Peer Victimization in Children With ADHD: A Community-Based Longitudinal Study

Abstract

Objectives: To investigate (a) whether children with ADHD experience higher levels of overt and relational peer victimization (PV) than children without ADHD and (b) child, family, and school predictors of PV in children with ADHD. Method: 173 children with ADHD and 199 non-ADHD controls were recruited through 43 Melbourne schools at mean age 7.3 years. Parent- and teacher-reported PV (Social Experience Questionnaire) data were collected at mean age 8.9 years. Potential child, family, and school predictors of PV were measured at baseline. Results: Children with ADHD experienced higher levels of PV than children without ADHD by both parent- and teacher-report. Child predictors accounted for the greatest variance in PV, and the strongest predictors of PV were teacher-reported conduct problems, and medication use. Conclusion: Children with ADHD are at higher risk of PV compared with non-ADHD controls. Recognizing and addressing PV is important to reduce additional impairment in children with ADHD.

Keywords

ADHD peer victimization

What’s Known on This Subject

Peer victimization (PV) in childhood is associated with long-term mental health problems.

Children with ADHD experience a twofold to sevenfold increased rate of PV compared with controls.

What This Study Adds

Both overt and relational PV were more prevalent in children with ADHD than controls.

Comorbid conduct problems and use of medication were the strongest predictors of PV in this community sample of children with ADHD.

Introduction

Peer victimization (PV) is defined as overt (hitting, insulting) and/or relational (shunning, ignoring) attacks repeated over time (Olweus, 1995). The prevalence of PV among primary school students is estimated to be over 20% (Glew, Fan, Katon, Rivara, & Kernic, 2005). PV is linked to poor long-term outcomes such as psychiatric disorders and low self-worth (McDougall & Vaillancourt, 2015).

ADHD is a common neurodevelopmental disorder often associated with impairments in peer relationships (Hinshaw & Melnick, 1995). Children with ADHD are often noisy and rule violating (Hodgens, Cole, & Boldizar, 2000), which may elicit PV as they are seen as different. In addition, externalizing and internalizing comorbidities often accompany ADHD (Steinhausen & Nøvik, 2006), so these children may also stand out as targets due to overt signs of these conditions (Humphrey, Storch, & Geffken, 2007). It is important to identify whether children with ADHD are at a greater risk of PV than their typically developing peers as this may exacerbate the emotional, social, and academic difficulties already experienced by these children, and may present a target for prevention or early intervention (Card & Hodges, 2008).

Previous studies have found that children with ADHD experience a twofold to sevenfold increased rate of PV compared with controls (Epstein-Ngo et al., 2015; Holmberg & Hjern, 2008; Sciberras, Ohan, & Anderson, 2012; Timmermanis & Wiener, 2011; Unnever & Cornell, 2003; Wiener & Mak, 2009). However, few studies have examined the different types of PV experienced by children with ADHD (Wiener & Mak, 2009; Holmberg & Hjern, 2008; Unnever & Cornell, 2003), or the age at which PV emerges in ADHD. Furthermore, little is known about the predictors of PV in children with ADHD. Previous studies have examined child predictors of PV in isolation and have produced mixed findings. Some studies have reported that girls with ADHD may be at increased risk of PV compared with boys (Bacchini, Affuso, & Trotta, 2008; Novik et al., 2006; Unnever & Cornell, 2003). ADHD symptom severity has been found to be a predictor of PV in some studies (Bagwell, Molina, Pelham, & Hoza, 2001; Hoza, 2007; Kawabata, Tseng, & Gau, 2012; Wiener & Mak, 2009), whereas others have found that coexisting internalizing or externalizing comorbidities play a major role (Fite, Evans, Cooley, & Rubens, 2014; Mayes, Calhoun, Baweja, & Mahr, 2015). Although children with ADHD are known to have an increased risk of academic and language impairments, no study has investigated links between these problems and PV in the ADHD population.

Research on family- and school-level predictors of PV in children with ADHD is even more sparse. Potential parent predictors of PV such as mental health problems, self-efficacy, and educational level have yet to be examined. Although research from the general population suggests that preschool climate and student–teacher relationships are important factors in PV (Ladd, Birch, & Buhs, 1999), these factors have not been examined in children with ADHD.

