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Students' Mental Health Profiles and Their Association with Health Behaviors and School Satisfaction in Dubai-Based British Curriculum Schools

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Title: Students' Mental Health Profiles and Their Association with Health Behaviors and School Satisfaction in Dubai-Based British Curriculum Schools
Language: English
Authors: Collin A. Webster (ORCID 0000-0003-1680-9149), Diana Mîndrila (ORCID 0000-0001-8374-9524), Anthony D. Murphy, Ivana Banicevic, Dušan Peric, Dragan Stankic, Željko Banicevic
Source: Psychology in the Schools. 2025 62(10):4023-4040.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 18
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Descriptors: Foreign Countries, International Schools, Elementary School Students, Mental Health, Profiles, Health Behavior, Student Satisfaction, Physical Activities, Computer Use, Sleep, Dietetics, Age, Race, Ethnicity, Predictor Variables
Geographic Terms: United Arab Emirates, United Kingdom (Great Britain)
DOI: 10.1002/pits.23592
ISSN: 0033-3085
1520-6807
Abstract: Health behavior, mental health, and school satisfaction are associated in school-aged youth. However, most previous research examining such associations does not account for unique groupings of these variables based on person-centered analyses. This study examined student mental health profiles and their association with health behaviors and school satisfaction. Students (N = 315, M[subscript age] = 11.39) from two British curriculum schools in Dubai, the United Arab Emirates, self-reported their mental health, physical activity, screentime, sleep quality, dietary habits, and school satisfaction. LPA revealed a four-profile solution as an optimal fit to the data. Significant differences in profile membership were found based on students' health behavior profiles, health behaviors, school satisfaction, age, and race/ethnicity. All health behaviors except physical activity predicted mental health profiles, and mental health profiles predicted school satisfaction. This study provides initial evidence of distinct mental health profiles that may require customized support in health programming for students in Dubai-based British curriculum schools.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1483564
Database: ERIC
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  Value: <anid>AN0187949514;pis01oct.25;2025Sep16.03:20;v2.2.500</anid> <title id="AN0187949514-1">Students' Mental Health Profiles and Their Association With Health Behaviors and School Satisfaction in Dubai‐Based British Curriculum Schools </title> <p>Health behavior, mental health, and school satisfaction are associated in school‐aged youth. However, most previous research examining such associations does not account for unique groupings of these variables based on person‐centered analyses. This study examined student mental health profiles and their association with health behaviors and school satisfaction. Students (N = 315, Mage = 11.39) from two British curriculum schools in Dubai, the United Arab Emirates, self‐reported their mental health, physical activity, screentime, sleep quality, dietary habits, and school satisfaction. LPA revealed a four‐profile solution as an optimal fit to the data. Significant differences in profile membership were found based on students' health behavior profiles, health behaviors, school satisfaction, age, and race/ethnicity. All health behaviors except physical activity predicted mental health profiles, and mental health profiles predicted school satisfaction. This study provides initial evidence of distinct mental health profiles that may require customized support in health programming for students in Dubai‐based British curriculum schools.</p> <p>Summary: Mental health profiles were identified for students in Dubai‐based British schools.Profiles varied depending on students' health behaviors, school satisfaction, age, and race/ethnicity.Students' screentime, sleep quality, and diet predicted profile membership, which in turn predicted school satisfaction.