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Profiles of School Refusal among Neurodivergent Youth

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Title: Profiles of School Refusal among Neurodivergent Youth
Language: English
Authors: Jessica E. Granieri (ORCID 0000-0002-5745-1342), Hannah E. Morton, Raymond G. Romanczyk (ORCID 0000-0001-9194-1209), Jennifer M. Gillis Mattson
Source: European Education. 2023 55(3-4):186-201.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 16
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Children, Adolescents, Autism Spectrum Disorders, Attention Deficit Hyperactivity Disorder, Students with Disabilities, Truancy, Profiles, Bullying, Peer Relationship, Severity (of Disability)
DOI: 10.1080/10564934.2023.2251013
ISSN: 1056-4934
1944-7086
Abstract: Neurodivergent youth (i.e., autistic, attention-deficit hyperactivity disorder) are at increased risk for school refusal and subsequent disengagement. Factors associated with school refusal in this population remain unclear. Latent profile analysis was conducted to identify profiles of school and demographic variables associated with parent-reported school refusal for 508 neurodivergent and community youth (ages 6-17 years). Five profiles were identified, including three groups characterized by frequent school refusal, high levels of neurodivergent traits, and frequent peer victimization. Differentiation was noted via educational placement, support needs, mental health, and bullying. Implications concern identification and intervention for subgroups of neurodivergent youth.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1407461
Database: ERIC
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  Value: <anid>AN0174795722;eue01sep.23;2024Jan17.05:36;v2.2.500</anid> <title id="AN0174795722-1">Profiles of School Refusal Among Neurodivergent Youth </title> <p>Neurodivergent youth (i.e., autistic, attention-deficit hyperactivity disorder) are at increased risk for school refusal and subsequent disengagement. Factors associated with school refusal in this population remain unclear. Latent profile analysis was conducted to identify profiles of school and demographic variables associated with parent-reported school refusal for 508 neurodivergent and community youth (ages 6–17 years). Five profiles were identified, including three groups characterized by frequent school refusal, high levels of neurodivergent traits, and frequent peer victimization. Differentiation was noted via educational placement, support needs, mental health, and bullying. Implications concern identification and intervention for subgroups of neurodivergent youth.</p> <p>School absenteeism is a major concern across many countries (Kearney et al., [<reflink idref="bib28" id="ref1">28</reflink>]). Nearly 14% of youth attending public schools in the United States are "chronically absent" (i.e., missing more than 15 days of school; U.S. Department of Education, [<reflink idref="bib51" id="ref2">51</reflink>]), which is consistent with high international estimates (Munkhaugen et al., [<reflink idref="bib40" id="ref3">40</reflink>]; Pflug & Schneider, [<reflink idref="bib45" id="ref4">45</reflink>]). These high rates are concerning due to many associated negative outcomes, such as poor academic achievement and later school dropout (e.g., Fremont, [<reflink idref="bib19" id="ref5">19</reflink>]; Kearney, [<reflink idref="bib27" id="ref6">27</reflink>]; Thomas et al., [<reflink idref="bib49" id="ref7">49</reflink>]). Notably, almost half of reported absences result from school refusal (Totsika et al., [<reflink idref="bib50" id="ref8">50</reflink>]). This refusal can be defined as any verbal or physical resistance to attend school that may result in school tardiness, partial absence (e.g., leaving school early), or complete absence (Berg et al., [<reflink idref="bib6" id="ref9">6</reflink>]; Kearney, [<reflink idref="bib27" id="ref10">27</reflink>]; Munkhaugen et al., [<reflink idref="bib39" id="ref11">39</reflink>]). Approximately 35% of community youth experience school refusal, which increases to over 50% for autistic youth (Kearney et al., [<reflink idref="bib29" id="ref12">29</reflink>]; Munkhaugen et al., [<reflink idref="bib39" id="ref13">39</reflink>]). Risk for school refusal may be even higher among autistic youth with co-occurring attention-deficit hyperactivity disorder (ADHD; Black & Zablotsky, [<reflink idref="bib8" id="ref14">8</reflink>]; McClemont et al., [<reflink idref="bib33" id="ref15">33</reflink>]).</p> <p>In the general community, youth who refuse school are more likely to endorse diagnoses of depression and separation anxiety, sleep disturbances, and somatic complaints (e.g., Egger et al., [<reflink idref="bib16" id="ref16">16</reflink>]; Kearney, [<reflink idref="bib27" id="ref17">27</reflink>]; Munkhaugen et al., [<reflink idref="bib40" id="ref18">40</reflink>]). Classroom climate may also increase likelihood of school refusal (Kearney, [<reflink idref="bib27" id="ref19">27</reflink>]), particularly via youth who are bullied feeling "threatened" at school (Glew et al., [<reflink idref="bib20" id="ref20">20</reflink>]). This concern is particularly salient given that 40% of middle schoolers have experienced bullying (Hicks et al., [<reflink idref="bib23" id="ref21">23</reflink>]) and approximately 25% of elementary-aged students report they would refuse school if bullied (Glew et al., [<reflink idref="bib20" id="ref22">20</reflink>]). The role of these potential risk factors related to autism and ADHD is not yet well understood.</p> <p>Autism spectrum disorder (ASD) is a neurodevelopmental condition often characterized by social communication differences, intense interests, and repetitive behaviors (American Psychiatric Association, [<reflink idref="bib1" id="ref23">1</reflink>]; World Health Organization, [<reflink idref="bib55" id="ref24">55</reflink>]).[<reflink idref="bib1" id="ref25">1</reflink>] ADHD is another neurodevelopmental diagnosis characterized by domains of inattention and hyperactivity/impulsivity, as well as differences in social information processing (American Psychiatric Association, [<reflink idref="bib1" id="ref26">1</reflink>]). There is notable overlap between characteristics of autism and ADHD (Antshel & Russo, [<reflink idref="bib2" id="ref27">2</reflink>]), and ADHD co-occurs in approximately 50% of autistic individuals (Hong et al., [<reflink idref="bib24" id="ref28">24</reflink>]). Along with an increased risk of school refusal (Black & Zablotsky, [<reflink idref="bib8" id="ref29">8</reflink>]; McClemont et al., [<reflink idref="bib33" id="ref30">33</reflink>]), autistic youth with ADHD are at higher risk for bullying victimization relative to autistic youth without ADHD (Chou et al., [<reflink idref="bib12" id="ref31">12</reflink>]). ADHD traits also increase risk for peer victimization (Evans et al., [<reflink idref="bib17" id="ref32">17</reflink>]; Yen et al., [<reflink idref="bib56" id="ref33">56</reflink>]), potentially via differences in noticing neurotypical social cues or violations of social norms (e.g., turn taking; Bellanti & Bierman, [<reflink idref="bib4" id="ref34">4</reflink>]; Diamantopoulou et al., [<reflink idref="bib15" id="ref35">15</reflink>]). This is concerning, as youth who are bullied are six times more likely to engage in school refusal (Vidourek et al., [<reflink idref="bib52" id="ref36">52</reflink>]). In fact, 35% of autistic youth have missed school due to bullying (McClemont et al., [<reflink idref="bib33" id="ref37">33</reflink>]), and 83.3% of autistic boys who engaged in school refusal reported being bullied almost every day (Bitsika et al., [<reflink idref="bib7" id="ref38">7</reflink>]).</p> <p>Autistic youth with ADHD may also experience greater externalizing (e.g., oppositional behavior) and internalizing (e.g., anxiety, mood problems) behavior relative to youth with only one of these conditions (Hong et al., [<reflink idref="bib24" id="ref39">24</reflink>]). Both internalizing and externalizing behaviors are commonly associated with both school refusal and bullying victimization (Munkhaugen et al., [<reflink idref="bib40" id="ref40">40</reflink>]) and particularly for autistic youth (Cappadocia et al., [<reflink idref="bib11" id="ref41">11</reflink>]; Munkhaugen et al., [<reflink idref="bib40" id="ref42">40</reflink>]). Externalizing behaviors may also influence accommodations or services via Individualized Education Plans (IEPs; e.g., behavioral support plan or 1:1 aide) and school placement (e.g., percent of the day spent in general education settings), both of which are associated with bullying victimization but are not yet well understood in the context of school refusal (Lauderdale-Littin et al., [<reflink idref="bib31" id="ref43">31</reflink>]; McClemont et al., [<reflink idref="bib33" id="ref44">33</reflink>]; Rowley et al., [<reflink idref="bib48" id="ref45">48</reflink>]).