We aimed to address these gaps by examining PV in a community-based cohort of children aged 6 to 8 years with and without ADHD. Specifically, we examined the following:

Aim 1: Differences in parent- and teacher-reported overt and relational PV between children with and without ADHD.

Aim 2: A range of child, family, and school predictors of parent- and teacher-reported PV in children with ADHD.

We hypothesized that children with ADHD would have higher rates of both parent- and teacher-reported PV than controls, and that academic underperformance, and comorbid conduct problems and maternal mental health problems would be the strongest predictors of PV in children with ADHD.

Method

Design and Setting

This study used baseline and 18-month data from the Children’s Attention Project, a community-based longitudinal study of ADHD (Sciberras et al., 2013). Approval was obtained from ethics committees of the Royal Children’s Hospital (#31056) and the Victorian Department of Education and Early Childhood Development (#2011_001095). Parents provided consent for participation in each stage of the study.

Eligibility and Screening

Participants were Grade 1 children recruited from 43 government schools selected for representation of diverse socioeconomic communities in metropolitan Melbourne, Victoria, Australia, across two consecutive years (2011-2012). Children with ADHD and non-ADHD controls were on average 7.3 years of age at baseline and 8.9 years of age at the 18-month follow-up. Children with intellectual disability, severe medical conditions, genetic disorders, moderate–severe sensory impairment, or neurological problems were excluded, as were non-English speaking families.

A two-stage screening and case-confirmation procedure was conducted to ascertain the sample. Screening was conducted using parent and teacher Conners 3 ADHD Index surveys (Conners, 2008). Children were classified as screening positive if their scores on both the parent and teacher ADHD indices were >75th percentile for age for boys and >80th percentile for girls, or if they have been diagnosed with ADHD. Children screening positive were randomly matched on gender and school to children screening negative on both the parent and teacher ADHD indices. Both groups were then invited into the longitudinal study, which involved ADHD case confirmation (Diagnostic Interview Schedule for Children [DISC-IV], diagnostic interviews with parents; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000), and detailed parent and teacher surveys. Assessments were completed by research staff with at least a 4-year undergraduate degree in psychology who were blinded to child screening status. Families participating in the longitudinal study were followed up 18 months later (Figure 1).

Figure 1.

Participant flow

Measures

Outcome measure

Overt and relational victimization (outcome measure at 8.9 years) was measured using parent and teacher reports on the Social Experience Questionnaire (SEQ; Storch, Crisp, Roberti, Bagner, & Masia-Warner, 2005). Overt (three items) and relational victimization (three items) scales were used, with items rated on a 5-point Likert-type scale from never true to always true. These are averaged, giving a victimization score range of 1 to 5. Questions included being hit or kicked by peers, and being ignored by peers when they are mad at him or her. The SEQ has very good to excellent internal consistency for overt (a = 0.93) and relational victimization (a = 0.82) scales (Storch et al., 2005), and high internal consistency for both overt (parent a = 0.89, teacher a = 0.85, respectively) and relational victimization (parent a = 0.81, teacher a = 0.84, respectively) scales.

Predictor measures

A number of child, family, and school predictors (measured at baseline) were examined, as outlined in Table 1. All measures were well validated and selected because of theoretically potential links with PV and/or findings from previous studies.

Table 1.

Measures of Child, Family, and School Predictors at Baseline (Age 7.3 Years).