</p> <p>Keywords: academic performance; adolescents; children; strengths and difficulties</p> <p>The World Health Organization (WHO) defines mental health (MH) as "a state of well‐being that enables people to cope with the stresses of life, realize their abilities, learn and work well, and contribute to their community" (World Health Organization [<reflink idref="bib69" id="ref1">69</reflink>]). MH plays a critical role in childhood, as it supports adaptive social and emotional development and promotes optimum functioning in school, at home, and in the community (Centers for Disease Control and Prevention [<reflink idref="bib9" id="ref2">9</reflink>]). Unfortunately, 14% of youth ages 10–19 globally experience MH conditions, which can lead to numerous consequences such as social exclusion, educational difficulties, and risk‐taking behaviors (World Health Organization [<reflink idref="bib69" id="ref3">69</reflink>]). The academic challenges associated with MH problems are well documented (Halpern‐Manners et al. [<reflink idref="bib21" id="ref4">21</reflink>]). In a longitudinal study, behavioral and emotional problems at age 3 were associated with performing below grade level at age 12, while conduct and emotional problems at age 12 were associated with non‐eligibility for university studies after completing secondary education (Agnafors et al. [<reflink idref="bib1" id="ref5">1</reflink>]).</p> <p>When considering the connection between MH and academic success in children and adolescents, school satisfaction has emerged as an important area of focus. School satisfaction is conceptualized as one dimension of a child's relationship to school (Libbey [<reflink idref="bib31" id="ref6">31</reflink>]). In previous research, higher school satisfaction was linked to lower emotional distress and reduced internalizing and externalizing behaviors (DeSantis King et al. [<reflink idref="bib13" id="ref7">13</reflink>]; Jovаnovic and Jerkovic [<reflink idref="bib26" id="ref8">26</reflink>]), as well as to lower truancy (Horanicova et al. [<reflink idref="bib22" id="ref9">22</reflink>]) and higher academic achievement (Jovаnovic and Jerkovic [<reflink idref="bib26" id="ref10">26</reflink>]). The prevailing approach to understanding the relationship between MH and school satisfaction has been to consider how school satisfaction may promote MH (e.g., Cavioni et al. [<reflink idref="bib8" id="ref11">8</reflink>]; Horanicova et al. [<reflink idref="bib22" id="ref12">22</reflink>]; State and Kern [<reflink idref="bib59" id="ref13">59</reflink>]; Suldo et al. [<reflink idref="bib61" id="ref14">61</reflink>]). This perspective highlights the importance of adaptive school experiences (e.g., having a positive attitude toward school, feeling a strong sense of belonging in the school environment, experiencing positive relationships with teachers and peers) in determining MH outcomes. However, there also may be utility in investigating students' MH as an antecedent variable in models of school satisfaction. Baker et al. ([<reflink idref="bib3" id="ref15">3</reflink>]) proposed a developmental ecological perspective of healthy school environments in which students' healthy adjustment to school (e.g., school satisfaction) can be explained by how well the school environment accommodates students' individual needs (e.g., supports MH). Taking this view, it could be expected that students with different MH needs experience different degrees of school satisfaction, with higher satisfaction achieved through schools' provision of appropriately tailored and individualized student support. Yet, since Baker et al.'s (2003) work, scant empirical or theoretical literature has further explored students' MH as a determinant of their school satisfaction.</p> <p>In line with Baker et al.'s (2003) developmental ecological perspective, it is not only important to consider the role of MH in determining school satisfaction, but also to identify modifiable factors that can leveraged to support students' individual MH needs, ultimately leading to increased school satisfaction. While research has uncovered a wide array of individual and environmental factors associated with the MH of children and adolescents (e.g., Mudunna et al. [<reflink idref="bib37" id="ref16">37</reflink>]), mounting evidence demonstrates that students' health behaviors constitute a critical intervention target for MH support within school systems. Researchers have reported associations between MH and physical activity (Bell et al. [<reflink idref="bib5" id="ref17">5</reflink>]; Guddal et al. [<reflink idref="bib19" id="ref18">19</reflink>]), screentime (Sampasa‐Kanyinga et al. [<reflink idref="bib53" id="ref19">53</reflink>]; Sánchez‐Miguel et al. [<reflink idref="bib55" id="ref20">55</reflink>]), sleep (Blake et al. [<reflink idref="bib6" id="ref21">6</reflink>]; Musshafen et al. [<reflink idref="bib40" id="ref22">40</reflink>]; Short et al. [<reflink idref="bib58" id="ref23">58</reflink>]), and diet (Due et al. [<reflink idref="bib14" id="ref24">14</reflink>]; Hosker et al. [<reflink idref="bib23" id="ref25">23</reflink>]; Jacka et al. [<reflink idref="bib24" id="ref26">24</reflink>]; Kohlboeck et al. [<reflink idref="bib28" id="ref27">28</reflink>]; O'Neil et al. [<reflink idref="bib47" id="ref28">47</reflink>]; Valois