</p> <p>Overall, the literature on school refusal among neurodivergent youth is scarce. Neurodivergent traits, bullying, and internalizing behaviors are three factors consistently associated with school refusal. Externalizing behaviors, although not as commonly researched as internalizing symptoms, also appear to be associated with school refusal (McClemont et al., [<reflink idref="bib33" id="ref46">33</reflink>]; Munkhaugen et al., [<reflink idref="bib40" id="ref47">40</reflink>]). It is also plausible that percent of the day spent in general education may play an important role in school refusal, given that classroom climate is associated with school refusal (Kearney, [<reflink idref="bib27" id="ref48">27</reflink>]) and that support within the classroom setting was found to predict school refusal due to bullying among neurodivergent youth (McClemont et al., [<reflink idref="bib33" id="ref49">33</reflink>]). Further, placement in general education predicted bullying among neurodivergent youth, which is also associated with school refusal, suggesting general education may also relate to school refusal more directly (McClemont et al., [<reflink idref="bib33" id="ref50">33</reflink>]; Zablotsky et al., [<reflink idref="bib57" id="ref51">57</reflink>]).</p> <hd id="AN0174795722-2">The Present Study</hd> <p>Consistent with findings from international studies, neurodivergent (autistic, ADHD) youth in the United States display high rates of school refusal behavior, with increased risk associated with peer victimization, more autistic and ADHD traits, general education placement, and internalizing symptoms relative to community youth. Significant heterogeneity within autism and ADHD precludes a better understanding of vulnerabilities and support needs (Mottron & Bzdok, [<reflink idref="bib38" id="ref52">38</reflink>]; Nigg et al., [<reflink idref="bib42" id="ref53">42</reflink>]), which may be clarified through subgrouping or profile analyses (Karalunas et al., [<reflink idref="bib25" id="ref54">25</reflink>]; Kushki et al., [<reflink idref="bib30" id="ref55">30</reflink>]). Thus, the primary aim of this study was to identify profiles of youth school refusal and neurodivergent traits within a community-recruited sample of school age youth, oversampled for autism and ADHD, to inform targeted identification and intervention efforts. We hypothesized that multiple profiles of youth varying in both neurodivergent traits and school refusal frequency would emerge. A secondary, exploratory aim was to further compare characteristics of youth profiles to identify target variables for future research and educational support.</p> <hd id="AN0174795722-3">Methods</hd> <p></p> <hd id="AN0174795722-4">Participants</hd> <p>Participants were 508 adults from a larger study on peer victimization in autism, with data collection occurring from the end of January through the beginning of April 2020, with institutional review board (IRB) approval from Binghamton University. Eligible participants were at least 18 years old, were a primary caregiver for a child between 6 and 17 years old, had Internet access, and were able to read and answer questions in English. Recruitment materials described seeking parents of autistic or nonautistic youth to participate in the study, with the a priori goal to compare these groups. Importantly, in understanding the sample characteristics, youth without a parent-reported diagnosis of autism and with a diagnosis of ADHD were also grouped into the neurodivergent category, due to the aforementioned overlap in characteristics of autism and ADHD. Youth were classified as neurodivergent if they had a parent-reported diagnosis of ADHD, autism, or both, confirmed by clinical cutoffs on the corresponding diagnostic screener (i.e., Social Responsiveness Scale, Second Edition, and Vanderbilt ADHD Diagnostic Parent Rating Scale). Of note, neurodivergent youth with additional co-occurring diagnoses were not excluded, as neurodivergent youth with high rates of school refusal are more likely to have co-occurring internalizing and externalizing disorders relative to neurodivergent youth with low rates of school refusal (Cappadocia et al., [<reflink idref="bib11" id="ref56">11</reflink>]; Munkhaugen et al., [<reflink idref="bib40" id="ref57">40</reflink>]). A portion of youth (<emph>n</emph> = 144) did not have a parent-reported diagnosis of either autism or ADHD. These youth may have been neurotypical or may have had a different diagnosis, such as a learning disability, and were included as a "community comparison sample."</p> <p>Parents were recruited primarily within the United States through various methods, including targeted posts on social media and the distribution of digital flyers to organizations related to autistic or neurodivergent youth. To obtain a comparison sample, parent groups and organizations that were not specific to developmental disabilities were also included in recruitment efforts. Virtual recruitment efforts also reached some international participants, resulting in a small portion of parents (<emph>n</emph> = 27) from other countries. Data on international youth were retained to see whether they would emerge as a distinct profile indicating differences in the risk for school refusal or differences in the factors associated with school refusal. In total, 519 parents consented to participate and answered at least one survey question; 10 parents were excluded for not being at least 18 years old and one additional parent was statistically excluded during analysis due to lack of data for any of the variables of interest.</p> <p>Most parents were White, non-Hispanic (84.5%) women (94.5%) between the ages of 20 and 71 years (<emph>M<subs>ag</subs></emph><subs>e</subs> = 40.59, <emph>SD<subs>age</subs></emph> = 8.49) and married (72.2%; see Table 1). Most parents were employed at least part-time (58.9%). On average, parents had completed some college education or a two-year degree and reported an annual household income between $50,000 and $74,999. Youth were on average 11.55 years old (SD = 3.45 years), boys (68.9%), and White, non-Hispanic (66.6%). Most youth (<emph>n</emph> = 364) had parent-reported diagnoses of autism (<emph>n</emph> = 309) or ADHD (<emph>n</emph> = 212), with one-third diagnosed as both (32.0%). Youth with neither diagnosis (<emph>n</emph> = 144) were retained as a "community" sample.</p> <p>Table 1. Parent and child demographics.</p> <p> <ephtml> <table><thead><tr><td /><td>Overall</td><td>1 ND-HF</td><td>2 ND-HI</td><td>3 AT-MF</td><td>4 AT-LI</td><td>5 CC-LF</td><td>F/χ<sup>2</sup></td><td>Contrasts</td></tr></thead><tbody valign="top"><tr><td><italic>n</italic></td><td char=".">508</td><td char=".">145</td><td char=".">118</td><td char=".">92</td><td char=".">82</td><td char=".">71</td><td /><td /></tr><tr><td><italic>Demographics</italic></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td><bold> Child</bold></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td><bold />Age (years)</td><td char=".">11.55 (3.45)</td><td char=".">11.01 (3.26)</td><td char=".">11.72 (3.24)</td><td char=".">11.53 (3.57)</td><td char=".">11.86 (3.73)</td><td char=".">12.06 (3.61)</td><td char=".">1.52</td><td>NS</td></tr><tr><td><bold />Male</td><td char=".">68.90%</td><td char=".">69.66%</td><td char=".">81.36%</td><td char=".">64.13%</td><td char=".">65.85%</td><td char=".">56.34%</td><td char=".">15.66</td><td>2 > 5</td></tr><tr><td><bold />BIPOC</td><td char=".">33.40%</td><td char=".">30.34%</td><td char=".">27.35%</td><td char=".">35.16%</td><td char=".">36.59%</td><td char=".">43.66%</td><td char=".">6.33</td><td>NS</td></tr><tr><td><bold />LGBTQIA+</td><td char=".">10.04%</td><td char=".">12.41%</td><td char=".">16.10%</td><td char=".">6.52%</td><td char=".">3.66%</td><td char=".">7.04%</td><td char=".">11.96</td><td>2 > 4</td></tr><tr><td><bold> Caregiver</bold></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td><bold />Age (years)</td><td char=".">40.59 (8.49)</td><td char=".">39.54 (8.01)</td><td char=".">39.86 (7.99)</td><td char=".">40.80 (8.68)</td><td char=".">42.30 (8.27)</td><td char=".">41.66 (9.92)</td><td char=".">1.91</td><td>NS</td></tr><tr><td><bold />Married</td><td char=".">72.19%</td><td char=".">65.55%</td><td char=".">73.75%</td><td char=".">65.67%</td><td char=".">80.95%</td><td char=".">85.37%</td><td char=".">9.11</td><td>NS</td></tr><tr><td><bold />Employed full-time</td><td char=".">42.99%</td><td char=".">38.89%</td><td char=".">37.50%</td><td char=".">51.47%</td><td char=".">28.57%</td><td char=".">63.41%</td><td char=".">14.22</td><td>5 > 