Construct	Measure and description	Source
Child predictors
Sex	Child sex.	P
Symptom severity	Conners 3 ADHD Index—10-item validated scale assessing fidgeting, inattention, not listening to instructions, distraction, difficulty organizing, restlessness, and interrupting others. Items rated on a 4-point Likert-type scale from “not true at all” to “very much true” (Conners, 2008).	T, P
Academic competence	Academic Competence (SSIS)—seven-item validated scale assessing overall academic performance, motivation, reading, and mathematical ability of the student in comparison with other students in the classroom (Lereya, Samara, & Wolke, 2013).	T
IQ	The WASI—Vocabulary (verbal ability) and Matrix Reasoning (non-verbal ability) were administered. An estimated Full Scale Intelligent Quotient standard score (M = 100, SD = 15) was generated from the two subtests.	C
Language problems	The CELF-4 Screening Test—criterion-referenced test to identify children who may be experiencing language difficulties, using the most discriminating items from the CELF-4. CELF-4 above or below criterion (yes or no) was used.	C
Peer, conduct, emotional problems	SDQ—25-item validated measure of behavioral and emotional problems for children aged 4 to 16 years assessing peer problems, conduct problems, and emotional problems. Items were rated on a 3-point Likert-type scale from never to certainly true.	T
Self-control	Self-Control (SSIS)—seven-item validated scale assessing self-control. Items were rated on a 4-point Likert-type scale from never to almost always.	T
Medication use	Study designed. Primary caregiver report of whether child has taken medication to assist with learning, behavior, or emotional difficulties (yes or no). If yes, specification of methylphenidate, dexamphetamine, and atomoxetine (Sciberras et al., 2013).	P
Family predictors
Mental health	K6—six-item validated measure of non-specific psychological distress. Items are rated on a 5-point scale from none of the time to all of the time.	P
Self-efficacy	LSAC self-efficacy scale—perceived efficacy as a parent was assessed using one item on a 5-point scale from not very good at being a parent to a very good parent.	P
Parenting		P
Parental warmth	LSAC parenting warmth scale—assesses closeness such as hugged/held child, tell child they make you happy, expressing affection using six items on a 5-point scale from never/almost never to always/almost always.	P
Consistent parenting	LSAC consistent parenting scale—assesses frequency of child punishment or lack thereof using five items on a 5-point scale from never/almost never to always/almost always.
Angry parenting	LSAC angry parenting scale—assesses frequency of difficulty managing child such as setting punishment based on parent’s mood, telling child they are not as good as others on using six items on a 5-point scale from never/almost never to always/almost always.
Education	Study designed. Primary caregiver report of whether they have achieved a university degree (yes or no; Conners, 2008).
School predictors
Student–teacher relationship	STRS—a 15-item validated measure assessing teachers’ perception of his or her relationship with a particular student in terms of conflict and closeness. The measure is rated on a 5-point Likert-type scale from definitely does not apply to definitely applies. Total raw score was used.	T
School climate	Study designed. A six-item self-report measure assessing teacher’s perception of being able to rely on colleagues for support and assistance, understanding of their roles, contribution to school policies and practice, and positivity of school environment. The measure is rated on a 5-point Likert-type scale from strongly disagree to strongly agree. Total raw score was used (α = 0.84).	T

Note. P = parent report; T = teacher report; C = child report; SSIS = Social Skills Improvement System; WASI = Wechsler Abbreviated Scale of Intelligence; CELF-4 = Clinical Evaluation of Language Fundamentals–Fourth Edition; SDQ = Strengths and Difficulties Questionnaire; K6 = Kessler 6 scale; LSAC = Longitudinal Study of Australian Children; STRS = Student–Teacher Relationship Scale.

Sociodemographic factors included child age and single-parent family status. Socioeconomic status was measured using the census-based Socio-Economic Indexes for Areas (SEIFA) disadvantage index (Australian Bureau of Statistics, 2013) for the family’s postcode.

Comorbidities were identified using the DISC-IV (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). Children were defined as having an externalizing disorder and an internalizing disorder if they had one or more of the following: separation anxiety disorder, social phobia, generalized anxiety disorder, posttraumatic stress disorder, obsessive compulsive disorder, major depression, dysthymia, hypomania, or manic episode.

Statistical analysis

Subjects with either parent or teacher victimization data available at 18-month follow-up were included in the analyses (372 out of 391). The t tests and chi-square tests were used to compare the demographic characteristics of children with and without ADHD for continuous and categorical variables respectively.

For Aim 1, unadjusted linear regression was used to compare parent- and teacher-reported PV between children with and without ADHD. Cohen’s d was used as a measure of effect size, with effect sizes 0.2 considered small, 0.3 to 0.7 moderate, and >0.7 large (Cohen, 1988).

For Aim 2, univariate relationships between predictors and parent- and teacher-reported overt and relational PV in children with ADHD only were first examined using a series of pair-wise correlations. Predictors associated with victimization at an alpha level of <0.1 in univariate analyses were then included in two stepwise hierarchical linear regression models, one for parent- and another for teacher-reported victimization. Given that previous studies have indicated that child factors are important predictors of PV, we included child factors first, followed by family and school factors to calculate how much additional variance these factors explained. Hence, Step 1 analyzed child predictors only, Step 2 child and family predictors, and Step 3 child, family, and school predictors. Given the moderate to high correlation between overt and relational victimization (parent report: r = 0.60, p < .001; teacher report: r = .57, p < .001), composite victimization scores were derived by averaging these scores. Findings reported below did not differ when considering overt and relational victimization separately by either parent or teacher report.