et al. [<reflink idref="bib64" id="ref29">64</reflink>]) in school‐aged youth. An emphasis on these health behaviors is congruent with the Health Promoting Schools model, which focuses on factors within school systems (e.g., policies, facilities, curriculum, teaching practices, community partnerships) that can be modified to positively impact students' health (Jones et al. [<reflink idref="bib25" id="ref30">25</reflink>]; Lowry et al. [<reflink idref="bib33" id="ref31">33</reflink>]). However, Lowry et al. ([<reflink idref="bib33" id="ref32">33</reflink>]) note that research examining the efficacy of the Health Promoting Schools model in improving students' MH outcomes has yielded mixed results, and the authors call for future initiatives employing the model to adopt a more individualized approach to modifying school determinants of health.</p> <p>Given the need to understand how to customize MH support for individual students, person‐centered research approaches must be utilized to advance beyond examining relationships between variables at an aggregate level (Von Eye and Wiedermann [<reflink idref="bib16" id="ref33">16</reflink>]). In contrast to variable‐centered analyses, which assume homogeneity among participants and a central tendency for scores to be close to the average, person‐centered analyses assume heterogeneity among participants and scores that reflect individual differences (Saqr et al. [<reflink idref="bib56" id="ref34">56</reflink>]). Person‐centered research reveals how variables group within individuals and combine into distinct clusters or profiles across a sample (Von Eye and Wiedermann [<reflink idref="bib16" id="ref35">16</reflink>]). Identifying unique patterns of health behavior and MH among students is critical to creating school programming that is more targeted and better matched to students with different needs. Person‐centered approaches have been used in several recent studies with school‐aged youth to identify groupings of health behaviors and demonstrate how group membership plays a significant role in MH (Bang et al. [<reflink idref="bib4" id="ref36">4</reflink>]; Mahon et al. [<reflink idref="bib35" id="ref37">35</reflink>]; Webster et al. [<reflink idref="bib66" id="ref38">66</reflink>]; Wilhite et al. [<reflink idref="bib67" id="ref39">67</reflink>]). In addition, students' demographic characteristics were found to be significant factors in group membership, with older participants (Mahon et al. [<reflink idref="bib35" id="ref40">35</reflink>]; Webster et al. [<reflink idref="bib66" id="ref41">66</reflink>]) and boys belonging to healthier profile groups (Mahon et al. [<reflink idref="bib35" id="ref42">35</reflink>]) than younger participants and girls. However, to expand on this study, it may be useful to also identify MH profiles and investigate their relationship to health behavior profiles, as this could provide more detailed, individual‐level information for intervention purposes. Furthermore, examining associations between MH profiles, specific health behaviors (e.g., physical activity, diet), and school satisfaction would extend theory as it pertains to this line of inquiry by highlighting how distinct MH groupings may associate differently with possible determinants and outcomes of children's MH.</p> <p>The present study builds on our previous research in which we examined health behavior profiles and their associations with MH and school satisfaction in a sample of students attending British curriculum schools in Dubai, the United Arab Emirates (Webster et al. [<reflink idref="bib66" id="ref43">66</reflink>]). The UAE's National Policy for the Promotion of Mental Health identifies strategic objectives that include promoting multisectoral collaboration, addressing mental disorders for people of all ages, and strengthening research to inform MH services (United Arab Emirates Government Portal [<reflink idref="bib63" id="ref44">63</reflink>], October 21). MH challenges are more prevalent in Dubai than in the other emirates (Marquez et al. [<reflink idref="bib36" id="ref45">36</reflink>]). Moreover, compared to other types of schools in the UAE, British schools were reported to have the worst well‐being and MH outcomes (Marquez et al. [<reflink idref="bib36" id="ref46">36</reflink>]). Understanding how to support students' MH in the context of Dubai‐based British schools is therefore a critical area of investigative focus that can elucidate effective MH services within the education sector and advance national policy implementation.