4</td></tr><tr><td><bold />SES</td><td char=".">0.00 (0.88)</td><td char=".">−0.19 (0.90)</td><td char=".">−0.06 (0.89)</td><td char=".">0.20 (0.88)</td><td char=".">0.13 (0.87)</td><td char=".">0.06 (0.74)</td><td char=".">2.37</td><td>NS</td></tr><tr><td><italic>ND traits + mental health</italic></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td><bold />AT or ADHD</td><td char=".">71.79%</td><td char=".">84.14%</td><td char=".">86.44%</td><td char=".">72.53%</td><td char=".">64.63%</td><td char=".">29.58%</td><td char=".">82.93</td><td>2,1 > 4 > 5; 3 > 5</td></tr><tr><td><bold />Autism diagnosis</td><td char=".">60.95%</td><td char=".">66.21%</td><td char=".">78.81%</td><td char=".">61.54%</td><td char=".">59.76%</td><td char=".">21.13%</td><td char=".">65.96</td><td>2,1,3,4 > 5</td></tr><tr><td><bold />ADHD diagnosis</td><td char=".">41.81%</td><td char=".">62.07%</td><td char=".">50.00%</td><td char=".">43.96%</td><td char=".">15.85%</td><td char=".">14.08%</td><td char=".">78.89</td><td>1,2,3 > 4,5</td></tr><tr><td><bold />AT + ADHD</td><td char=".">31.97%</td><td char=".">44.14%</td><td char=".">42.37%</td><td char=".">32.97%</td><td char=".">10.98%</td><td char=".">5.63%</td><td char=".">64.75</td><td>1,2,3 > 4,5</td></tr><tr><td><bold />SRS-2 <italic>t</italic> score</td><td char=".">70.40 (13.83)</td><td char=".">79.63 (8.44)</td><td char=".">78.35 (8.83)</td><td char=".">67.18 (11.05)</td><td char=".">60.52 (10.78)</td><td char=".">52.38 (9.72)</td><td char=".">93.56</td><td>1,2 > 3 > 4 > 5</td></tr><tr><td><bold />Inattention</td><td char=".">5.02 (3.25)</td><td char=".">7.89 (1.16)</td><td char=".">7.43 (1.56)</td><td char=".">3.70 (1.92)</td><td char=".">1.66 (1.59)</td><td char=".">0.65 (1.07)</td><td char=".">360.82</td><td>1,2 > 3 > 4 > 5</td></tr><tr><td><bold />Hyperactive/impulsivity</td><td char=".">3.53 (3.02)</td><td char=".">5.40 (2.65)</td><td char=".">5.56 (2.59)</td><td char=".">2.44 (2.07)</td><td char=".">0.97 (1.32)</td><td char=".">0.67 (1.18)</td><td char=".">85.29</td><td>1,2 > 3 > 4,5</td></tr><tr><td><bold />Internalizing</td><td char=".">2.12 (2.31)</td><td char=".">3.37 (2.37)</td><td char=".">2.66 (2.46)</td><td char=".">2.04 (1.96)</td><td char=".">0.64 (1.37)</td><td char=".">0.32 (0.59)</td><td char=".">28.64</td><td>1 > 3 > 4,5; 2 > 4,5</td></tr><tr><td><bold />Externalizing</td><td char=".">2.90 (3.29)</td><td char=".">4.31 (3.52)</td><td char=".">4.47 (3.39)</td><td char=".">1.94 (2.66)</td><td char=".">0.95 (1.71)</td><td char=".">0.68 (1.36)</td><td char=".">28.84</td><td>2,1 > 3,4,5</td></tr><tr><td><bold />ADHD medications</td><td char=".">20.60%</td><td char=".">29.66%</td><td char=".">25.22%</td><td char=".">20.22%</td><td char=".">11.25%</td><td char=".">5.63%</td><td char=".">25.75</td><td>1 > 4,5; 2 > 5</td></tr><tr><td><italic>School</italic></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td><bold />Bullying total</td><td char=".">26.91 (21.12)</td><td char=".">38.62 (21.34)</td><td char=".">29.98 (21.84)</td><td char=".">28.60 (17.84)</td><td char=".">11.42 (10.14)</td><td char=".">9.79 (8.89)</td><td char=".">31.21</td><td>1 > 2,3 > 4,5</td></tr><tr><td><bold />Bullying victim</td><td char=".">39.44%</td><td char=".">66.67%</td><td char=".">45.24%</td><td char=".">40.28%</td><td char=".">7.69%</td><td char=".">2.13%</td><td char=".">98.59</td><td>1 > 2,3 > 4,5</td></tr><tr><td><bold />Time in general education</td><td char=".">56.00%</td><td char=".">81.56%</td><td char=".">4.03%</td><td char=".">91.03%</td><td char=".">10.61%</td><td char=".">97.89%</td><td char=".">115.48</td><td>5,3 > 4,2; 1 > 4,2</td></tr><tr><td><bold />Nonpublic school</td><td char=".">29.92%</td><td char=".">0.00%</td><td char=".">58.47%</td><td char=".">0.00%</td><td char=".">55.82%</td><td char=".">0.00%</td><td char=".">0.26</td><td>NS</td></tr><tr><td><italic>Support needs</italic></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td><bold />Past or current IEP</td><td char=".">72.64%</td><td char=".">82.76%</td><td char=".">85.59%</td><td char=".">68.48%</td><td char=".">73.17%</td><td char=".">35.21%</td><td char=".">63.44</td><td>2,1,4,3 > 5</td></tr><tr><td><bold />IEP services</td><td char=".">1.23 (1.43)</td><td char=".">1.35 (1.31)</td><td char=".">1.69</td><td char=".">1.02 (1.43)</td><td char=".">1.38 (1.48)</td><td char=".">0.30 (0.76)</td><td char=".">12.67</td><td>2,1,4,3 > 5; 2 > 3</td></tr><tr><td><bold />Academic</td><td char=".">1.33 (1.42)</td><td char=".">1.71 (1.45)</td><td char=".">2.01 (1.43)</td><td char=".">0.82 (1.05)</td><td char=".">1.02 (1.35)</td><td char=".">0.46 (1.03)</td><td char=".">17.19</td><td>2,1 > 4,3,5</td></tr><tr><td><bold />Spoken communication</td><td char=".">1.12 (1.25)</td><td char=".">1.25 (1.17)</td><td char=".">1.52 (1.39)</td><td char=".">0.85 (1.17)</td><td char=".">1.25 (1.38)</td><td char=".">0.39 (0.70)</td><td char=".">8.49</td><td>2 > 3,5; 4,1 > 5</td></tr><tr><td><bold />Self-care</td><td char=".">1.47 (1.33)</td><td char=".">1.82 (1.15)</td><td char=".">2.11 (1.39)</td><td char=".">1.23 (1.20)</td><td char=".">1.20 (1.33)</td><td char=".">0.24 (0.59)</td><td char=".">23.93</td><td>2,1 > 3,4 > 5</td></tr><tr><td><bold />Social</td><td char=".">1.16 (1.14)</td><td char=".">1.69 (1.12)</td><td char=".">1.81 (1.09)</td><td char=".">0.89 (0.89)</td><td char=".">0.36 (0.69)</td><td char=".">0.13 (0.40)</td><td char=".">42.38</td><td>2,1 > 3 > 4,5</td></tr></tbody></table> </ephtml> </p> <hd id="AN0174795722-5">Measures</hd> <p></p> <hd id="AN0174795722-6">Social Responsiveness Scale, Second Edition (SRS-2)</hd> <p>The Social Responsiveness Scale, Second Edition (SRS-2) school-age form is a parent- and teacher- report measure of autistic traits in youth ages 4–18 years, with strong internal consistency and good interrater reliability (Constantino et al., [<reflink idref="bib14" id="ref58">14</reflink>]), as well as good predictive and concurrent validity (Bruni, [<reflink idref="bib10" id="ref59">10</reflink>]). The clinical cutoff for autistic traits is a total score ≥60. In the current sample, internal consistency for items contributing to the total score was excellent (ω =.99).</p> <hd id="AN0174795722-7">Vanderbilt ADHD Diagnostic Parent Rating Scale (VADPRS)</hd> <p>The Vanderbilt ADHD Diagnostic Parent Rating Scale (VADPRS) is a measure of ADHD symptoms, externalizing symptoms, and internalizing symptoms for youth ages 4–18 years (Wolraich et al., [<reflink idref="bib54" id="ref60">54</reflink>]). The measure consists of 55 items. The first 47 items pertain to symptoms of ADHD (i.e., inattentive, hyperactive, and combined subtype), conduct disorder (CD), oppositional defiant disorder (ODD), and anxiety/depression, and the remaining eight items pertain to functioning across various domains, such as academic performance, interpersonal relationships, and participation in organized activities. The sum scores of CD- and ODD-related traits measured on the VADPRS are thought to best emulate externalizing symptoms, while anxiety/depression symptoms are thought to best emulate internalizing symptoms (Becker et al., [<reflink idref="bib3" id="ref61">3</reflink>]). In the current sample, internal consistency was excellent for the <emph>inattentive</emph> (ω =.99), <emph>hyperactive/inattentive</emph> (ω =.99), <emph>CD/ODD</emph> (ω =.99), and <emph>anxiety/depression</emph> (ω =.99) subscales.</p> <p>The VADPRS was used as an index of ADHD-related traits, internalizing symptoms, and externalizing symptoms. Clinical cutoffs for the inattentive or hyperactive subtypes are six or more out of nine questions endorsed as a score of 2 ("often") or 3 ("very often") on corresponding subscale questions, and a score of 4 ("somewhat of a problem") or 5 ("problematic") on one of the eight functional impairment questions. Items from the VADPRS were also used to comprise composite scores describing children's support needs (see the "Support Needs" subsection).</p> <hd id="AN0174795722-8">Assessment of Bullying Experiences (ABE)</hd> <p>The Assessment of Bullying Experiences (ABE) is a parent-report measure of bullying victimization for neurodivergent youth (Morton et al., [<reflink idref="bib36" id="ref62">36</reflink>]). Parents rate the frequency of their child's bullying experiences across cyber, relational, verbal, and physical bullying on a 6-point scale (0: <emph>never</emph> to 5: <emph>at least once per week or more</emph>). The ABE has both good convergent and discriminant validity (Morton et al., [<reflink idref="bib36" id="ref63">36</reflink>]). In the current sample, internal consistency for items contributing to the overall ABE bullying scale was excellent (ω =.99).