An alpha level of .05 was used to determine significance for all analyses. For regression analyses, all continuous predictors were standardized to have a mean of zero and an SD of one. School clustering was accounted for in all analyses. Analyses were conducted using Stata Version 14.1 (Stata Corp, College Station, TX, USA).

Results

Sample Characteristics

There were no differences in child age, ADHD symptom severity, and socioeconomic status between those with victimization data available and those without (Table 2). However, there was an overrepresentation of males among those with victimization data available compared with those without (p = .005).

Table 2.

Sample Characteristics for Children With ADHD and Controls.

	ADHD (n = 122-173)	Control (160-199)	p value
Child characteristics
Age at baseline, M (SD)	7.3 (0.5)	7.3 (0.4)	.50
Age at 18-month follow-up, M (SD)	8.9 (0.5)	8.9 (0.4)	.88
Male, n (%)	122 (70.5)	130 (65.3)	.29
ADHD presentation, n (%)
Combined	93 (52)	—	—
Inattentive	64 (36)	—	—
Hyperactive/impulsive	22 (12)	—	—
Estimated full scale IQ standard score (WASI), M (SD)	92.3 (12.2)	101.8 (13.6)	<.001
Externalizing disorder, n (%)	94 (54.3)	17 (8.5)	<.001
Internalizing disorder, n (%)	44 (25.4)	9 (4.5)	<.001
Medication use at 18 months, n (%)
Methylphenidate	21 (17.2)	0 (0)	<.001
Dexamphetamine	0 (0)	0 (0)	—
Atomoxetine	1 (0.8)	0 (0)	<.001
Family characteristics
Survey completed by mother, n (%)	157 (95.7)	180 (92.8)	.24
Parent age in years, M (SD)	37.2 (5.8)	39.2 (5.2)	<.001
Single parent family, n (%)	42 (25.6)	22 (11.3)	<.001
Highest educational level of parent, n (%)
Completed high school	103 (62.8)	158 (81.4)	<.001
Completed university	40 (27.6)	92 (54.4)	<.001
Clinical range on K6, n (%)	5.4 (4.5)	2.6 (2.8)	<.001
SEIFA, M (SD)	1,012 (43.2)	1,015 (45.8)	.46

Note. WASI = Wechsler Abbreviated Scales of Intelligence; K6 = Kessler 6 scale; SEIFA = Socio-Economic Indexes for Areas.

Children with ADHD had lower IQ, and higher rates of both comorbid externalizing and internalizing disorders than controls. A small number of children with ADHD (22, 18%) were taking ADHD medication, and no control children were taking ADHD medication (Table 2).

Differences in PV in Children With and Without ADHD

Children with ADHD experienced higher levels of victimization than children without ADHD by parent (d = 0.77-d = 1.03) and teacher-reports (d = 0.43-d = 0.74) (Table 3).

Table 3.

Parent- and Teacher-Reported PV in Children With ADHD and Controls.

Variable	ADHDaM (SD)	ControlsbM (SD)	Mean difference (95% CI)	Cohen’s d	p value
Parent report
Overt victimization	1.83 (0.81)	1.27 (0.50)	0.55 [0.40, 0.70]	0.77	<.001
Relational victimization	2.30 (0.95)	1.41 (0.56)	0.90 [0.72, 1.10]	1.03	<.001
Teacher report
Overt victimization	1.35 (0.54)	1.14 (0.38)	0.20 [0.11, 0.30]	0.43	<.001
Relational victimization	1.78 (0.95)	1.20 (0.42)	0.57 [0.42, 0.72]	0.74	<.001

Note. PV = peer victimization; CI = confidence interval.

n—parent report = 122; teacher report = 183.

n—parent report = 161; teacher report = 167.

Predictors of PV in Children With ADHD

Parent-reported victimization: In Step 1, 6% of the variance in PV was explained by the included child predictors, with the overall model being nonsignificant (p = .13; Table 4). Only two child predictors were independently associated with victimization: conduct problems (β = .16, p = .08) and medication use (β = .38, p = .05).

Table 4.

Stepwise Linear Regression for Predictors of Parent-Reported PV in Children With ADHD (n = 118).