</p> <p>Using the same data from the sample in our previous investigation (Webster et al. [<reflink idref="bib66" id="ref47">66</reflink>]), our aim in the current study was to explore students' MH profiles and their associations with health behavior profiles, specific health behaviors, demographic characteristics, and school satisfaction. This study was guided by the following research questions and hypotheses:</p> <p></p> <ulist> <item> 1. What latent profiles of MH underlie the data? This question was exploratory and therefore no hypotheses were posed.</item> <p></p> <item> 2. Do MH profiles differ by health behavior profiles, specific health behaviors, school satisfaction, and demographic characteristics (age, gender, and race/ethnicity)? This question was mostly exploratory, although our previous study (Webster et al. [<reflink idref="bib66" id="ref48">66</reflink>]) and the study by Mahon et al. ([<reflink idref="bib35" id="ref49">35</reflink>]) led us to expect that healthier MH profiles would include more older students and boys than younger students and girls.</item> <p></p> <item> 3. Are specific health behaviors significant predictors of MH profiles? In line with Baker et al.'s (2003) developmental ecological perspective and empirical support for health behaviors as modifiable factors associated with children's MH (Bell et al. [<reflink idref="bib5" id="ref50">5</reflink>]; Blake et al. [<reflink idref="bib6" id="ref51">6</reflink>]; Due et al. [<reflink idref="bib14" id="ref52">14</reflink>]; Guddal et al. [<reflink idref="bib19" id="ref53">19</reflink>]; Hosker et al. [<reflink idref="bib23" id="ref54">23</reflink>]; Jacka et al. [<reflink idref="bib24" id="ref55">24</reflink>]; Kohlboeck et al. [<reflink idref="bib28" id="ref56">28</reflink>]; Musshafen et al. [<reflink idref="bib40" id="ref57">40</reflink>]; O'Neil et al. [<reflink idref="bib47" id="ref58">47</reflink>]; Sampasa‐Kanyinga et al. [<reflink idref="bib53" id="ref59">53</reflink>]; Sánchez‐Miguel et al. [<reflink idref="bib55" id="ref60">55</reflink>]; Short et al. [<reflink idref="bib58" id="ref61">58</reflink>]; Valois et al. [<reflink idref="bib64" id="ref62">64</reflink>]), it was hypothesized that physical activity, screentime, sleep, and diet would be significant predictors of students' MH profiles in this study.</item> <p></p> <item> 4. Are MH profiles significant predictors of school satisfaction? Based on Baker et al.'s (2003) developmental perspective, which views school satisfaction as an outcome of appropriately individualized support of students' needs (evidenced in part by MH), it was hypothesized that MH profiles would significantly predict school satisfaction.</item> </ulist> <hd id="AN0187949514-2">Methods</hd> <p></p> <hd id="AN0187949514-3">Participants and Setting</hd> <p>Participants in this study were 315 students who were convenience sampled from two British curriculum schools in Dubai, UAE. Students' average age was 11.39 (SD = 2.045). The proportions of students identifying as females (48.5%) were close to the proportion of students identifying as males (44.7%); the remaining participants identified their gender as "other" (4.1%) or preferred not to identify their gender (2.7%). For race/ethnicity, students identified as White (33.2%), "other" (26.0%), Asian (16.8%), Black or African American (6.8%), Hispanic or Latino (4.8%), American Indian or Alaska Native (2.1%), or Hawaiian or other Pacific Islander (0.4%); the remaining participants preferred not to identify their race (9.9%). Both schools were private, independent schools serving students ages 3–18. At the time data were collected, total student enrollment was 1020 and 1290 for the first and second schools, respectively.</p> <hd id="AN0187949514-4">Instrumentation</hd> <p></p> <hd id="AN0187949514-5">Physical Activity</hd> <p>The Health Behavior in School‐aged Children Research Protocol (Currie et al. [<reflink idref="bib11" id="ref63">11</reflink>]) was used to assess students' physical activity. A definition of physical activity, consistent with current international consensus guidelines for school‐aged youth (World Health Organization [<reflink idref="bib70" id="ref64">70</reflink>]), is provided in a stem that precedes the items. The stem reads, "In the next two questions, physical activity means all activities which raise your heart rate or momentarily get you out of breath, for example, doing exercise, playing with your friends, going to school, or in school PE. Sport also includes, for example, jogging, intensive walking, roller skating, cycling, dancing, skating, skiing, soccer, basketball, and baseball." The two items that follow are, "Think about your typical week. How many days did you exercise for at least 60 min during which you got out of breath?" and "Think about your last 7 days. How many days did you exercise for at least 60 min during which you got out of breath?" Response