</p> <p>While a higher total score is generally indicative of more frequent experiences of bullying, scores ≥29 for neurodivergent youth and ≥36 for community youth are of particular concern. Scores above these thresholds are considered to be clinically meaningful, as they may be associated with current or future mental health concerns (Morton et al., [<reflink idref="bib35" id="ref64">35</reflink>]).</p> <hd id="AN0174795722-9">GO4KIDDS Brief Adaptive Scale</hd> <p>The GO4KIDDS Brief Adaptive Scale is a parent-report measure of adaptive skills and independence pertaining to youth and adolescents with developmental disabilities, ages 3–20 years, in the areas of daily living, communication, and social interaction (Perry et al., [<reflink idref="bib44" id="ref65">44</reflink>]). Internal consistency for items contributing to overall adaptive skill was good (ω =.87). Items from the GO4KIDDS Brief Adaptive Scale were used to comprise composite scores describing children's support needs (see the "Support Needs" subsection).</p> <hd id="AN0174795722-10">Demographics Questionnaire</hd> <p>Demographic information was collected on parents (i.e., age, race, gender, education, marital status, occupation, and income) and their child (i.e., age, race, gender, and sexual orientation). Parents also reported on their child's school experiences, such as reporting their child's school setting (e.g., public versus nonpublic school), the percent of time spent in a general education setting versus another setting (e.g., special education classroom), and identifying IEP-related services their child receives. The percent of time spent in general education was measured using a 5-point ordinal scale, (1: <emph>0% of the day</emph> to 5: <emph>100% of the day</emph>). Two composite variables, "past or current IEP" and "IEP services," were created from individual items asking about speech therapy, occupational therapy, physical therapy, a 1:1 aide, and a behavior support plan to indicate (a) whether the child currently or has ever received IEP-related services and (b) the number of current and lifetime IEP-related services received.</p> <p>Parents reported the frequency of their child's school refusal in the past school year, using a 7-point ordinal scale (1: <emph>never</emph> to 7: <emph>at least once per week</emph>). Parents also endorsed whether any or all of the following list of reasons applied to why they believed their child resists or refuses school: (a) minor illness, (b) serious health condition, (c) tired/difficulty sleeping, (d) bullying, (e) mental health, (f) doesn't like teacher/schoolwork, (g) wants to stay home and play, (h) other. All "other" options were qualitatively described by parents, and were ultimately able to be collapsed into one of the seven other reasons for parsimony.</p> <hd id="AN0174795722-11">Support Needs</hd> <p>Four composite variables were created reflecting academic, social, spoken communication, and self-care support needs (see Table S1). Each variable was the sum (range 0–4) of the number of items the parent reported their child required some support or was not independent. Academic support needs (ω =.85) comprised VADPRS Performance items on school and learning performance (numbers 48–51, items endorsed as 4: <emph>somewhat of a problem</emph> or 5: <emph>problematic</emph>). Social support needs (ω =.75) similarly comprised VADPRS performance items on relationships and team participation (numbers 52–55). Spoken communication support needs (ω =.77) comprised two items from the GO4KIDDS on understanding and using spoken language (numbers 3 and 4, number of items not scored as independent) and two items from the demographic questionnaire asking how the child most often communicates and whether they can give verbal directions. Self-care support needs (ω =.81) comprised four items from the GO4KIDDS on toileting, dressing, and eating (numbers 1 and 6–8, number of items not scored as independent).</p> <hd id="AN0174795722-12">Procedure</hd> <p>All participation occurred online via the secure Qualtrics survey platform. Parents completed an initial consent page and then a child demographics questionnaire. Subsequent questionnaires were presented in randomized order. Parents were instructed to answer all questions about <emph>one</emph> of their children between the ages of 6 and 17. Parent demographic information was collected as the final part of the survey. Upon completion, parents were provided with the option to enter a raffle for one of three $100 gift cards by completing a separate survey with their contact information.</p> <hd id="AN0174795722-13">Data Analytic Plan</hd> <p>Latent profile analysis (LPA) was conducted in MPlus 8.7 using full information maximum likelihood (FIML) for missing data. LPA is a recommended approach for exploratory research, appropriate given scarce literature surrounding school refusal in neurodivergent youth. Given heterogeneity in both autism and ADHD (Mottron & Bzdok, [<reflink idref="bib38" id="ref66">38</reflink>]), autistic and ADHD-related traits were conceptualized dimensionally (Constantino et al., [<reflink idref="bib13" id="ref67">13</reflink>]; Mottron, [<reflink idref="bib37" id="ref68">37</reflink>]). Ordinal variables with five or greater levels, such as time in general education and the primary variable of interest, school refusal frequency, were retained as continuous for parsimony and reduction of Type I errors (Rhemtulla et al., [<reflink idref="bib46" id="ref69">46</reflink>]). Other included variables with literature precedence for association with school refusal were autistic- (SRS-2 total score) and ADHD-related traits (VADPRS inattentive and hyperactive/impulsive subscales), internalizing behaviors (VADPRS anxiety/depression), frequency of bullying victimization (ABE total score), and time spent in general education.</p> <p>Models were compared using the Akaike information criterion/Bayesian information criterion (AIC/BIC), entropy, and Lo–Mendell–Rubin Adjusted Likelihood. Improvement was identified via smaller AIC/BIC statistics, including AIC smaller than both the BIC and the adjusted BIC (Weller et al., [<reflink idref="bib53" id="ref70">53</reflink>]), entropy greater than 0.7 (Muthén, [<reflink idref="bib41" id="ref71">41</reflink>]), and a significant Lo–Mendell–Rubin Adjusted Likelihood test (Nylund et al., [<reflink idref="bib43" id="ref72">43</reflink>]). Fit statistics were used as a flexible guide to identify the optimal model, which was also informed by theory (Nylund et al., [<reflink idref="bib43" id="ref73">43</reflink>]; Weller et al., [<reflink idref="bib53" id="ref74">53</reflink>]).</p> <p>LPA assignments were then exported for further differentiation of classes by demographics and variables with less literature precedence for association with school refusal (e.g., externalizing symptoms, support needs). Scheffé's adjustment was used for multiple comparisons. These post hoc comparisons were conducted with the subset of data that had complete responses for the respective variables, given that FIML requires complete cases for the dependent variable. All preliminary data cleaning and post hoc analyses were completed in StataBE.</p> <hd id="AN0174795722-14">Results</hd> <p></p> <hd id="AN0174795722-15">School Refusal Frequency</hd> <p>Overall, 64.0% of youth refused school in the past school year, with 48.4% refusing school at least monthly and 29.6% doing so weekly. Of youth who had ever refused school, the most common reason was bullying (46.8%), followed by mental health (43.5%; see Table 2).</p> <p>Table 2. Parent-reported school refusal frequency and reasons.</p> <p> <ephtml> <table><thead><tr><td /><td>Overall</td><td>1 ND-HF</td><td>2 ND-HI</td><td>3 AT-MF</td><td>4 AT-LI</td><td>5 CC-LF</td><td>F/x2</td><td>Contrasts</td></tr></thead><tbody valign="top"><tr><td><italic>n</italic></td><td>508</td><td>145</td><td>118</td><td>92</td><td>82</td><td>71</td><td /><td /></tr><tr><td>Refusal frequency<xref ref-type="table-fn" rid="tfn2">a</xref></td><td>3.15 (2.39)</td><td>4.07 (2.08)</td><td>4.15 (2.19)</td><td>3.09 (2.32)</td><td>1.93 (2.10)</td><td>1.14 (1.68)</td><td char=".">36.27</td><td>2,1 > 3 > 4,5</td></tr><tr><td><italic>Refusal reasons</italic></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Minor illness</td><td char=".">19.18%</td><td char=".">24.06%</td><td char=".">12.38%</td><td char=".">18.84%</td><td char=".">19.61%</td><td char=".">21.21%</td><td char=".">5.51</td><td>NS</td></tr><tr><td>Serious health condition</td><td char=".">3.84%</td><td char=".">2.26%</td><td char=".">4.76%</td><td char=".">5.80%</td><td char=".">3.92%</td><td char=".">3.03%</td><td char=".">1.97</td><td>NS</td></tr><tr><td>Sleep</td><td char=".">38.87%</td><td char=".">36.09%</td><td char=".">43.81%</td><td char=".">39.13%</td><td char=".">35.29%</td><td char=".">39.39%</td><td char=".">1.78</td><td>NS</td></tr><tr><td>Bullying</td><td char=".">46.80%</td><td char=".">57.89%</td><td char=".">42.86%</td><td char=".">47.83%</td><td char=".">33.33%</td><td char=".">33.33%</td><td char=".">13.53</td><td>1 > 4</td></tr><tr><td>Mental health</td><td char=".">43.48%</td><td char=".">54.89%</td><td char=".">46.67%</td><td char=".">36.23%</td><td char=".">35.29%</td><td char=".">15.15%</td><td char=".">22.52</td><td>1,2 > 5</td></tr><tr><td>Teacher/work</td><td char=".">38.62%</td><td char=".">41.35%</td><td char=".">42.86%</td><td char=".">43.48%</td><td char=".">29.41%</td><td char=".">18.18%</td><td char=".">10.25</td><td>NS</td></tr><tr><td>Play</td><td char=".">37.08%</td><td char=".">36.84%</td><td char=".">41.90%</td><td char=".">36.23%</td><td char=".">35.29%</td><td char=".">27.27%</td><td char=".">2.55</td><td>NS</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note.