomenon is termed differential susceptibility ( Belsky and Pluess, 2009 ; Boyce and Ellis, 2005 ). For example, in their study of 5 and 6-year old children, Obradovic and colleagues (2010) found that high stress reactivity as measured using respiratory sinus arrhythmia and salivary cortisol was linked to poorer socioemotional behavior in the context of being in an environment that was high in family adversity. In a context characterized by lower adversity, high stress-reactive children had more adaptive outcomes.

To the committee's knowledge, there are no studies that have examined bullying perpetration in relation to serotonin transporter polymorphisms, although there are studies that have examined this polymorphism in aggressive and non-aggressive children. For example, Beitchman et al. (2006) examined 5-HTTLPR in clinically referred children between the ages of 5 and 15 and found a positive association between the short/short genotype and aggression. In other studies, the short allele has been associated with problems with impulse control that includes the use of aggression ( Retz et al., 2004 ).

The moderating role of different candidate genes has also been examined in relation to exposure to childhood adversity and poorer developmental outcomes (see review by Vaillancourt et al., in press ). With respect to bullying, only a few studies have examined gene-environment interactions. In one study by Whelan and colleagues (2014) , harsh parenting was associated with increased peer victimization and perpetration, but this effect was not moderated by the Monoamine Oxidase A (MAOA) genotype. 19 In another longitudinal study, Kretschmer and colleagues (2013) found that carriers of the 4-repeat homozygous variant of the dopamine receptor D4 gene were more susceptible to the effects of peer victimization 20 on delinquency later in adolescence than noncarriers of this allele. Finally, in a large sample of post-institutionalized children from 25 countries, VanZomeren-Dohm and colleagues (2015) examined the moderating role of FKBP5 rs1360780 21 in the relation between peer victimization 22 and depression symptoms. In this study, gender was also found to be a moderator. Specifically, girls who had the minor genotype (TT or CT) were more depressed at higher levels of peer victimization, but less depressed at lower level of peer victimization than girls who had CC genotype. For boys, the CC genotype was associated with more symptoms of depression than girls with the same CC genotype who had been bullied.

It is clear that genetics influences how experiences contribute to mental and physical well-being, although the specifics of these gene-environment interactions are complex and not completely understood. Even though genes appear to modulate humans' response to being a target or a perpetrator of bullying behavior, it is still unclear what aspects of these experiences are interacting with genes and which genes are implicated to produce the variability in outcomes. Human genes and environment interact in a very complex manner: what biological events a particular gene influences can change at different stages of development. That gene therefore interacts with the environment in unique ways across the development timeline. These gene-environment interactions can be subtle and are under constant flux ( Lake and Chan, 2015 ). Knowing both the genes involved and the specific environment conditions is critically important to understanding these interactions; a simplistic view of either the genetic or environmental component, especially when considered in isolation from the behavioral literature, is unlikely to be productive.

Epigenetic Consequences

It is clear from the research reviewed here that there are a variety of pathways leading to adaptive and maladaptive endpoints and that these pathways can also vary within the “system” along with other conditions and attributes ( McDougall and Vaillancourt, 2015 , p. 300), including a person's genetic susceptibility. In this section, the committee focuses on studies examining how genetic susceptibility can make certain individuals more sensitive to negative environmental influences.

Although a person's DNA is fixed at conception (i.e., nonmalleable), environment can have a strong effect on how some genes are used at each of the stages of development. One way such changes in gene use and expression can occur is through an epigenetic effect, in which environmental events alter the portions of the genome that control when gene replication is turned on or off and what parts of a gene get transcribed ( McGowan et al., 2009 ; Roth, 2014 ). That is, while an individual's genetic information is critically important, the environment can help to increase or decrease how some genetic information is used by indirectly turning on or off some genes based on input received by somatic cells from the environment. Such epigenetic alterations have been empirically validated in several animal studies. For example, in one line of epigenetic studies, infant rat pups are raised with either low- or high-nurturing mothers or with mothers that treated the pups harshly. The researchers found that the type of maternal care received in infancy had a notable effect on the rats' subsequent ability to deal with stress ( McGowan et al., 2011 ; Roth and Sweatt, 2011 ; Weaver et al., 2004 ). The behavioral effects were correlated with changes in DNA methylation. 23 Epigenetic changes associated with gene-environment interactions is a new and exciting research area that provide a direct link between how our genes are read and is thought to enable us to pass our experiences to the next generations. It is helpful to think of genes as books in a library and epigenetics as placing a barrier in front of a book to decrease the chances it is read or providing easy access to the book. Thus far, research has found that certain epigenetic mechanisms are strongly correlated with different neurobehavioral developmental trajectories, including changes in vulnerability and resilience to psychopathology. How epigenetics relates to individual responses to being a target or perpetrator of bullying is not clear, but the research in related areas of behavior highlights an important emerging area for investigation.

Various epigenetic processes appear to interact with many changes in the brain produced by early life experiences, including not only the number and shape of brain cells but also how these cells connect to one another at synapses ( Hanson et al., 2015 ).

Regarding bullying, the committee identified only one study that has examined epigenetic changes. Specifically, Ouellet-Morin and colleagues (2013) found an increase in DNA methylation of the serotonin transporter gene for children who had been bullied by their peers but not in children who had not been bullied. These researchers also found that children with higher serotonin DNA methylation had a blunted cortisol response to stress, which they had previously shown changes as a consequence of poor treatment by peers ( Ouellet-Morin et al., 2011 ). That is, their 2011 study of twin children assessed at ages 5 and 10 found that being bullied was correlated with a change in how the body responds to stress. Bullied children displayed a blunted cortisol response to a psychosocial stress test. Because the design of the study involved an examination of identical twins who were discordant with respect to their experiences of being bullied (one twin was bullied while the other one was not), Ouellet-Morin and colleagues (2011) concluded that the effect could not be attributed to “variations in either genetic makeup, family environment, or other concomitant factors, nor could they be attributed to the twins' perceptions of the degree of stress experienced during the task” ( Vaillancourt et al., 2013a , p. 243).

In summary, it is important to note that there is no gene for being a perpetrator or a target of bullying behavior. Based on current knowledge of the genetics of complex social behavior, such as bullying, the genetic component of individual response is likely to involve multiple genes that interact with the environment in a complex manner. The current understanding of genetics and complex behaviors is that genes do not cause a behavior; gene-by-environment studies do not use the word “environment” the same way it is used in everyday language or even in traditional social psychology (as in Chapter 3 ). Rather, it is a construct used in a model to estimate how much variability exists in a given environment. This means that the same gene placed in different environments would yield very different percentages for gene-environment interactions. It is unclear how this information would inform our understanding of bullying.

Telomere Erosion Consequences

Epigenetic research has found that negative life experiences can alter the expression of a gene, which in turn, can confer a risk for poor outcomes. Research also suggests that the experience of being bullied is associated with telomere erosion. The end of each chromatid has been found to shorten as people age; this telomere “tail” also erodes as a function of engaging in unhealthy behavior such as smoking or being obese. Telomere erosion is also associated with certain illnesses such as cancer, diabetes, and heart disease ( Blackburn and Epel, 2012 ; Kiecolt-Glaser et al., 2011 ; Vaillancourt et al., 2013a ). Given these associations, scientists are now examining telomere erosion as a biomarker of stress exposure ( Epel et al., 2004 ), including the stress of being bullied by peers.