options fall along an eight‐point scale (0–7 days per week). Following Kokkonen et al. ([<reflink idref="bib29" id="ref65">29</reflink>]), we summed the responses from both items and used the sum score in data analysis as an indicator of total moderate‐to‐vigorous physical activity. Previous studies reported moderate or higher test‐retest reliability for the physical activity measure, with coefficients ranging from 0.50 to 0.80 (Su et al. [<reflink idref="bib60" id="ref66">60</reflink>]). Conclusions concerning validity are not possible, given scarce evidence; however, the measure is commonly used with children and adolescents (Su et al. [<reflink idref="bib60" id="ref67">60</reflink>]) and its brief nature was an important consideration in the current study to reduce testing burden for participants.</p> <hd id="AN0187949514-6">Screentime</hd> <p>Consistent with Sampasa‐Kanyinga et al. ([<reflink idref="bib54" id="ref68">54</reflink>]), students' screentime was assessed using a single item: "Think about the last 7 days. How many hours a day, on average, did you spend watching TV/movies/videos, playing video games, texting, messaging, posting, or surfing the Internet in your free time? This includes time on any screen, such as a smartphone, tablet, TV, gaming device, computer, or wearable technology." Response options ranged from 0 to "more than 10." The measure aligns with current public health guidelines related to children's recreational screentime (World Health Organization [<reflink idref="bib70" id="ref69">70</reflink>]). Self‐report measures of children and adolescents' screentime, including single‐item assessments, have been shown to have acceptable validity and reliability (Lubans et al. [<reflink idref="bib34" id="ref70">34</reflink>]; Schmitz et al. [<reflink idref="bib57" id="ref71">57</reflink>]). For example, Schmitz et al. ([<reflink idref="bib57" id="ref72">57</reflink>]) reported a Kappa coefficient of 0.55 and a Spearman correlation coefficient of 0.457 for the single item assessing television viewing from the Youth Risk Behavior Survey, indicating moderate levels of reliability and validity, respectively.</p> <hd id="AN0187949514-7">Sleep</hd> <p>Students' sleep quality was assessed using a revised, brief version of the Adolescent Sleep‐Wake Scale (Essner et al. [<reflink idref="bib15" id="ref73">15</reflink>]), which includes 10 items that measure three factors: Falling Asleep and Reinitiating Sleep‐Revised (5 items, e.g., "After waking up during the night, I have trouble going back to sleep"), Returning to Wakefulness‐Revised (2 items, e.g., "In the morning, I wake up feeling rested and alert"), and Going to Bed‐Revised (3 items, e.g., "In general, I am ready for bed at bedtime"). Participants could choose one of six response options that ranged from "Never True" to "Always True." Negatively worded items were reverse‐scored and responses across all 10 items were aggregated for a total measure of sleep quality. Essner et al. ([<reflink idref="bib15" id="ref74">15</reflink>]) provided evidence of construct validity (i.e., a three‐factor solution) and acceptable internal consistencies with Cronbach's coefficient α's ranging from.78 to 0.81. Although the scale is designed for adolescents, it has conceptual similarity to a version developed for assessing sleep quality in younger children (ages 2–8; LeBourgeois and Harsh [<reflink idref="bib30" id="ref75">30</reflink>]). With the current sample, the scale had a Cronbach's α of 0.797.</p> <hd id="AN0187949514-8">Diet</hd> <p>Students' dietary habits were assessed using a 10‐item questionnaire (Perić and Romanov [<reflink idref="bib49" id="ref76">49</reflink>]). Originally designed for adults, the instrument measures two factors: Time and Jobs Management (six items, e.g., "I usually do not eat dinner during the workdays"), and Knowledge and Self‐Control (four items, e.g., "Fast food/street food is a good solution for my daily diet"). In consultation with the instrument developer, the measure was deemed appropriate for the current study with a slight modification (i.e., changing "workdays" to "schooldays"). Participants used a five‐point response scale that ranged from "Never True" to "Always True." Where needed, items were reverse‐scored, and then responses across all 10 items were summed to create a total diet score to be consistent with our previous research (Webster et al. [<reflink idref="bib66" id="ref77">66</reflink>]). Perić and Romanov ([<reflink idref="bib49" id="ref78">49</reflink>]) confirmed the factor structure and reported acceptable reliability (<emph>α</emph> = 0.709) for the measure. The Cronbach's α of the scale in the present study was 0.618.