Variables	Step 1			Step 2			Step 3
Variables	β	SE	p	β	SE	p	β	SE	p
Child predictors
Gender	.09	0.16	.57	.12	0.16	.47	.12	0.17	.47
IQ	.04	0.09	.70	.04	0.09	.69	.04	0.09	.69
Language difficulties	.01	0.18	.95	.06	0.19	.76	.06	0.19	.77
Conduct problems	.16	0.09	.08	.17	0.09	.05	.18	0.10	.07
Emotional problems	−.01	0.07	.95	−.01	0.07	.92	−.01	0.07	.92
Self-control	.03	0.11	.78	.07	0.12	.56	.07	0.12	.59
Medication use	.38	0.19	.05	.36	0.19	.06	.36	0.19	.07
Parent predictors
Parent mental health				.09	0.07	.22	.09	0.07	.23
Angry parenting				.07	0.08	.42	0.07	0.09	.43
School predictors
Teacher–student relationship							.01	0.09	.95
Statistics
R²	.06		.07	.06
ΔR²	—		.01	−.01
Prob. > χ²	0.13		.09	.13

Note. SE = standard error.

The inclusion of family and school predictors did not add to the variance in PV, and the overall model was nonsignificant (p = .13). Although there was some evidence of a continued association with conduct problems (p = .07, β = .18) and medication use (p = .07, β = .36), these were no longer statistically significant.

Teacher-reported victimization: In Step 1, 10% of the variance in PV was explained by the included child predictors with the overall model being significant (p = .02; see Table 5). The only child predictor independently associated with PV was conduct problems (β = 0.19, p = .002).

Table 5.

Stepwise Linear Regression for Predictors of Teacher-Reported Victimization in Children With ADHD (n = 155).

Variables	Step 1			Step 2			Step 3
Variables	β	SE	p	β	SE	p	β	SE	p
Child predictors
Gender	0.04	0.12	.71	0.06	0.12	.63	0.08	0.12	.51
IQ	−0.06	0.07	.43	−0.06	0.07	.43	−0.05	0.07	.50
Language difficulties	−0.10	0.13	.43	−0.09	0.13	.46	−0.10	0.13	.43
Conduct problems	0.19	0.06	.002	0.19	0.06	.003	0.22	0.07	.001
Emotional problems	−0.03	0.05	.54	−0.03	0.05	.61	−0.02	0.05	.65
Self-control	0.04	0.08	.59	0.05	0.08	.56	0.02	0.08	.82
Medication use	0.08	0.14	.55	0.07	0.14	.62	0.07	0.14	.63
Parent predictors
Parent mental health				0.005	0.05	.92	−0.01	0.05	.90
Angry parenting				0.03	0.06	.56	0.05	0.06	.39
School predictors
Teacher–student relationship							0.08	0.06	.20
R ²	.10		.08	.08
ΔR²	—		−.02	.00
Prob. > χ²	0.02		0.04	0.04

Note. SE = standard error.

The inclusion of family and school predictors did not add to the variance in PV, although the model was still significant (p = .04). Conduct problems continued to be independently associated with PV in the final model (β = 0.22, p = .001).

Discussion

In this study, children with ADHD aged 8.9 years experienced higher levels of overt and relational victimization than their peers by both parent and teacher reports. Child factors accounted for the greatest variance in PV in children with ADHD. The strongest independent predictor of victimization was conduct problems, followed by medication use. Although some family and school predictors were associated with victimization in univariate analyses, there was limited evidence that they were independently associated with victimization when child factors were also considered.

Conduct problems was the main child predictor independently associated with victimization; this relationship was statistically significant for teacher-reported victimization and approached statistical significance for parent report. This finding was consistent with previous studies which have found that comorbid externalizing problems predicted PV in children with ADHD (Mayes et al., 2015; Sciberras et al., 2012). Children with ADHD and externalizing disorders often display socially inappropriate behaviors, perhaps triggering victimization.

This was the first study to investigate medication use as a predictor of PV in young children. We found an association between medication use and PV by parent report, independent of ADHD symptom severity or associated conduct problems. It is possible that witnessing the act of taking medication (less common in recent years given the high utilization of long-acting stimulant preparations) or awareness by classmates that the victim takes medication may increase the risk of PV. Our findings are at variance with Epstein-Ngo et al. (2015) who found no differences in PV when comparing adolescents with ADHD and recent stimulant prescription (n = 572) to adolescents with ADHD but without recent prescription (n = 173). The younger age of our sample may explain the difference in findings.