A recent longitudinal study by Shalev and colleagues (2013) examined telomere erosion in relation to children's exposure to violence, 24 a significant early-life stressor that is known to have long-term consequences for health. They found that exposure to violence, including being a target of bullying, was associated with telomere erosion for children assessed at age 5 and again at age 10. The sample for this study included 236 children recruited from the Environmental-Risk Longitudinal Twin Study ( Moffitt, 2002 ), 42 percent of whom had one or more exposures to violence. The study found that cumulative exposure to violence 25 is positively associated with accelerated telomere erosion in children, from baseline to follow-up, with potential impact for life-long health ( Shalev et al., 2013 ).

In this chapter, the committee reviewed and critically analyzed the available research on the physical health, psychosocial, and academic achievement consequences for children and youth who are bullied, for those who bully, for those who are both bullied and bullies, and for those who are bystanders to events of bullying. It also examined the potential mediating mechanisms of, and the genetic predisposition to, mental health outcomes associated with childhood and youth experiences of bullying behavior. Most studies are cross-sectional and thus provide only associations suggestive of a possible causal effect. This problem is most acute for studies based on anonymous self-report, in which both the independent variable (experience of bullying in one or more roles) and dependent variables (such as emotional adjustment) are data collected at the same time from sources subject to various forms of bias.

The limited amount of data from longitudinal and experimental research designs limits the ability to draw conclusions with respect to causality. Additional longitudinal studies, for example, could help establish that the negative consequences attributed to bullying were not present before the bullying occurred. But even this does not prove a causal effect, since bullying and the associated impairments might be products of some third factor. Below, the committee summarizes what is known about associations and consequences and identifies key conclusions that can be drawn from this evidence base.

• FINDINGS AND CONCLUSIONS

Finding 4.1: Individuals who both bully and are also bullied by others experience a greater variety of both internalizing and externalizing symptoms than those who only bully or are only bullied. Finding 4.2: Individuals who bully others are likely to experience negative emotional, behavioral, and mental health outcomes, though most research has not distinguished perpetration of bullying from other forms of peer aggression. Finding 4.3: A large body of research indicates that individuals who have been bullied are at increased risk of subsequent mental, emotional, and behavioral problems, especially internalizing problems. Finding 4.4: Studies of bystander behavior in bullying have rarely examined the emotional and psychological impact of witnessing bullying. Finding 4.5: Children and youth who are bullied subsequently experience a range of somatic disturbances. Finding 4.6: Social-cognitive factors (e.g., self-blame) and unsuccessful emotion regulation (i.e., emotion dysregulation) mediate relationships between bullying and adverse outcomes. Finding 4.7: There is evidence that stressful events, such as might occur with experiences of being bullied, alter emotional brain circuits. This potential outcome is critically in need of further investigation. Finding 4.8: Genetics influences how experiences contribute to mental and physical well-being, although the nature of this relationship is complex and not completely understood. Finding 4.9: Emerging evidence suggests that repeated exposure to bullying may produce a neural signature that could underlie some of the behavioral outcomes associated with being bullied. Finding 4.10: There are limited data on the physical health consequence of bullying for those individuals who are involved in bullying as targets, perpetrators, as both targets and perpetrators, and as bystanders. Finding 4.11: Poly-victims (individuals who are targets of multiple types of aggression) are more likely to experience negative emotional, behavioral, and mental health outcomes than individuals targeted with only one form of aggression. Finding 4.12: The long-term consequences of being bullied extend into adulthood and the effects can be more severe than other forms of being maltreated as a child. Finding 4.13: Individuals who are involved in bullying (as perpetrators, targets, or both) in any capacity are significantly more likely to contemplate or attempt suicide, compared to children who are not involved in bullying. It is not known whether bystanders are at increased risk of suicidal ideation or suicide attempts. Finding 4.14: There is not enough evidence to date to conclude that being the target of bullying is a causal factor for multiple-homicide targeted school shootings, nor is there clear evidence on how experience as a target or perpetrator of bullying, or the mental health and behavior issues related to such experiences, contribute to school shootings.

Conclusions

Conclusion 4.1: Further research is needed to obtain more in-depth evidence on the physical health consequences of being the target of bullying including neural consequences. Conclusion 4.2: Additional research is needed to examine mediators of short- and long-term physical health outcomes of individuals who are bullied. Evidence is also needed regarding how these outcomes vary over time for different groups of children and youth, why individuals with similar experiences of being bullied might have different physical health outcomes, and how physical and emotional health outcomes intersect over time. Conclusion 4.3: Although the effects of being bullied on the brain are not yet fully understood, there are changes in the stress response systems and in the brain that are associated with increased risk for mental health problems, cognitive function, self-regulation, and other physical health problems. Conclusion 4.4: Bullying has significant short- and long-term internalizing and externalizing psychological consequences for the children who are involved in bullying behavior. Conclusion 4.5: The data are unclear on the role of bullying as one of or a precipitating cause of school shootings. Conclusion 4.6: Individuals who both bully others and are themselves bullied appear to be at greatest risk for poor psychosocial outcomes, compared to those who only bully or are only bullied and to those who are not bullied. Conclusion 4.7: While cross-sectional studies indicate that children who are bullied are at increased risk for poor academic achievement relative to those who are not bullied, the results from longitudinal studies are inconsistent and warrant more research. Conclusion 4.8: Existing evidence suggests that both social-cognitive and emotion regulation processes may mediate the relation between being bullied and adverse mental health outcomes. Conclusion 4.9: Although genes appear to modulate humans' response to being either a target or a perpetrator of bullying behavior, it is still unclear what aspects of these experiences are interacting with genes and which genes are implicated to produce the variability in outcomes. Examining the role of genes in bullying in the context of the environment is essential to providing meaningful information on the genetic component of individual differences in outcomes from being a target or a perpetrator of bullying behavior.

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Somatization is “a syndrome of physical symptoms that are distressing and may not be fully explained by a known medical condition after appropriate investigation. In addition, the symptoms may be caused or exacerbated by anxiety, depression, and interpersonal conflicts, and it is common for somatization, depression, and anxiety to all occur together” ( Greenberg, 2016 ).

Consolidation of memory is a biological process where the information one learns is stabilized within neural circuits and placed into long-term memory through a complex orchestration of molecular-level change and gene activation within neurons.

Peer victimization was measured with a 21-item revised version of the Social Experiences Questionnaire. The measure assesses overt and relational victimization and frequency of different acts of victimization ( Rudolph et al., 2016 ).

Reijntjes and colleagues (2010, p. 244) defined peer victimization as taking “various forms, including direct bullying behaviors (e.g., teasing, physical aggression) as well as more indirect manifestations such as group exclusion or malicious gossip.” Hawker and Boulton (2000, p. 441) defined peer victimization as “the experience among children of being a target of the aggressive behavior of other children, who are not siblings and not necessarily age-mates.”

Peer victimization was measured using peer, self-, and teacher reports, including peer nominations, a four-item self-report victimization scale, and a six-item teacher report victimization scale ( Kochel et al., 2012 ).

Stapinski et al. (2014) used a modified version of the Bullying and Friendship Interview Schedule to assess self-reported peer victimization. This measure includes items on overt victimization, such as threats, physical violence, and relational victimization.

Nonclinical psychotic symptoms are symptoms that do not meet the clinical definition for those psychotic disorders associated with such symptoms.

Peer victimization was measured using a modified six-item version of the Peer Victimization Scale, which asks students to select a statement that is most like them. Higher scores indicated higher levels of peer victimization ( Juvonen et al., 2011 ).