</p> <hd id="AN0187949514-9">MH</hd> <p>The baseline version of the Youth Self‐Report Strengths and Difficulties Questionnaire, designed for children ages 11–17, was used to assess students' MH (Goodman et al. [<reflink idref="bib18" id="ref79">18</reflink>]). Participants are asked to respond in relation to the past 6 months. A total of 25 items are included, which measure five factors: Emotional Symptoms (5 items, e.g., "I am often unhappy, downhearted, or tearful"), Conduct Problems (5 items, e.g., "I get very angry and often lose my temper"), Hyperactivity/Inattention (5 items, e.g., "I am easily distracted. I find it difficult to concentrate"), Peer Relationships (5 items, e.g., "I am usually on my own. I generally play alone or keep to myself"), and Prosocial Behavior (5 items, e.g., "I try to be nice to other people. I care about their feelings"). There are three response options: Not True, Somewhat True, and Certainly True. Negatively worded items were reverse‐scored, and a sum score was calculated for each factor. Psychometric support for the measure was reported in previous studies with children and adolescents (Goodman [<reflink idref="bib17" id="ref80">17</reflink>]; Goodman et al. [<reflink idref="bib18" id="ref81">18</reflink>]; Muris et al. [<reflink idref="bib38" id="ref82">38</reflink>]; Van Roy et al. [<reflink idref="bib51" id="ref83">51</reflink>]). For example, Goodman et al. ([<reflink idref="bib18" id="ref84">18</reflink>]) found a Cronbach's α of 0.82 for the full scale administered with a sample of participants ages 11–16, while Muris et al. ([<reflink idref="bib39" id="ref85">39</reflink>]) found sufficient and comparable criterion validity, inter‐rater agreement, and convergent validity for participants who were 8–10 and 11–13 years old. Cronbach's α for the total measure with the current study sample was 0.841 (0.708 for Emotional Symptoms, 0.681 for Conduct Problems, 0.694 for Hyperactivity/Inattention, 0.590 for Peer Relationships, and 0.718 for Prosocial Behavior). As it is not uncommon for multi‐construct scales measuring affective qualities (e.g., social and emotional characteristics) to show higher internal consistency than their subscales (Taber [<reflink idref="bib62" id="ref86">62</reflink>]), the measure was deemed acceptable for the present study (Bland and Altman [<reflink idref="bib7" id="ref87">7</reflink>]).</p> <hd id="AN0187949514-10">School Satisfaction</hd> <p>A three‐item measure from a previous version of the Health Behavior in School‐aged Children Research Protocol (Wold et al. [<reflink idref="bib68" id="ref88">68</reflink>]) was used to assess students' satisfaction with school. The items were "I like school," "School is a nice place to be," and "Going to school is boring." The third item was reverse‐scored, and all three items were summed to create a composite score for data analysis. The full survey protocol was extensively piloted in 13 countries with Cronbach's α's ranging from 0.58 to 0.81 for the school satisfaction measure (Samdal et al. [<reflink idref="bib52" id="ref89">52</reflink>]). With the current study sample, the three‐item scale had a Cronbach's α of 0.788.</p> <p>Upon request, the authors can provide the complete measurement instruments used in the study.</p> <hd id="AN0187949514-11">Procedures</hd> <p>Before data collection, we obtained approval to conduct the study from the Ethics Board of the University of Birmingham, and we secured informed consent from students (18 or older) or students' parents in accordance with Dubai government regulations. An online survey was developed and administered using JISC (Joint Information Systems Committee, jisc.ac.uk, Bristol, England). The survey contained instructions for participants, the previously mentioned measures, and three demographic items to collect information about students' age, gender, and race/ethnicity. We used an established partner network of schools for study recruitment. A volunteer teacher at each participating school assisted with study logistics, such as communication with parents and teachers, collecting informed consent forms, distributing the survey link to teachers, and scheduling the administration of the survey, which was open for a 2‐week period in the fall of the year. Teachers were encouraged to assist students, as needed, during its administration. This was especially important in helping to ensure that younger children in the study were able to comprehend instructions, correctly interpret survey items, and respond appropriately using the available response options for each measure.