In contrast to previous studies, we did not find sex, ADHD symptom severity, or peer problems to be associated with PV in children with ADHD. Comparing 52 children with ADHD with 52 controls, Wiener and Mak (2009) found that girls with ADHD were more likely to be victimized than boys by self-report. They also found that ADHD symptom severity was associated with PV. However, our findings that ADHD symptom severity was not independently predictive of PV was consistent with two larger studies which used parent (Fite et al., 2014) and teacher (Humphrey et al., 2007) reports. In our study, academic competence was associated with PV in univariate analyses; however, there was little evidence of an independent association. It is possible a robust association would be observed at an older age as academic gaps tend to widen over time and so potentially become more stigmatizing.

This was the first study to examine parental psychological distress, parenting factors (self-efficacy, warmth, anger, consistency), and socioeconomic status in relation to PV in children with ADHD. None of these added to the variance of PV for children with ADHD. This contrasts with a meta-analysis in the general population which found that maladaptive parenting styles were independently predictive of PV (Lereya, Samara, & Wolke, 2013). Our finding that socioeconomic status did not influence victimization was however consistent with findings from the general population (Tippett & Wolke, 2014). This was also the first study to examine school-level variables in relation to PV in children with ADHD, and we found no associations.

This study had a number of design strengths. We recruited a community-ascertained sample of boys and girls with diagnostically confirmed ADHD in a narrow age band, giving confidence that our findings are generalizable to children with ADHD in the early primary school developmental period. We considered both overt and relational victimization, utilized a multi-informant assessment of victimization, and assessed a broad range of predictors at the child, family, and school level. Our study also had some limitations. The research criteria used to define ADHD status were comprehensive; however, this may not necessarily correlate with case definition by clinical assessment. We had relatively few participants taking ADHD medications, and so our ability to identify an association between medication use and PV may have been limited. Finally, we did not utilize peer or self-reports nor cyberbullying, which will become more relevant as the sample approaches adolescence.

In our study, the broad range of variables in our model across child, family, and school levels was only able to explain a minority of the variance in PV in children with ADHD. Qualitative research may be required to shed more light on the factors contributing to PV in these vulnerable children.

Conclusion

Our data highlight that PV is a real problem for children with ADHD, which may compound the stress and alienation already experienced by some of these children. Second, the identification of conduct problems as a risk factor for PV adds support to targeting this comorbidity in children with ADHD using proven treatments such as cognitive behavioral therapy, family interventions, and school interventions (Larmar & Gatfield, 2006), which may reduce the risk of PV and in turn its long-term negative consequences.

Footnotes

Acknowledgements

The authors would like to acknowledge the research assistants, students, and interns who contributed to data collection for this study. They would also like to thank the many families, teachers, and schools for their participation in this study. Some study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at Murdoch Children’s Research Institute (MCRI). REDCap is a secure, web-based application designed to support data capture for research studies.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is funded by the Australian National Health and Medical Research Council (NHMRC; project grant no. 1008522), the Collier Foundation, and the Murdoch Children’s Research Institute (MCRI). Dr. Sciberras’ position is funded by an NHMRC Early Career Fellowship in Population Health 1037159 (2012-2015) and an NHMRC Career Development Fellowship 1110688 (2016-2019). Dr. Efron is supported by a Clinician Scientist Fellowship from the MCRI. This research was supported by the Victorian Government’s Operational Infrastructure Support Program to the MCRI.

Author Biographies

Daryl Efron is a developmental-behavioral pediatrician at the Royal Children’s Hospital, Melbourne, Australia; a senior research fellow at the Murdoch Children’s Research Institute, Melbourne, Australia; and a senior lecturer in the Department of Pediatrics, The University of Melbourne.

Michell Wijaya is a medical student in the Department of Pediatrics, The University of Melbourne.

Philip Hazell is a child and adolescent psychiatrist, director of Child and Adolescent Mental Health Services, Sydney Local Health District; and conjoint professor of child and adolescent psychiatry at the Sydney Medical School and Thomas Walker Hospital.

Emma Sciberras is a clinical psychologist and senior lecturer in the School of Psychology, Deakin University.

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