Peer victimization was measured using a 16-item peer nomination interview and a teacher-completed Social Behavior Rating Scale ( Schwartz et al., 2005 ).

Peer victimization is used here to include the broader category of bullying, peer victimization, and bullying behavior.

Psychotic experiences included hallucinations (visual and auditory), delusions (spied on, persecution, thoughts read, reference, control, grandiosity), and experiences of thought interference (broadcasting, insertion, and withdrawal), and any unspecified delusions.

This section is adapted from Rose (2015 , pp. 18-21).

A telomere is the “segment at the end of each chromosome arm which consists of a series of repeated DNA sequences that regulate chromosomal replication at each cell division.” See http://ghr ​.nlm.nih.gov/glossary=telomere [December 2015]. Telomeres are associated with “chromosomal stability” and the regulation of “cells' cellular replicative lifespan” (Kiecolt-Glaser et al., 2011, p. 16).

Peer victimization was measured by a teacher-reported seven-item measure with items measuring broader peer victimization (Hanish et al., 2004).

Peer victimization was measured using the Revised Peer Experiences Questionnaire, which assesses overt, relational, and reputational victimization by peers (McLaughlin et al., 2009).

High risk was defined as a mother who had her first child at age 20 or younger ( Moffitt, 2002 ).

Peer victimization was assessed through teacher, peer, and self-ratings. Children were asked to circle photographs of two classmates who get called names by other children and who are often pushed or hit by other children.

An allele is an alternate form of the same gene. Except for the XY chromosomes in males, human chromosomes are paired, so a cell's genome usually has two alleles for each gene.

The MAOA genotype has been called the “warrior” gene because of its association with aggression in studies using surveys and observations ( McDermott et al., 2009 ).

Peer victimization was measured using a teacher-report 3-item scale that assessed relational victimization in the classroom ( Kretschmer et al., 2013 ).

The FKBP5 rs1360780 gene is associated with a number of different psychological disorders ( Wilker et al., 2014 ).

VanZomeren-Dohm and colleagues (2015 measured peer victimization using the MacArthur Health and Behavior Questionnaire Parent-Form, version 2.1, in which parents reported on their children's experiences of overt peer victimization.

DNA methylation is a heritable epigenetic mark involving the covalent transfer of a methyl group to the C-5 position of the cytosine ring by DNA methyltransferases (a family of enzymes that act on DNA). Cytosine is one of the four bases that occur in varying sequences to form the “code” carried by strands of DNA ( Robertson, 2005 ).

Exposure to violence included domestic violence, bullying victimization, and physical abuse by an adult.

Cumulative violence exposure was measured by an index that summed each type of violence exposure.

• Cite this Page Committee on the Biological and Psychosocial Effects of Peer Victimization: Lessons for Bullying Prevention; Board on Children, Youth, and Families; Committee on Law and Justice; Division of Behavioral and Social Sciences and Education; Health and Medicine Division; National Academies of Sciences, Engineering, and Medicine; Rivara F, Le Menestrel S, editors. Preventing Bullying Through Science, Policy, and Practice. Washington (DC): National Academies Press (US); 2016 Sep 14. 4, Consequences of Bullying Behavior.
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Q Methodology as an Innovative Addition to Bullying Researchers’ Methodological Repertoire

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• Published: 11 May 2022
• Volume 4 , pages 209–219, ( 2022 )

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• Adrian Lundberg   ORCID: orcid.org/0000-0001-8555-6398 1 &
• Lisa Hellström   ORCID: orcid.org/0000-0002-9326-1175 1  

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A Correction to this article was published on 18 July 2022

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The field of bullying research deals with methodological issues and concerns affecting the comprehension of bullying and how it should be defined. For the purpose of designing relevant and powerful bullying prevention strategies, this article argues that instead of pursuing a universal definition of what constitutes bullying, it may be of greater importance to investigate culturally and contextually bound understandings and definitions of bullying. Inherent to that shift is the transition to a more qualitative research approach in the field and a stronger focus on participants’ subjective views and voices. Challenges in qualitative methods are closely connected to individual barriers of hard-to-reach populations and the lack of a necessary willingness to share on the one hand and the required ability to share subjective viewpoints on the other hand. By reviewing and discussing Q methodology, this paper contributes to bullying researchers’ methodological repertoire of less-intrusive methodologies. Q methodology offers an approach whereby cultural contexts and local definitions of bullying can be put in the front. Furthermore, developmentally appropriate intervention and prevention programs might be created based on exploratory Q research and could later be validated through large-scale investigations. Generally, research results based on Q methodology are expected to be useful for educators and policymakers aiming to create a safe learning environment for all children. With regard to contemporary bullying researchers, Q methodology may open up novel possibilities through its status as an innovative addition to more mainstream approaches.

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Introduction

Bullying, internationally recognized as a problematic and aggressive form of behavior, has negative effects, not only for those directly involved but for anybody and in particular children in the surrounding environment (Modin, 2012 ). However, one of the major concerns among researchers in the field of bullying is the type of research methods employed in the studies on bullying behavior in schools. The appropriateness of using quantitative or qualitative research methods rests on the assumption of the researcher and the nature of the phenomena under investigation (Hong & Espelage, 2012 ). There is a need for adults to widen their understanding and maintain a focus on children’s behaviors to be able to provide assistance and support in reducing the amount of stress and anxiety resulting from online and offline victimization (Hellström & Lundberg, 2020 ). A crucial step for widening this understanding is an increased visibility of children’s own viewpoints. When the voices of children, particularly those of victims and perpetrators, but also those of bystanders are heard in these matters, effective support can be designed based specifically on what children want and need rather than what adults interpret and understand to be supporting the child (O’Brien, 2019 ). However, bullying victims and their perpetrators are hard-to-reach populations (Shaghaghi et al., 2011 ; Sydor, 2013 ) for a range of reasons. To name but a few, researchers perennially face difficulties regarding potential participants’ self-identification, the sensitivity of bullying topics, or the power imbalance between them and their young respondents. Furthermore, limited verbal literacy and/or a lack of cognitive ability of some respondents due to age or disability contribute to common methodological issues in the field. Nevertheless, and despite ethical restrictions around the immediate questioning of younger children or children with disabilities that prohibit researchers to perform the assessments with them directly, it would be ethically indefensible to not study a sensitive topic like bullying among vulnerable groups of children. Hence, the research community is responsible for developing valid and reliable methods to explore bullying among different groups of children, where the children’s own voices are heard and taken into account (Hellström, 2019 ). Consequently, this paper aims to contribute to bullying researchers’ methodological repertoire with an additional less-intrusive methodology, particularly suitable for research with hard-to-reach populations.

Historically, the field of bullying and cyberbullying has been dominated by quantitative research approaches, most often with the aim to examine prevalence rates. However, recent research has seen an increase in the use of more qualitative and multiple data collection approaches on how children and youth explain actions and reactions in bullying situations (e.g., Acquadro Maran & Begotti, 2021 ; Eriksen & Lyng, 2018 ; Patton et al., 2017 ). This may be translated into a need to more clearly understand the phenomenon in different contexts. As acknowledged by many researchers, bullying is considerably influenced by the context in which it occurs and the field is benefitting from studying the phenomenon in the setting where all the contextual variables are operating (see, e.g., Acquadro Maran & Begotti, 2021 ; Scheithauer et al., 2016 ; Torrance, 2000 ). Cultural differences in attitudes regarding violence as well as perceptions, attitudes, and values regarding bullying are likely to exist and have an impact when bullying is being studied. For this reason, listening to the voices of children and adolescents when investigating the nature of bullying in different cultures is essential (Hellström & Lundberg, 2020 ; Scheithauer et al., 2016 ).