</emph> School refusal frequency and reasons for school refusal in the past school year were parent-reported.</p> <p>2 School refusal frequency was reported on a 7-point ordinal scale: 1 = <emph>never</emph>, 2 = <emph>has happened in the past, but not this school year</emph>, 3 = <emph>1–2 times this school year</emph>, 4 = <emph>every few months</emph>, 5 = <emph>about once a month</emph>, 6 = <emph>2–3 times per month</emph>, 7 = <emph>at least once a week</emph>.</p> <hd id="AN0174795722-16">LPA Solution</hd> <p>A five-class solution was the best fit to the data (see Table 3). Classes were labeled according to the presence of neurodivergent (ND) traits, school refusal (high [H], moderate [M], or low [L]), and time spent in general education (frequent [F] or infrequent [I]). Classes 1 and 2 (i.e., ND-HF and ND-HI, respectively) had high levels of ADHD and autistic traits and high levels of school refusal, but the ND-HF class comprised students who spent frequent time in general education and the ND-HI class comprised students who spent infrequent time in general education. The third and fourth classes (i.e., AT-MF and, AT-LI, respectively) had autistic but not ADHD traits above the clinical threshold and engaged in moderate to low school refusal. The fifth class (i.e., CC-LF) included community youth with low ND traits and low school refusal.</p> <p>Table 3. Model fit statistics for different class sizes.</p> <p> <ephtml> <table><thead><tr><td>Classes<xref ref-type="table-fn" rid="tfn4">a</xref></td><td>AIC</td><td>Adj. BIC</td><td>Class count</td><td>Entropy</td><td>Class prob</td><td>LMR</td><td>LRT<xref ref-type="table-fn" rid="tfn5">b</xref></td></tr></thead><tbody valign="top"><tr><td>2</td><td char=".">13860.405</td><td char=".">13883.645</td><td char=".">41%</td><td char=".">0.723</td><td char=".">0.865</td><td char="."><.001</td><td char="."><.001</td></tr><tr><td>3</td><td char=".">13753.655</td><td char=".">13785.345</td><td char=".">18%</td><td char=".">0.694</td><td char=".">0.738</td><td char=".">0.128</td><td char="."><.001</td></tr><tr><td>4</td><td char=".">13366.434</td><td char=".">13406.576</td><td char=".">17%</td><td char=".">0.862</td><td char=".">0.89</td><td char="."><.001</td><td char="."><.001</td></tr><tr><td><bold>5</bold></td><td char="."><bold>13302.251</bold></td><td char="."><bold>13350.844</bold></td><td char="."><bold>14%</bold></td><td char="."><bold>0.825</bold></td><td char="."><bold>0.759</bold></td><td char="."><bold>0.067</bold></td><td char="."><bold><.001</bold></td></tr><tr><td>6</td><td char=".">13241.997</td><td char=".">13299.04</td><td char=".">10%</td><td char=".">0.801</td><td char=".">0.723</td><td char=".">0.092</td><td char="."><.001</td></tr><tr><td>7</td><td char=".">13161.947</td><td char=".">13227.442</td><td char=".">5%</td><td char=".">0.821</td><td char=".">0.674</td><td char=".">0.150</td><td char="."><.001</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note. N</emph> = 508. Boldfaced values indicate the best fitting model based on these statistics and theory. AIC = Akaike information criterion; BIC = Bayesian information criterion; LMR LRT = Lo–Mendell–Rubin Likelihood Ratio test.</item> <item>4 The six- and seven-class solutions did not converge. Upon rerunning these class solutions, there was variability in the model fit statistics, suggesting that the model cannot be trusted. Therefore, the five-class model was deemed the best model.</item> <item>5 Mendell–Rubin likelihood ratio test compares model fit of the class (<emph>k</emph>) to the class smaller (<emph>k</emph> – 1).</item> </ulist> <hd id="AN0174795722-17">Class Descriptions</hd> <p> <bold>Class 1: ND-HF (<emph>n</emph> = 145; 29% of sample).</bold> The ND-HF class comprised neurodivergent (ND) youth who, relative to other neurodivergent and community youth, engaged in high levels (H) of school refusal (∼1 time per month) and who spent frequent (F) time in general education (i.e., 81.56% of the day). They had clinical levels of autistic traits and ADHD inattention and hyperactivity/impulsivity measured just below the cutoff. ND-HF youth also met the screening thresholds for internalizing symptoms and were bullied by peers (measuring above the involvement cutoff).</p> <p> <bold>Class 2: ND-HI (<emph>n</emph> = 118; 23%)</bold>. The ND-HI class included ND youth who, relative to other neurodivergent and community youth, engaged in high (H) school refusal (i.e., ∼1 time per month) and who spent infrequent (I) time in general education (i.e., 4.03% of the day). They had clinical levels of autistic traits, ADHD inattention, and ADHD hyperactivity/impulsivity. ND-HI youth had internalizing symptoms at the screening cutoff and experienced regular bullying victimization (measuring just above the involvement cutoff).</p> <p> <bold>Class 3: AT-MF (<emph>n</emph> = 92, 18%).</bold> The AT-MF class included autistic (AT) youth who, relative to other neurodivergent and community youth, engaged in moderate (M) school refusal (i.e., every few months) and who spent frequent (F) time in general education (i.e., 91.03% of the day). They had clinical levels of autistic traits but not ADHD inattention or hyperactivity/impulsivity. AT-MF youth also had subthreshold internalizing symptoms and were victims of bullying.</p> <p> <bold>Class 4: AT-LI (<emph>n</emph> = 82; 16%).</bold> The AT-LI class included autistic (AT) youth who, relative to other neurodivergent and community youth, engaged in low (L) school refusal (i.e., ∼1–2 times per year) and who spent infrequent (I) time in general education (i.e., 10.61% of the day). They had clinical levels of autistic traits but not ADHD inattention or hyperactivity/impulsivity. AT-LI youth also had low levels of internalizing symptoms and were rarely bullying victims.</p> <p> <bold>Class 5: CC-LF (<emph>n</emph> = 71, 14%).</bold> This class included community comparison (CC) youth who, relative to other youth, engaged in low (L) school refusal (i.e., in the past) and who spent frequent (F) time in general education (i.e., 97.89% of the day). They measured below clinical cutoffs for both autistic and ADHD traits, as well as internalizing symptoms, and experienced low levels of bullying victimization.</p> <hd id="AN0174795722-18">Class Comparisons</hd> <p>Figure 1 shows class comparisons across school refusal, time in general education, ND traits, internalizing symptoms, and bullying victimization.</p> <p>PHOTO (COLOR): Figure 1. Z-scores of school refusal and school refusal-related variables.</p> <p> <bold>School Refusal.</bold> There was a significant effect of class assignment on school refusal (<emph>F</emph>(<reflink idref="bib4" id="ref75">4</reflink>, 489) = 36.27, <emph>p</emph> <.001) such that ND-HI and ND-HF youth's monthly refusal was more frequent than refusal every few months for AT-MF youth (<emph>t</emph> ≥3.41, adjusted [adj.] <emph>p</emph> ≤.022). Each of these classes refused school more frequently than AT-LI youth (<emph>t</emph> > 3.58, adj. <emph>p</emph> <.013) and CC-LF youth (<emph>t</emph> > 5.79, adj. <emph>p</emph> <.001), which did not differ (<emph>t</emph> = 2.29, adj. <emph>p</emph> =.266). Of youth who ever refused school, classes differed in likelihood of school refusal due to bullying (χ<sups>2</sups>(<reflink idref="bib4" id="ref76">4</reflink>) = 13.53, <emph>p</emph> =.009) or mental health (χ<sups>2</sups>(<reflink idref="bib4" id="ref77">4</reflink>) = 22.52, <emph>p</emph> <.001). ND-HF youth were more likely to refuse school due to bullying compared to AT-LI youth (<emph>t</emph> = 3.12, adj. <emph>p</emph> =.045). ND-HF and ND-HI youth were more likely to refuse school due to mental health versus CC-LF youth (<emph>t</emph> ≥ 3.98, adj. <emph>p</emph> <.003).</p> <p> <bold>Autism</bold>. There was a significant difference across classes in frequency of parent-reported autism diagnosis (χ<sups>2</sups>(<reflink idref="bib4" id="ref78">4</reflink>) = 65.96, <emph>p</emph> <.001) and degree of autistic traits (<emph>F</emph>(<reflink idref="bib4" id="ref79">4</reflink>, 343) = 93.56, <emph>p</emph> <.001). An autism diagnosis was less frequently reported for CC-LF youth compared to other classes (<emph>t</emph> ≥ 5.32, adj. <emph>p</emph> <.001). The other classes did not differ in the frequency of autism diagnosis (adj. <emph>p</emph> ≥.079). As measured by the SRS-2 total <emph>t</emph> score, the highest autistic traits were reported for youth in the ND-HF class and ND-HI class, which were each significantly greater than the autistic traits reported for youth in the AT-MF class (ND-HF: <emph>t</emph> = 8.34, adj. <emph>p</emph> <.001; ND-HI: <emph>t</emph> = 7.14, adj. <emph>p</emph> <.001). These three classes each had more autistic traits compared to the AT-LI class (<emph>t</emph> ≥ 3.71, adj. <emph>p</emph> ≤.009) and CC-LF class (<emph>t</emph> > 8.24, adj. <emph>p</emph> <.001), which also differed (<emph>t</emph> = 4.15, adj. <emph>p</emph> =.002).</p> <p> <bold>ADHD</bold>. There was a significant difference across classes in the frequency of parent-reported ADHD diagnosis (χ<sups>2</sups>(<reflink idref="bib4" id="ref80">4</reflink>) = 78.89, <emph>p</emph> <.001), as well as in the degree of ADHD-inattentive traits (<emph>F</emph>(<reflink idref="bib4" id="ref81">4</reflink>, 378) = 360.82, <emph>p</emph> <.001) and ADHD hyperactive/impulsive traits (<emph>F</emph>(<reflink idref="bib4" id="ref82">4</reflink>, 377) = 85.29, <emph>p</emph> <.001). Likelihood of an ADHD diagnosis was significantly greater for youth in the ND-HF, ND-HI, and AT-MF classes compared to those in AT-LI (<emph>t</emph> ≥ 4.27, adj. <emph>p</emph> ≤.001) and CC-LF (<emph>t</emph> > 4.50, adj. <emph>p</emph> <.001) classes, which did not differ (<emph>t</emph> = 0.31, adj. <emph>p</emph> =.999). Traits of ADHD-inattention and hyperactivity/impulsivity were also similarly elevated in the ND-HF and ND-HI classes (inattentive: <emph>t</emph> = 2.13, adj. <emph>p</emph> =.338; hyperactivity/impulsivity: <emph>t</emph> = –0.49, adj. <emph>p</emph> =.993). Inattentive and hyperactive/impulsive traits were higher in these two classes compared to AT-MF youth (inattentive: <emph>t</emph> > 15.97, adj. <emph>p</emph> <.001; hyperactivity/impulsivity: <emph>t</emph> ≥ 8.88, adj. <emph>p</emph> <.001), and each class had higher results than for CC-LF youth (inattentive: <emph>t</emph> ≥ 3.57, adj. <emph>p</emph> ≤.014).</p> <p> <bold>School Factors.</bold> The number of current IEP-related services differed by class (<emph>F</emph>(<reflink idref="bib4" id="ref83">4</reflink>, 502) = 12.67, <emph>p</emph> <.001), with more services reported for youth in the ND-HF, ND-HI, and AT-LI classes compared to the CC-LF class (<emph>t</emph> ≥ 4.89, adj. <emph>p</emph> <.001). Support needs also varied by class (<emph>F</emph> ≥ 8.49, <emph>p</emph> <.001), with ND-HI and ND-HF youth having higher academic, self-care, and social support needs compared to each of the other classes (<emph>t</emph> > 3.09, adj. <emph>p</emph> ≤.05). ND-HI and ND-HF youth also had more spoken communication support needs compared to CC-LF youth (<emph>t</emph> ≥ 4.19, adj. <emph>p</emph> ≤.002); however, ND-HF youth's spoken communication support needs did not differ from any of the other classes (<emph>t</emph> < |2.19|, adj. <emph>p</emph> >.310).</p> <p>Bullying victimization frequency (<emph>F</emph>(<reflink idref="bib4" id="ref84">4</reflink>, 355) = 31.21, <emph>p</emph> <.001) and likelihood (χ<sups>2</sups>(<reflink idref="bib4" id="ref85">4</reflink>) = 98.59, <emph>p</emph> <.001) differed by class, such that ND-HF youth experienced significantly more bullying victimization compared to each of the other classes (<emph>t</emph> ≥ 3.23, adj. <emph>p</emph> ≤.035); this class also had significantly more current bullying victims compared to the AT-MF, AT-LI, and CC-LF classes (<emph>t</emph> > 3.57, adj. <emph>p</emph> ≤.013). Similar and moderate frequency of bullying victimization, and likelihood of current victimization, were also reported for ND-HI and AT-MF youth, with results that were higher than for AT-LI or CC-LF youth (<emph>t</emph> ≥ 4.75, adj. <emph>p</emph> <.001).</p> <p> <bold>Internalizing and Externalizing Symptoms.</bold> Classes differed by both internalizing symptoms (<emph>F</emph>(<reflink idref="bib4" id="ref86">4</reflink>, 364) = 28.64, <emph>p</emph> <.001) and externalizing symptoms (<emph>F</emph>(<reflink idref="bib4" id="ref87">4</reflink>, 370) = 28.84, <emph>p</emph> <.001). Internalizing (Int) and externalizing (Ext) symptoms similarly measured at or above screening cutoffs for ND-HF and ND-HI youth (Int: <emph>t</emph> = 2.41, adj. <emph>p</emph> =.218; Ext: <emph>t</emph> = 0.39, adj. <emph>p</emph> =.997) and were significantly higher for than AT-LI or CC-LF youth (Int: <emph>t</emph> ≥ 5.81, adj. <emph>p</emph> <.001; Ext: <emph>t</emph> ≥ 7.10, adj. <emph>p</emph> <.001), which did not differ. AT-MF youth had internalizing and externalizing symptoms below screening cutoffs, at frequencies that were significantly higher for than AT-LI or CC-LF youth for internalizing (<emph>t</emph> ≥ 3.87, adj. <emph>p</emph> ≥.005) but not externalizing behaviors (<emph>t</emph> < |1.95|; adj. <emph>p</emph> ≥.228).</p> <p> <bold>Demographics.</bold> Classes differed by male gender (χ<sups>2</sups>(<reflink idref="bib4" id="ref88">4</reflink>) = 15.66, <emph>p</emph> =.003) such that the ND-HI class contained a greater proportion of boys compared to the CC-LF class (<emph>t</emph> = 3.63, adj. <emph>p</emph> =.010). The distribution of LGBTQIA + youth also differed (χ<sups>2</sups>(<reflink idref="bib4" id="ref89">4</reflink>) = 11.96, <emph>p</emph> =.012), such that the ND-HI class had a greater proportion of LGBTQIA + youth compared to the AT-LI class (<emph>t</emph> = 3.14, adj. <emph>p</emph> =.043). Finally, parent full-time employment differed among groups (χ<sups>2</sups>(<reflink idref="bib4" id="ref90">4</reflink>) = 14.22, <emph>p</emph> =.007), such that the CC-LF class had more parents who reported working full time compared to the AT-LI class (<emph>t</emph> = 3.40, adj. <emph>p</emph> =.021). All other demographic variables did not differ significantly among classes, including geographic location of residency, socioeconomic status (SES), parent marital status, race, parent age, and child age.</p> <hd id="AN0174795722-19">Discussion</hd> <p>The aim of this study was to characterize profiles of school refusal of neurodivergent and community youth. Five profiles of school refusal in a neurodiverse sample of school-age youth were identified. Two classes included neurodivergent youth with high levels of ADHD and autistic traits and high levels of school refusal, differing in their amount of time spent in general education. Two other classes had clinical levels of autistic but not ADHD traits and engaged in moderate to low school refusal. The fifth class included community youth with low neurodivergent traits and low school refusal. These distinct profiles highlight the varied school experiences for neurodivergent youth that are related to school refusal.</p> <p>Two groups of youth with high levels of school refusal emerged (ND-HF and ND-HI), which had higher autistic and ADHD traits compared to the other classes. However, these youth were just as likely to have an autism diagnosis compared to the other neurodivergent classes with moderate or low school refusal, highlighting the crucial role of ADHD traits in explaining risk for school refusal (McClemont et al., [<reflink idref="bib33" id="ref91">33</reflink>]; Munkhaugen et al., [<reflink idref="bib40" id="ref92">40</reflink>]). Both of these high school refusal classes also had higher academic, self-care, and social support needs relative to other classes, consistent with literature reporting that support needs are often greatest for autistic youth with co-occurring ADHD (Berenguer et al., [<reflink idref="bib5" id="ref93">5</reflink>]). Here, we extend literature identifying the relationship between ADHD traits with academic and social support needs (Evans et al., [<reflink idref="bib17" id="ref94">17</reflink>]; Kawabata et al., [<reflink idref="bib26" id="ref95">26</reflink>]) by demonstrating that profiles of youth with high levels of school refusal had the highest amounts of both inattentive and hyperactive traits and academic and social support needs. These youth were also less independent in their self-care needs (e.g., toileting, dressing, feeding) compared to other classes. Thus, neurodivergent youth who are not currently receiving adequate support, or who require additional support to be successful within their environment, may be at high risk for school refusal. Finally, the two high-risk groups had significantly more externalizing behaviors compared to the other classes, whereas their internalizing behaviors were greater than the classes with low risk (but not moderate risk).