In addition to studying outcomes or products, bullying research has also emphasized the importance of studying processes (Acquadro Maran & Begotti, 2021 ). Here, the use of qualitative methods allows scholars to not only explore perceptions and understandings of bullying and its characteristics, but also interpret bullying in light of a specific social context, presented from a specific internal point of view. In other words, qualitative approaches may offer methods to understand how people make sense of their experiences of the bullying phenomenon. The processes implemented by a qualitative approach allow researchers to build hypotheses and theories in an inductive way (Atieno, 2009 ). Thus, a qualitative approach can enrich quantitative knowledge of the bullying phenomenon, paying attention to the significance that individuals attribute to situations and their own experiences. It can allow the research and clinical community to better project and implement bullying assessment and prevention programs (Hutson, 2018 ).

Instead of placing qualitative and quantitative approaches in opposition, they can both be useful and complementary, depending on the purpose of the research (Acquadro Maran & Begotti, 2021 ). In their review of mixed methods research on bullying and peer victimization in school, Hong and Espelage ( 2012 ) underlined that instead of using single methods, mixed methods have the advantage of generating a deeper and more complex understanding of the phenomenon. By combining objective data with information about the personal context within which the phenomenon occurs, mixed methods can generate new insights and new perspectives to the research field (Hong & Espelage, 2012 ; Kulig et al., 2008 ; Pellegrini & Long, 2002 ). However, Hong and Espelage ( 2012 ) also argued that mixed methods can lead to divergence and contradictions in findings that may serve as a challenge to researchers. For example, Cowie and Olafsson ( 2000 ) examined the impact of a peer support program to reduce bullying using both quantitative and qualitative data collection methods. While a quantitative approach collecting pre-test and post-test data showed no effects in decreasing bullying, interviews with peer supporters, students, and potential users of the intervention revealed the strength of the program and its positive impact, in light of students and peer supporters. Thus, rather than rejecting the program, the divergence in findings leads to a new rationale for modifying the program and addressing its limits.

Understandably, no single data collection approach is complete but deals with methodological issues and concerns affecting the research field and the comprehension of bullying. To provide a robust foundation for the introduction of an additional methodological perspective in bullying research, common data collection methods and methodological issues are outlined below.

Methodological Issues in Bullying Research

Large-scale cohort studies generating statistical findings often use R-statistics, descriptive analyses, averages, and correlations to estimate and compare prevalence rates of bullying, to explore personality traits of bullies and victims, and the main correlates and predictors of the phenomenon. Nevertheless, large-scale surveys have a harder time examining why bullying happens (O’Brian, 2019 ) and usually do not give voice to study objects’ own unique understanding and experiences (Acquadro Maran & Begotti, 2021 ; Bosacki et al., 2006 ; Woodhead & Faulkner, 2008 ). Other concerns using large-scale surveys include whether a definition is used or the term bullying is operationalized, which components are included in the definition, what cut-off points for determining involvement are being used, the lack of reliability information, and the absence of validity studies (Swearer et al., 2010 ).

Other issues include the validity in cross-cultural comparisons using large-scale surveys. For example, prevalence rates across Europe are often established using standard questionnaires that have been translated into appropriate languages. Comparing four large-scale surveys, Smith et al. ( 2016 ) found that when prevalence rates by country are compared across surveys, there are some obvious discrepancies, which suggest a need to examine systematically how these surveys compare in measuring cross-national differences. Low external validity rates between these studies raise concerns about using these cross-national data sets to make judgments about which countries are higher or lower in victim rates. The varying definitions and words used in bullying research may make it difficult to compare findings from studies conducted in different countries and cultures (Griffin & Gross, 2004 ). However, some argue that the problem seems to be more about inconsistency in the type of assessments (e.g., self-report, nominations) used to measure bullying rather than the varying definition of bullying (Jia & Mikami, 2018 ). When using a single-item approach (e.g., “How often have you been bullied?”) it is not possible to investigate the equivalency of the constructs between countries, which is a crucial precondition for any statistically valid comparison between them (Scheithauer et al., 2016 ). Smith et al. ( 2016 ) conclude that revising definitions and how bullying is translated and expressed in different languages and contexts would help examine comparability between countries.

Interviews, focus groups and the use of vignettes (usually with younger children) can all be regarded as suitable when examining youths’ perceptions of the bullying phenomenon (Creswell, 2013 ; Hellström et al., 2015 ; Hutson, 2018 ). They all allow an exploration of the bullying phenomenon within a social context taking into consideration the voices of children and might solve some of the methodological concerns linked to large-scale surveys. However, these data collection methods are also challenged by individual barriers of hard-to-reach populations (Ellard-Gray et al., 2015 ) and may include the lack of a necessary willingness to share on the one hand and the required ability to share subjective viewpoints on the other hand.

Willingness to Share

In contrast to large-scale surveys requiring large samples of respondents with reasonable literacy skills, interviews, which may rely even heavier on students’ verbal skills, are less plentiful in bullying research. This might at least partially be based on a noteworthy expectation of respondents to be willing to share something. It must be remembered that asking students to express their own or others’ experiences of emotionally charged situations, for example concerning bullying, is particularly challenging (Khanolainen & Semenova, 2020 ) and can be perceived as intrusive by respondents who have not had the opportunity to build a rapport with the researchers. This constitutes a reason why research in this important area is difficult and complex to design and perform. Ethnographic studies may be considered less intrusive, as observations offer a data collection technique where respondents are not asked to share any verbal information or personal experiences. However, ethnographical studies are often challenging due to the amount of time, resources, and competence that are required by the researchers involved (Queirós et al., 2017 ). In addition, ethnographical studies are often used for other purposes than asking participants to share their views on certain topics.

Vulnerable populations often try to avoid participating in research about a sensitive topic that is related to their vulnerable status, as recalling and retelling painful experiences might be distressing. The stigma surrounding bullying may affect children’s willingness to share their personal experiences in direct approaches using the word bullying (Greif & Furlong, 2006 ). For this reason, a single-item approach, in which no definition of bullying is provided, allows researchers to ask follow-up questions about perceptions and contexts and enables participants to enrich the discussion by adjusting their answers based on the suggestions and opinions of others (Jacobs et al., 2015 ). Generally, data collection methods with depersonalization and distancing effects have proven effective in research studying sensitive issues such as abuse, trauma, stigma and so on (e.g., Cromer & Freyd, 2009 ; Hughes & Huby, 2002 ). An interesting point raised by Jacobs and colleagues ( 2015 ) is that a direct approach that asks adolescents if they have ever experienced cyberbullying may lead to a poorer discussion and an underestimation of the phenomenon. This is because perceptions and contexts often differ between persons and because adolescents do not perceive all behaviors as cyberbullying. The same can be true for bullying taking place offline (Hellström et al., 2015 ).