</p> <p>The third class included youth with moderate school refusal (AT-MF) in the context of high autistic traits and subthreshold ADHD traits, which were still higher than for community youth. Notably, AT-MF youth were just as likely to have an ADHD diagnosis compared to the high school refusal classes (ND-HF and ND-HI), suggesting that these youth may be diagnosed with ADHD but are no longer meeting clinical thresholds for ADHD traits. Although AT-MF youth did not differ from the high-risk classes in ever having services at school, they currently received less services compared to ND-HI youth and had self-care and social support needs that, although lower than the high school refusal groups, were greater than for community youth with low school refusal (CC-LF). They also did not differ in likelihood of being prescribed ADHD medication compared to any other class. However, it is possible they participated in other supports or therapy outside of the school setting. Overall, the AT-MF class represented autistic youth in public general education without significant traits related to ADHD, internalizing, or externalizing behaviors, leaving them significantly less vulnerable to school refusal compared to youth in the high-risk profiles. In addition to the role of ADHD symptoms for autistic youth, an emerging literature base highlights the impact of behavior support needs on school refusal (McClemont et al., [<reflink idref="bib33" id="ref96">33</reflink>]), as well as internalizing symptoms (Chou et al., [<reflink idref="bib12" id="ref97">12</reflink>]; Gonzálvez et al., [<reflink idref="bib21" id="ref98">21</reflink>]; Munkhaugen et al., [<reflink idref="bib40" id="ref99">40</reflink>]), which indeed also distinguished school refusal profiles in this sample.</p> <p>These three classes of youth who displayed high and moderate rates of school refusal are further distinct in several important ways. High-risk youth in general education (ND-HF) were more likely to be bullying victims compared to each of the other classes, as well as most likely to refuse school due to bullying or mental health. However, high-risk youth in other settings (ND-HI; e.g., in self-contained classrooms or nonpublic school) and AT-MF youth also experienced more bullying compared to low-risk classes. These findings are consistent with research identifying peer victimization as a risk factor for school refusal (Bitsika et al., [<reflink idref="bib7" id="ref100">7</reflink>]; Havik et al., [<reflink idref="bib22" id="ref101">22</reflink>]) and suggest there may be additional nuance to the increased risk for peer victimization in general education (Rowley et al., [<reflink idref="bib48" id="ref102">48</reflink>]). Indeed, AT-MF youth spent a similarly high percentage of their time in public general education compared with ND-HF youth yet experienced less bullying victimization. AT-MF youth also had lower rates of hyperactive symptoms compared to ND-HF youth, with such symptoms often associated with social differences (Evans et al., [<reflink idref="bib17" id="ref103">17</reflink>]; Kawabata et al., [<reflink idref="bib26" id="ref104">26</reflink>]). These results suggest that the greatest vulnerability to school refusal within the general education setting is seen for autistic youth with more ADHD traits who are bullied and have higher self-care, academic, and social support needs. These youth are likely to have greater difficulties with peers, perhaps due to social communication differences between neurodivergent and community youth (Mitchell et al., [<reflink idref="bib34" id="ref105">34</reflink>]). In this case, bullying victimization should be monitored specifically for neurodivergent youth in general education settings.</p> <p>In addition to more ADHD traits and externalizing behaviors, neurodivergent youth with high levels of school refusal also had higher internalizing symptoms compared to low-risk classes, which is consistent with prior work (Munkhaugen et al., [<reflink idref="bib40" id="ref106">40</reflink>]). Notably, the average bullying score for both high-risk classes being above the cutoff for involvement on the ABE shows a level of peer victimization likely related to internalizing mental health concerns (Morton et al., [<reflink idref="bib35" id="ref107">35</reflink>]). This finding is perhaps unsurprising, given that internalizing symptoms are associated with bullying victimization for autistic youth (Cappadocia et al., [<reflink idref="bib11" id="ref108">11</reflink>]). However, given the cross-sectional nature of our findings, the relationships between internalizing symptoms, bullying victimization, and school refusal remain unclear. Autistic youth who are bullied are more likely to develop future internalizing problems (Rodriguez et al., [<reflink idref="bib47" id="ref109">47</reflink>]). Future research is necessary to examine whether bullying predicts school refusal and internalizing symptoms, whether internalizing symptoms independently predicts school refusal, or whether internalizing symptoms mediates the relationship between bullying victimization and school refusal.</p> <p>Overall, factors that were found to be associated with the profiles at risk for high levels of school refusal were consistent with existing studies published across different countries. Further, no profile emerged that was comprised of only international youth, and international youth were distributed fairly evenly across all five classes. Thus, findings may extend to the experience of youth in other countries, with more specific work needed in this area to gain a more comprehensive understanding of school refusal among neurodivergent youth.</p> <hd id="AN0174795722-20">Limitations</hd> <p>This study provides novel insight into profiles of school refusal behavior among neurodivergent youth; however, there are limitations. All measures were collected via parent report, which may have limited objectivity and provided a differing perspective from child report, especially in neurodivergent populations (Lerner et al., [<reflink idref="bib32" id="ref110">32</reflink>]). Relatedly, diagnoses were reported by parents, given that all data collection occurred virtually; however, online report does not differ significantly from clinician-confirmed diagnoses (Fombonne et al., [<reflink idref="bib18" id="ref111">18</reflink>]). However, this online methodology is also a strength in that any parent with Internet access could participate without restriction by geographical location or transportation. We acknowledge that using convenience sampling limits generalizability, as parents may have self-selected to participate based on knowledge or experience with the topic of peer victimization, which itself has been associated with school refusal in neurodivergent and neurotypical youth (Bitsika et al., [<reflink idref="bib7" id="ref112">7</reflink>]; McClemont et al., [<reflink idref="bib33" id="ref113">33</reflink>]; Vidourek et al., [<reflink idref="bib52" id="ref114">52</reflink>]). As such, prevalence rates of school refusal and bullying within this sample may not reflect the broader community or neurodivergent populations. Although the community comparison group did not report diagnoses of autism or ADHD and had results below clinical cutoffs on related measures, it is also possible that parents of youth with other related diagnoses (e.g., learning disabilities) or with a connection to the neurodivergent community (i.e., special education teachers, extended family members) were more likely to self-select for participation and as such may not fully represent the general population. Finally, the definition of school refusal provided to parents when asking them about the frequency of their child's school refusal in the past year was intentionally limited, given ambiguity in the operationalization of this construct in the literature. However, parents may have varied in their interpretation of the behavior and subsequent responding. It is also possible that some parents interpreted the "past school year" as the past few months within the present school year, whereas other parents recalled school refusal from the previous academic year. It is possible that differences in the amount of referenced time impacted parents' ability to accurately recall their child's school refusal.</p> <hd id="AN0174795722-21">Future Directions</hd> <p>Identifying youth at increased risk for school refusal might allow for earlier intervention to assist in preventing chronic absenteeism, which commonly follows school refusal (Totsika et al., [<reflink idref="bib50" id="ref115">50</reflink>]). Longitudinal studies exploring variables associated with school refusal in the current study are needed to provide further insight into causal and high-risk factors. Teacher and child reports of school refusal and environmental factors are also needed, as parent knowledge of their child's school experiences can be limited. Researchers and educators should consider how different reasons for school refusal interface with the support needs of neurodivergent youth, to potentially inform more individualized approaches to improving school engagement.</p> <hd id="AN0174795722-22">Conclusion</hd> <p>Neurodivergent youth are at increased risk for school refusal, yet associated variables are poorly understood, hindering identification of "at-risk" students. These findings offer insight into factors related to school refusal, including particular vulnerability for youth with greater autistic and ADHD characteristics, suggesting an advantage of dimensional examination as opposed to diagnostic categorization. Internalizing and externalizing symptoms, bullying victimization, and academic and social support needs should also be considered within the context of the school setting, and providing needed supports may help to maintain or improve school engagement.</p> <hd id="AN0174795722-23">Declarations</hd> <p></p> <hd id="AN0174795722-24">Conflicts of Interest</hd> <p>The authors have no relevant financial or nonfinancial interests to disclose.</p> <hd id="AN0174795722-25">Consent to Participate</hd> <p>Informed consent was obtained from all individual participants included in the study.</p> <hd id="AN0174795722-26">Ethics Approval</hd> <p>This study was approved by the IRB at the authors' university. 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Autism: The International Journal of Research and Practice, 18 (4), 419 – 427. https://doi.org/10.1177/1362361313477920</bibtext> </blist> </ref> <aug> <p>By Jessica E. Granieri; Hannah E. Morton; Raymond G. Romanczyk and Jennifer M. Gillis Mattson</p> <p>Reported by Author; Author; Author; Author</p> <p></p> <p>Jessica Granieri is currently a fourth year clinical psychology doctoral student at Binghamton University. Her research relates to the social experiences of autistic youth across different contexts, including educational settings.</p> <p>Hannah Morton is a postdoctoral fellow at Oregon Health and Science University. Her research examines peer interactions among neurodivergent youth to inform their support needs and develop acceptance-based interventions.</p> <p>Raymond Romanczyk is a SUNY Distinguished Service Professor at Binghamton University in the doctoral clinical psychology program. His research interests focus upon child disorders.</p> <p>Jennifer Gillis Mattson is a Professor of Psychology at Binghamton University. Her research interests focus broadly upon the experiences of autistic individuals across a range of ages.</p> </aug> <nolink nlid="nl1" bibid="bib28" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib51" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib40" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib45" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib19" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib27" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib49" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib50" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib39" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib29" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib33" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib16" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib20" firstref="ref20"></nolink> <nolink nlid="nl14" bibid="bib23" firstref="ref21"></nolink> <nolink nlid="nl15" bibid="bib55" firstref="ref24"></nolink> <nolink nlid="nl16" bibid="bib24" firstref="ref28"></nolink> <nolink nlid="nl17" bibid="bib12" firstref="ref31"></nolink> <nolink nlid="nl18" bibid="bib17" firstref="ref32"></nolink> <nolink nlid="nl19" bibid="bib56" firstref="ref33"></nolink> <nolink nlid="nl20" bibid="bib15" firstref="ref35"></nolink> <nolink nlid="nl21" bibid="bib52" firstref="ref36"></nolink> <nolink nlid="nl22" bibid="bib11" firstref="ref41"></nolink> <nolink nlid="nl23" bibid="bib31" firstref="ref43"></nolink> <nolink nlid="nl24" bibid="bib48" firstref="ref45"></nolink> <nolink nlid="nl25" bibid="bib57" firstref="ref51"></nolink> <nolink nlid="nl26" bibid="bib38" firstref="ref52"></nolink> <nolink nlid="nl27" bibid="bib42" firstref="ref53"></nolink> <nolink nlid="nl28" bibid="bib25" firstref="ref54"></nolink> <nolink nlid="nl29" bibid="bib30" firstref="ref55"></nolink> <nolink nlid="nl30" bibid="bib14" firstref="ref58"></nolink> <nolink nlid="nl31" bibid="bib10" firstref="ref59"></nolink> <nolink nlid="nl32" bibid="bib54" firstref="ref60"></nolink> <nolink nlid="nl33" bibid="bib36" firstref="ref62"></nolink> <nolink nlid="nl34" bibid="bib35" firstref="ref64"></nolink> <nolink nlid="nl35" bibid="bib44" firstref="ref65"></nolink> <nolink nlid="nl36" bibid="bib13" firstref="ref67"></nolink> <nolink nlid="nl37" bibid="bib37" firstref="ref68"></nolink> <nolink nlid="nl38" bibid="bib46" firstref="ref69"></nolink> <nolink nlid="nl39" bibid="bib53" firstref="ref70"></nolink> <nolink nlid="nl40" bibid="bib41" firstref="ref71"></nolink> <nolink nlid="nl41" bibid="bib43" firstref="ref72"></nolink> <nolink nlid="nl42" bibid="bib26" firstref="ref95"></nolink> <nolink nlid="nl43" bibid="bib21" firstref="ref98"></nolink> <nolink nlid="nl44" bibid="bib22" firstref="ref101"></nolink> <nolink nlid="nl45" bibid="bib34" firstref="ref105"></nolink> <nolink nlid="nl46" bibid="bib47" firstref="ref109"></nolink> <nolink nlid="nl47" bibid="bib32" firstref="ref110"></nolink> <nolink nlid="nl48" bibid="bib18" firstref="ref111"></nolink>
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  Data: Profiles of School Refusal among Neurodivergent Youth
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  Data: <searchLink fieldCode="AR" term="%22Jessica+E%2E+Granieri%22">Jessica E. Granieri</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5745-1342">0000-0002-5745-1342</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hannah+E%2E+Morton%22">Hannah E. Morton</searchLink><br /><searchLink fieldCode="AR" term="%22Raymond+G%2E+Romanczyk%22">Raymond G. Romanczyk</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9194-1209">0000-0001-9194-1209</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jennifer+M%2E+Gillis+Mattson%22">Jennifer M. Gillis Mattson</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22European+Education%22"><i>European Education</i></searchLink>. 2023 55(3-4):186-201.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 16
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  Data: 2023
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Truancy%22">Truancy</searchLink><br /><searchLink fieldCode="DE" term="%22Profiles%22">Profiles</searchLink><br /><searchLink fieldCode="DE" term="%22Bullying%22">Bullying</searchLink><br /><searchLink fieldCode="DE" term="%22Peer+Relationship%22">Peer Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Severity+%28of+Disability%29%22">Severity (of Disability)</searchLink>
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  Data: 10.1080/10564934.2023.2251013
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  Data: 1056-4934<br />1944-7086
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  Data: Neurodivergent youth (i.e., autistic, attention-deficit hyperactivity disorder) are at increased risk for school refusal and subsequent disengagement. Factors associated with school refusal in this population remain unclear. Latent profile analysis was conducted to identify profiles of school and demographic variables associated with parent-reported school refusal for 508 neurodivergent and community youth (ages 6-17 years). Five profiles were identified, including three groups characterized by frequent school refusal, high levels of neurodivergent traits, and frequent peer victimization. Differentiation was noted via educational placement, support needs, mental health, and bullying. Implications concern identification and intervention for subgroups of neurodivergent youth.
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      – SubjectFull: Adolescents
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      – SubjectFull: Bullying
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      – SubjectFull: Severity (of Disability)
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      – TitleFull: Profiles of School Refusal among Neurodivergent Youth
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