When planning research with children, it is important to consider the immediate research context as it might affect what children will talk about (Barker & Weller, 2003 ; Hill, 2006 ; Punch, 2002 ). In addition to more material aspects, such as the room or medium for a dialog, the potential power imbalance created in an interview situation between an adult researcher and the child under study adds to a potentially limited willingness to share. Sitting in front of an adult interviewer may create situations where children may find it difficult to express their feelings and responses may be given based on perceived expectations (Punch, 2002 ). This effect is expected to be even stronger when studying a sensitive topic like bullying. Therefore, respondents may provide more honest responses when they are unaware that the construct of bullying is being assessed (Swearer et al., 2010 ). Moreover, in research about sensitive topics, building a strong connection with participants (Lyon & Carabelli, 2016 ), characterized by mutual trust, is vital and might overcome the initial hesitation to participate and share personal accounts. Graphic vignettes have successfully been used as such unique communication bridges to collect detailed accounts of bullying experiences (Khanolainen & Semenova, 2020 ). However, some reluctance to engage has been reported even in art-based methods, usually known to be effective in research with verbally limited participants (Bagnoli, 2009 ; Vacchelli, 2018 ) or otherwise hard-to-reach populations (Goopy & Kassan, 2019 ). Most commonly, participants might not see themselves as creative or artistic enough (Scherer, 2016 ). In sum, the overarching challenging aspect of art-based methods related to a limited willingness to share personal information is an often-required production of some kind.

Ability to Share

Interviews as a data collection method demand adequate verbal literacy skills for participants to take part and to make their voices heard. This may be challenging especially for younger children or children with different types of disabilities. There is a wide research gap in exploring the voices of younger children (de Leeuw et al., 2020 ) and children with disabilities (Hellström, 2019 ) in bullying research. Students’ conceptualization of bullying behavior changes with age, as there are suggestions that younger students tend to focus more on physical forms of bullying (such as fighting), while older students include a wider variety of behaviors in their view of bullying, such as verbal aggression and social exclusion (Hellström & Lundberg, 2020 ; Monks & Smith, 2006 ; Smith et al., 2002 ; Hellström et al., 2015 ). This suggests that cognitive development may allow older students to conceptualize bullying along a number of dimensions (Monks & Smith, 2006 ). Furthermore, the exclusion of the voices of children with disabilities in bullying research is debated. It is discussed that the symptoms and characteristics of disabilities such as Attention Deficit Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder (ASD), i.e., difficulties understanding the thoughts, emotions, reactions, and behaviors of others, which makes them the ideal target for bullying may also make it hard for them to perceive, verbalize and report bullying and victimization in a reliable and valid manner (Slaughter et al., 2002 ). It may also be difficult for children with ASD to differentiate between playful teasing among friends and hurtful teasing. While many argue that children with ASD are unreliable respondents of victimization, under-reporting using parental and teacher reports has been shown in research on bullying (Waters et al., 2003 ; Bradshaw et al., 2007 ) and child maltreatment (Compier-de Block et al., 2017 ).

This Paper’s Contribution

The present paper contributes to this special issue about qualitative school bullying and cyberbullying research by reviewing and discussing Q methodology as an innovative addition to more mainstream approaches in the field. Despite the fact that Q methodology had been proclaimed as “especially valuable […] in educational psychology” (Stephenson, 1935 , p. 297) nearly 90 years ago, the approach has only relatively recently been described as an up-and-coming methodological choice of educational researchers interested in participants’ subjective views (Lundberg et al., 2020 ). Even though, Q enables researchers to investigate and uncover first-person accounts, characterized by a high level of qualitative detail in its narrative description, only few educational studies have applied Q methodology to investigate the subject of bullying (see Camodeca & Coppola, 2016 ; Ey & Spears, 2020 ; Hellström & Lundberg, 2020 ; Wester & Trepal, 2004 ). Within the wider field of bullying, Q methodology has also been used to investigate workplace bullying in hospitals (Benmore et al., 2018 ) and nursing units (Choi & Lee, 2019 ). By responding to common methodological issues outlined earlier, the potential Q methodology might have for bullying research is exemplified. A particular focus is thereby put on capturing respondents’ subjective viewpoints through its less-intrusive data collection technique. The present paper closes by discussing implications for practice and suggesting future directions for Q methodological bullying and cyberbullying research, in particular with hard-to-reach populations.

An Introduction to Q Methodology

Q as a methodology represents a larger conceptual and philosophical framework, which is by no means novel. However, the methodology has largely been marginalized since its invention in the 1930s by William Stephenson (Brown, 2006 ). As a research technique, it broadly consists of three stages that each can be split into a set of steps (see Fig.  1 ); (1) carefully constructing a data collection instrument, (2) collecting data, and (3) analyzing and interpreting data. The central, and therefore also best-known feature of Q methodology is Q sorting to collect data in the form of individual Q sorts. Participants thereby rank order a sample of self-referent stimuli along a continuum and in accordance with a central condition of instruction; for example, children might be asked to what extent particular scenarios describe bullying situations (Hellström & Lundberg, 2020 ) or they might be instructed to sort illustrated ways to resolve social exclusion according to the single face-valid dimension of “least preferred to most preferred” (de Leeuw et al., 2019 ). As soon as all items are placed on a most often bell-shaped distribution grid (see Fig.  2 ), participants might be asked to elaborate on their item placement to add a further layer of qualitative data. Such so-called post-sorting activities might include written annotations of items placed at the ends of the continuum or form the structure for interviews (Shemmings & Ellingsen, 2012 ).

Three stages and six steps of a Q methodological research process (adapted from Lundberg et al., 2020 )

A vertical distribution grid with two examples of face-valid dimensions. This rather small distribution is designed for a 16-item Q sample and therefore contains 16 slots to be filled

For participants to provide their subjective viewpoint toward a specific topic in the form of a Q sort, researchers need to construct the data collection instrument, called Q sample. Such a set of stimulus items is a representative sample from all possible items concerning the topic, which in the technical language in Q methodology is called concourse (Brown, 1980 ). The development of such a concourse about the topic at hand might stem from a wide range of sources, including academic literature, policy documents, informal discussions, or media (Watts & Stenner, 2012 ). Moreover, in a participatory research fashion, participants’ statements can be used verbatim to populate the concourse. This way, children’s own words and voices are part of the data collection instrument. A sophisticated structuring process then guides the researchers in selecting a Q sample from all initial statements in the concourse (Brown et al., 2019 ). In Hellström & Lundberg ( 2020 ), a literature review on findings and definitions of bullying, stemming from qualitative and quantitative research, provided the initial concourse. A matrix consisting of different modes, types, and contexts of bullying supported the construction of the final Q sample.

As a student and assistant of Charles Spearman, Q’s inventor Stephenson was well-informed about R-methodological factor analysis based on correlating traits. The British physicist-psychologist however inverted the procedure and thereby suggested correlating persons to study human behavior (Stephenson, 1935 , 1953 ). A detailed description of the statistical procedure of Q factor analysis is outside the scope of this article, especially as the focus of this special issue is put on qualitative research methods. In addition, with its focus on producing quantifiable data from highly subjective viewpoints (Duncan & Owens, 2011 ), it is safe to say that Q methodology is more often treated as a qualitative methodology with quantitative features than the other way around. Nevertheless, it is important to note that through factor analysis, individual viewpoints are clustered into so-called factors, representing shared viewpoints if they sufficiently correlate (see Fig.  3 ). In that sense, no outside criterion is applied to respondents’ subjective views and groups of similar sorts (factors/viewpoints) are not logically constructed by researchers. Instead, they inductively emerge through quantitative analysis, which helps “in learning how the subject, not the observer, understands and reacts to items” (Brown, 1980 , p. 191). This procedure allowed Hellström & Lundberg ( 2020 ) to describe two age-related definitions of bullying. Older students in particular perceived offline bullying as more severe than online bullying and their younger peers were mostly concerned about bullying situations taking place in a private setting.

A simplified illustration of Q factor analysis (step 5). Arrow A represents the statistical correlation of all collected individual viewpoints. Arrow B represents inverted factor analysis as the data condensation technique resulting in a manageable number of shared viewpoints

Despite its quantitative analysis, participant selection in Q methodology is largely in line with purposive sampling with small numbers. It, therefore, represents a major difference to R methodological research, where larger opportunity samples are desired. In Q methodology, participants are selected strategically in line with those who might likely “express a particularly interesting or pivotal point of view” (Watts & Stenner, 2012 , p. 71). Investigating a large number of similar respondents might therefore simply lead to more participants correlating with the same shared viewpoint and not necessarily add new viewpoints. In recent educational Q research, the average number of participants is 37 (Lundberg et al., 2020 ). Many studies have however been successfully conducted with considerably fewer, as for example illustrated by Benmore et al. ( 2018 ), who described three distinctive groups within their sample of 12 participants.

To illustrate Q methodology in bullying research, our small scale and exploratory study published in Educational Research (Hellström & Lundberg, 2020 ) serves as a practical example. The purpose of that study was to investigate definitions of bullying from young people’s perspectives and was guided by the following research question: What are students’ subjective viewpoints on bullying behavior? . In Table 1 , we describe the methodological steps introduced in Fig.  1 .

Q Methodology’s Response to the Methodological Issues Outlined Above

Above, methodological issues have been structured according to participants’ willingness and ability to share their subjective viewpoints and lived experiences. In order to respond to those, the present section focuses on Q methodology’s built-in features. A particularly important component is Q sorting as the central data collection technique that facilitates participants’ communicability of their subjectivity.

Engaging participants in a card sorting activity encourages students to express their viewpoints and thereby making their voices heard in a less-intrusive way, despite being cognitively engaging. Because they are asked to rank-order a predetermined sample of items, ideally in accordance with a carefully selected condition of instruction, they do not need to report or disclose their own personal experiences and are not obliged to actively create anything, as criticized in arts-based research. In that sense, Q methodology can be seen as a method to collect sensitive data in a more depersonalized way. This provides the basis to find a vital “balance between protecting the child and at the same time allowing access to important information” (Thorsen & Størksen, 2010 , p. 9), which is of particular importance for research about emotionally charged situations or sensitive topics as it is often the case with bullying (Ellingsen et al., 2014 ). Sharing their view through a fixed collection of items certainly makes participation in research for young children or otherwise hard-to-reach respondents less intimidating and results can be expected to be more truthful.

In comparison to researchers applying ethnographical approaches, who immerse themselves into the studied context to understand and document patterns of social behavior and interaction in a less intrusive way, Q methodologists are not expected to observe their participants. Even though the purpose of these approaches is different, being part of the culture under investigation or at least involving community partners in Q methodological research can still be useful for at least two reasons. As mentioned in Table 1 featuring the study by Hellström & Lundberg ( 2020 ), the pupils’ physical education and health teacher guided an exploratory and informal discussion and thereby provided valuable insights into the participants’ lifeworld that informed the Q sample. In addition to better tailoring the sample to the participants and making them feel seen and heard, the community partner could help build a positive rapport between participants and researchers, which otherwise requires much work. During the actual data collection exercise, participants were already familiar with the topic, well-informed about the research project, and perceived the sorting activity as an integral part of their lesson.

The play-like character of Q sorting has as well been reported as a positive influence on respondents’ motivation to participate (de Leeuw et al., 2019 ) and Wright ( 2013 ) mentions the engaging atmosphere created between the sorter and the researcher. The combination of these features allows assuming that obtaining participants’ viewpoint through Q methodology is less threatening than for example sitting in front of an interviewer and providing on-spot oral responses about a sensitive topic.

Q sorting as a data collection instrument represents a major advantage for Q methodological research with participants that do not (yet) possess sufficient verbal literacy and/or cognitive ability to process receptive or expressive language. To illustrate, two features are outlined here: first the flexibility of the Q sample, say the set of stimuli and second the fact that primary data collection in Q methodology is based on a silent activity.

Written statements are undoubtedly the most common type of items used in Q methodology and the number of such in a Q sample greatly varies. In recent research reporting from compulsory education settings, the average Q sample consists of about 40 items (Lundberg et al., 2020 ). In addition to applying a smaller set of items, their complexity can easily be adapted in line with participants’ receptive literacy skills and their developmental stage to facilitate understanding. Statements can for example be shortened or they can start identically to make the activity less taxing (Watts & Stenner, 2012 ). A different approach to cater to limited verbal literacy is the use of images instead of written statements. Constructing a visual Q sample might be more challenging for the researcher, in particular, if images are carefully selected and culturally tailored, meaning that they are clear, appealing and without too many details (Thorsen & Størksen, 2010 ). It might nevertheless be worth it, as such items provide a powerful tool to elicit viewpoints from otherwise marginalized or hard-to-reach research participants. Combes and colleagues ( 2004 ) for example, created a 37-item-Q sample with intellectually disabled participants’ own pictures to evaluate the planning of activities and de Leeuw et al. ( 2019 ) have used 15 images of hypothetical scenarios of social exclusion in a study with primary school pupils. Furthermore, as illustrated by Allgood and Svennungsen ( 2008 ) who photographed their participant’s own sculptures, Q samples consisting of objects (e.g., toys) or symbols (emojis) might be other options to investigate issues about bullying and cyberbullying without using text.

In addition to adaptations to the data collection instrument, the sorting process is usually carefully introduced and illustrated. Researchers might want to go through the entire Q sample to ensure the participants are able to discriminate each item (Combes et al., 2004 ). Even with adult participants without any cognitive impairments, it is suggested to pre-sort items into three provisional categories (Watts & Stenner, 2012 ). Two categories represent the respective ends of the continuum in the distribution grid and might be labeled and. Any items the sorter feels insecure or neutral about, are moved to the third category, which receives a question mark (?) for the sake of this exercise. During the actual rank-ordering process, the participants start to allocate items to one of the ends of the continuum (the top of the distribution grid in Fig.  2 ) with cards from the ☺ category and work themselves toward the center of the distribution grid. The process continues with items in the ☹ category, which are placed from the opposite end of the continuum toward the center. Any free spots are then filled with the remaining items in the (?) category. The graphic display of their viewpoint has been experienced as enabling for self-reflection (Combes et al., 2004 ) and might be utilized for a further discussion about the topic, for example as part of teacher workshops (Ey & Spears, 2020 ).

Meeting children at an appropriate cognitive level through adaptations of the data collection instrument and procedure, is not only a promising and important ethical decision in order to show young participants the respect they deserve (Thorsen & Størsken, 2010 ), but makes the sorting procedure a pleasant experience for the participants (John et al., 2014 ). Unsurprisingly, Q methodology has been described as a respectful, person-centered, and therefore child-friendly approach (Hughes, 2016 ).

Limitations

Despite its potential for bullying research, Q methodology has its limitations. The approach is still relatively unknown in the field of bullying research and academic editors’ and reviewers’ limited familiarity with it can make publishing Q methodological research challenging. Notwithstanding the limitation of not being based on a worked example, the contribution of the present paper hopefully fulfills some of the needed spadework toward greater acceptability within and beyond a field, which has only seen a limited number of Q methodological research studies. Because the careful construction of a well-balanced Q sample is time-consuming and prevents spontaneous research activities, a core set of items could be created to shorten the research process and support the investigation of what bullying means to particular groups of people. Such a Q sample would then have to be culturally tailored to fit local characteristics. Finally, the present paper is limited in our non-comprehensive selection of data collection methods as points of comparison when arguing for a more intensive focus on Q methodology for bullying research.

Future Research Directions

The results of Q methodological studies based on culturally tailored core Q samples would allow the emergence of local definitions connected to the needs of the immediate society or school context. As illustrated by Hellström & Lundberg ( 2020 ), even within the same school context, and with the same data collection instrument (Q sample), Q methodology yielded different, age-related definitions of bullying. Or in Wester and Trepal ( 2004 ), Q methodological analysis revealed more perceptions and opinions about bullying than researchers usually mention. Hence, Q methodology offers a robust and strategic approach that can foreground cultural contexts and local definitions of bullying. If desired, exploratory small-scale Q research might later be validated through large-scale investigations. A further direction for future research in the field of bullying research is connected to the great potential of visual Q samples to further minimize research participation restrictions for respondents with limited verbal or cognitive abilities.

Implications for Practice

When designing future bullying prevention strategies, Q methodology presents a range of benefits to take into consideration. The approach offers a robust way to collect viewpoints about bullying without asking participants to report their own experiences. The highly flexible sorting activity further represents a method to investigate bullying among groups that are underrepresented in bullying research, such as preschool children (Camodeca & Coppola, 2016 ). This is of great importance, as tackling bullying at an early age can prevent its escalation (Alsaker & Valkanover, 2001 ; Storey & Slaby, 2013 ). Making the voices of the hard-to-reach heard in an unrestricted way and doing research with them instead of about them (de Leeuw et al., 2019 ; Goopy & Kassan, 2019 ) is expected to enable them to be part of discussions about their own well-being. By incorporating social media platforms, computer games, or other contextually important activities when designing a Q sample, the sorting of statements in Hellström & Lundberg, ( 2020 ) turned into a highly relevant activity, clearly connected to the reality of the students. As a consequence, resulting policy creation processes based on such exploratory studies should lead to more effective interventions and bullying prevention programs confirming the conclusion by Ey and Spears ( 2020 ) that Q methodology served as a great model to develop and implement context-specific programs. Due to the enhanced accountability and involvement of children’s own voices, we foresee a considerable increase in implementation and success rates of such programs. Moreover, Q methodology has been suggested as an effective technique to evaluate expensive anti-bullying interventions (Benmore et al., 2018 ). Generally, research results based on exploratory Q methodology that quantitatively condensates rich data and makes commonalities and diversities among participants emerge through inverted factor analysis are expected to be useful for educators and policymakers aiming to create a safe learning environment for all children. At the same time, Q methodology does not only provide an excellent ground for participatory research, but is also highly cost-efficient due to its status as a small-sample approach. This might be particularly attractive, when neither time nor resources for other less-intrusive methodological approaches, such as for example ethnography, are available. Due to its highly engaging aspect and great potential for critical personal reflection, Q sorting might be applied in classes regardless of representing a part of a research study or simply as a learning tool (Duncan & Owens, 2011 ). Emerging discussions are expected to facilitate and mediate crucial dialogs and lead toward collective problem-solving among children.

The use of many different terminologies and different cultural understandings, including meaning, comprehension, and operationalization, indicates that bullying is a concept that is difficult to define and subject to cultural influences. For the purpose of designing relevant and powerful bullying prevention strategies, this paper argues that instead of pursuing a universal definition of what constitutes bullying, it may be of greater importance to investigate culturally and contextually bound understandings and definitions of bullying. Although the quest for cultural and contextual bound definitions is not new in bullying research, this paper offers an additional method, Q methodology, to capture participants’ subjective views and voices. Since particularly the marginalized and vulnerable participants, for example, bullying victims, are usually hard to reach, bullying researchers might benefit from a methodological repertoire enriched with a robust approach that is consistent with changes in methodological and epistemological thinking in the field. In this paper, we have argued that built-in features of Q methodology respond to perennial challenges in bullying research connected to a lack of willingness and limited ability to share among participants as well as studying bullying as a culturally sensitive topic. In summary, we showcased how Q methodology allows a thorough and less-intrusive investigation of what children perceive to be bullying and believe that Q methodology may open up novel possibilities for contemporary bullying researchers through its status as an innovative addition to more mainstream approaches.

Availability of Data and Material

Not applicable.

Code Availability

Change history, 18 july 2022.

A Correction to this paper has been published: https://doi.org/10.1007/s42380-022-00135-9

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Lundberg, A., Hellström, L. Q Methodology as an Innovative Addition to Bullying Researchers’ Methodological Repertoire. Int Journal of Bullying Prevention 4 , 209–219 (2022). https://doi.org/10.1007/s42380-022-00127-9

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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.

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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).

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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

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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.

Reviewed by:

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]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Effects of Verbal Bullying to High School Students

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headspace position papers are for general information only. They are not intended to be and should not be relied on as a substitute for specific medical or health advice. They are not intended to be and should not be relied on as clinical guidelines. While every effort is taken to ensure the information is accurate, headspace makes no representations and gives no warranties that this information is correct, current, complete, reliable or suitable for any purpose. We disclaim all responsibility and liability for any direct or indirect loss, damage, cost or expense whatsoever in the use of or reliance upon this information.

Encyclopedia of Social Work Online

Jonathan B Singer

Bullying is the most common form of violence in schools and has been shown to disrupt the emotional and social development of both the targets and the perpetrators of bullying (Raskauskas & Stoltz, 2007). Bullying can be physical, verbal, relational, and direct or indirect. There are well-established age and sex trends (Olweus, 1993; Smith, Madsen, & Moody, 1999). There has been considerable research on bullying-prevention programs and scholarship on best-practice guidelines for school social workers (Dupper, 2013). An emerging concern is with the use of electronic and Internet devices in bullying, referred to as “cyberbullying.” In this article we define bullying and cyberbullying; discuss risk factors associated with being a bully, a victim, and a bully-victim; describe prevention and intervention programs; and discuss emerging trends in both bullying and cyberbullying.

Luzia Pinheiro

Betie febriana

Introduction:Cyberbullying is a new form of bullying. It become a trend since the technology grows more and more. This is very different with traditional bullying because it can be done in anywhere and anytime specially in private area. This literature review try to summarize some researches with cyberbullying and traditional bullying.Method:The method is collect and analyze the article of cyberbullying and traditional bullying. Articles collected through electronic databases Springer, proquest, science direct and using the keyword cyberbullying, traditional bullying, nursing. Criteria of the articles is full text and published in the period 2007-2013. Result:Most studies has explained very well the difference between cyberbullying and traditional bullying. Descriptive quantitative approach became the choice of most researchers who are considered able to explain the phenomenon well. But this is too narrow and restrict researchers. Secondly, they are more explaining bullying in educa...

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Bullying is a pervasive problem affecting school-age children. Reviewing the latest findings on bullying perpetration and victimization, we highlight the social dominance function of bullying; the inflated self-views of bullies, and the effects of their behaviors on victims. Illuminating the plight of the victim, we review evidence on the cyclical processes between the risk factors and consequences of victimization and the mechanisms that can account for elevated emotional distress and health problems. Placing bullying in context, we consider the unique features of electronic communication that give rise to cyber bullying and the specific characteristics of schools that affect the rates and consequences of victimization. We then offer a critique of the main intervention approaches designed to reduce school bullying and its harmful effects. Finally, we discuss future directions that underscore the need to consider victimization a social stigma, conduct longitudinal research on protective factors, identify school context factors that shape the experience of victimization, and take a more nuanced approach to school-based interventions.

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Bullying is defined as a systematic abuse ofpower; the development of the research program on school bullying is outlined over four phases. The distinctive nature of cyberbullying, and also of identity-based bullying, is outlined. Measurement methods are discussed, and the kinds of prevalence rates obtained. Arange of risk factors for involvement as a bully, or victim, are ummarized. Arange of school-based interventions are described, together with discussion of a meta-analysis of their outcomes. In summary, research and practice have gone hand-in-hand in the researchreviewed, and have had some success in at least reducing what is a significant problem in the lives of many children.

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The Effects of Verbal Bullying in Grade 12 ABM Students in Bestlink College of the Philippines School Year 2018-2019

• Jurito G. Cabacang
• Aljay Valenzuela
• Sarah Jane Robregado
• Florante Gordora
• Kristelle Sablayan
• Rocelyn P. Catibag

Verbal bullying is one of the popular forms of bullying. Verbal bullying represents much form. Embarrass word directed at a person, name calling, insults, teasing, intimidation or making racist mark. Academic achievement is the first aspect which influences bullying at school. therefore, who bullied live within fear, self-blame, feel weak and it affects their personality traits and self-confidence, so this situation makes them unable to study well and they might hate going to school. Furthermore, they will lose their opportunities to participate with others or enjoy school activities. Hence, they will gain less academic performance and low educational attainment. There is a strong relationship between bullying and school quality such as class size, lack of library, sports facilities. Both bullies and victims feel more negative about school, and persistent bullying may lead to stress and depression. Bullying can lead to anxiety, low self-esteem, hopelessness and isolation. The researchers used survey questionnaire to gather information from the selected Grade 12 ABM students. This research questionnaire will help to gather ideas or information easily. Personal observation and results gathered from the survey questionnaire. The research locate of this study is inside of the Bestlink College of the Philippines. The data gathered from the questionnaire serve as the primary data and data gathered from the literature and studies are considered secondary data. This study aimed to investigate the impact of verbal bullying on student’s academic performance from their perspective. The bullying exist in all communities as well as schools whether government or private. They are not recorded but felt by some and this might affect the victim or the bullies too. School bullying creates negative environment in the school. The study conducted gathered result that verbal bullying greatly affects the academic performance of the students. Though it may not harm the victim physically but the emotionally. The research found that verbal bullying affects academic performance either the victims who suffer from these phenomena and in the same time it affects the bullies themselves. The research suggested that teachers and the school management have to take different measures for the purpose of preventing this thing to happen.

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• About Youth Violence
• Risk and Protective Factors
• School-Associated Violent Death Study
• Youth Violence Prevention Centers

About Bullying

• Bullying is a form of youth violence and an adverse childhood experience (ACE).
• Bullying is widespread in the U.S., but bullying is preventable.

What is bullying?

CDC defines bullying as any unwanted aggressive behavior(s) by another youth or group of youths, who are not siblings or current dating partners, that involves an observed or perceived power imbalance, and is repeated multiple times or is highly likely to be repeated. Bullying may inflict harm or distress on the targeted youth including physical, psychological, social, or educational harm. 1 Common types of bullying include:

• Physical such as hitting, kicking, and tripping.
• Verbal including name-calling and teasing.
• Relational or social such as spreading rumors and leaving out of the group.
• Damage to victim's property.

Bullying can also occur through technology, which is called electronic bullying or cyberbullying. 1 A young person can be a perpetrator, a victim, or both (also known as "bully/victim").

For more information about bullying definitions, please see Bullying Surveillance Among Youths: Uniform Definitions for Public Health and Recommended Data Elements, Version 1 .

Quick facts and stats

Bullying is widespread in the United States. Bullying negatively impacts all youth involved including those who are bullied, those who bully others, and those who witness bullying, known as bystanders.

• Bullying is common . About 1 in 5 high school students reported being bullied on school property. More than 1 in 6 high school students reported being bullied electronically in the last year. 2
• Some youth experience bullying more than others . Nearly 40% of high school students who identify as lesbian, gay, or bisexual and about 33% of those who were not sure of their sexual identity experienced bullying at school or electronically in the last year, compared to 22% of heterosexual high school students. About 30% of female high school students experienced bullying at school or electronically in the last year, compared to about 19% of males. Nearly 29% of white high school students experienced bullying at school or electronically in the last year compared to about 19% of Hispanic and 18% of Black high school students. 2
• Reports of bullying are highest in middle schools (28%) followed by high schools (16%), combined schools (12%), and primary schools (9%).
• Reports of cyberbullying are highest in middle schools (33%) followed by high schools (30%), combined schools (20%), and primary schools (5%). 3

Bullying can result in physical injury, social and emotional distress, self-harm, and even death. It also increases the risk for depression, anxiety, sleep difficulties, lower academic achievement, and dropping out of school. Youth who bully others are at increased risk for substance misuse, academic problems, and experiencing violence later in adolescence and adulthood. 4 Youth who bully others and are bullied themselves suffer the most serious consequences and are at greater risk for mental health and behavioral problems.

Bullying is preventable. There are many factors that may increase or decrease the risk for perpetrating or experiencing bullying. To prevent bullying, we must understand and address the factors that put people at risk for or protect them from violence . CDC developed, Youth Violence Prevention Resource for Action , to help communities take advantage of the best available evidence to prevent youth violence. 5 This resource is also available in Spanish and can be used as a tool in efforts to impact individual behaviors as well as the relationship, family, school, community, and societal risk and protective factors for violence. The approaches in this resource, particularly universal school-based programs that strengthen youths' skills and modify the physical and social environment, have been shown to reduce violence and bullying or key risk factors.

Different types of violence are connected and often share root causes. Bullying is linked to other forms of violence through shared risk and protective factors. Addressing and preventing one form of violence may have an impact on preventing other forms of violence.

• Gladden RM, Vivolo-Kantor AM, Hamburger ME, Lumpkin CD. Bullying surveillance among youths: Uniform definitions for public health and recommended data elements, Version 1.0. Atlanta, GA; National Center for Injury Prevention and Control, Centers for Disease Control and Prevention and U.S. Department of Education; 2013. Available from https://www.cdc.gov/violenceprevention/pdf/bullying-definitionsfinal-a.pdf.
• Centers for Disease Control and Prevention. Youth risk behavior surveillance—United States, 2019. Morbidity and Mortality Weekly Report–Surveillance Summaries 2020; 69(SS1). Available from https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2019/su6901-H.pdf
• Diliberti, M., Jackson, M., Correa, S., and Padgett, Z. (2019). Crime, Violence, Discipline, and Safety in U.S. Public Schools: Findings From the School Survey on Crime and Safety: 2017–18 (NCES 2019-061). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved from http://nces.ed.gov/pubsearch
• Farrington D, Baldry A. Individual risk factors for school bullying. Journal of Aggression, Conflict and Peace Research 2010; 2(1):4-16. Available from https://doi.org/10.5042/jacpr.2010.0001.
• David-Ferdon, C., Vivolo-Kantor, A. M., Dahlberg, L. L., Marshall, K. J., Rainford, N. & Hall, J. E. (2016). Youth Violence Prevention Resource for Action: A Compilation of the Best Available Evidence. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Note: The title of this document was changed in July 2023 to align with other Prevention Resources being developed by CDC's Injury Center. The document was previously cited as "A Comprehensive Technical Package for the Prevention of Youth Violence and Associated Risk Behaviors."

Youth Violence Prevention

Youth violence affects thousands of young people each day, and in turn, their families, schools, and communities. CDC works to understand the problem of violence experienced by youth and prevent it.

For Everyone

Public health.