</p> <hd id="AN0187949514-12">Data Analysis</hd> <p>Before analyzing the data, we examined the distribution of missing data points. Missing values ranged between 0.0% and 7.6% per variable and were distributed completely at random (Little's MCAR χ<sups>2</sups><subs>(<reflink idref="bib121" id="ref90">121</reflink>)</subs> = 91.790, <emph>p</emph> = 0.978); therefore, we employed the expectation‐maximization algorithm to impute missing values. Furthermore, we calculated measures of spread and location such as the minimum (Min), maximum (Max), mean (M), standard deviation (SD), and indices of skewness and kurtosis.</p> <p>The next step was calculating composite MH scores for items measuring emotional problems (MH_EmProb—5 items), conduct problems (MH_ConProb—5 items), hyperactivity/inattention (MH_HypAct—5 items), peer problems (MH_PeerProb—5 items), prosocial behavior (MHProSoc—5 items), physical activity (PA—2 items), sleep quality (sleep—10 items), dietary habits (diet—10 items), and school satisfaction (SS—3 items). Composite scores were further examined using descriptive statistics and standardized as <emph>z</emph> scores (<emph>M</emph> = 0, SD = 1) for further analyses. Confirmatory factor analysis (CFA) goodness of fit indices such as the χ<sups>2</sups> statistic, the root mean square error of approximation and its 90% confidence interval (RMSEA <subs>(90%CI)</subs>), the comparative fit index (CFI), the Tucker‐Lewis fit index (TLI), and the standardized root mean square residual (SRMR) were used to assess the fit of the MH, sleep, and diet scales to the data.</p> <hd id="AN0187949514-13">Latent Profile Analysis (LPA)</hd> <p>LPA groups individuals who have a set of characteristics in common (Collins and Lanza [<reflink idref="bib10" id="ref91">10</reflink>]). Multiple studies indicate that a sample of 300 cases or more meets the LPA requirements (Nylund et al. [<reflink idref="bib45" id="ref92">45</reflink>]; Guilford [<reflink idref="bib20" id="ref93">20</reflink>]; Nunnaly [<reflink idref="bib44" id="ref94">44</reflink>]; Nylund‐Gibson and Choi [<reflink idref="bib46" id="ref95">46</reflink>]). Furthermore, we used Monte Carlo simulations with 500 replications to estimate power (Muthén and Muthén [<reflink idref="bib42" id="ref96">42</reflink>]). Results indicated that a sample size of 255 was sufficient to achieve 0.95 power for the proposed LPA model with no missing values and normally distributed data.</p> <p>In our previous research, we identified four health behavior latent profiles for the students in our sample (Webster et al. [<reflink idref="bib66" id="ref97">66</reflink>]). These profiles included Poor Diet and Sleep (low factor scores on diet and sleep and close to average scores on physical activity and screentime), Average Health Behaviors (factors scores close to average for all behaviors), High Health Behaviors (high factor scores for physical activity, diet, and sleep and low factor scores for screentime), and Low Health Behaviors (low factor scores for physical activity, diet, and sleep and high factor scores for screentime).</p> <p>For the current study, participants' standardized MH scale scores (MH_EmProb, MH_ConProb, MH_HypAct, MH_PeerProb, MHProSoc) were specified as the observed indicators of a latent categorical variable MH. We estimated and compared models with three, four, and five profiles and examined goodness of fit indices, measures of classification precision, and the interpretability of the latent profiles to select an optimal model. We used the following indices of model fit: (a) the Akaike Information Criteria (AIC), (b) the Bayesian Information Criteria (BIC), and (c) the sample size‐adjusted BIC. These indices measure model parsimony and lower values indicate better fit to the data (Muthen [<reflink idref="bib41" id="ref98">41</reflink>]; Vermunt and Magidson [<reflink idref="bib65" id="ref99">65</reflink>]). In addition, we employed the Lo‐Mendell Rubin adjusted likelihood ratio test, which indicates whether increasing the number of latent profiles by one significantly improves model fit (Lo [<reflink idref="bib32" id="ref100">32</reflink>]; Nylund et al. [<reflink idref="bib45" id="ref101">45</reflink>]).</p> <p>Measures of classification precision were (a) average latent profile probabilities for most likely latent profile membership, and (b) classifications probabilities for most likely latent profile membership, and (c) entropy. Average latent profile probabilities and classification probabilities for most likely profile membership are the proportions of correctly classified cases in each group. Entropy takes values between 0 and 1; higher values indicate higher overall levels of classification certainty (Ramaswamy et al. [<reflink idref="bib50" id="ref102">50</reflink>]; Vermunt and Magidson [<reflink idref="bib65" id="ref103">65</reflink>]).</p> <p>MH latent profiles were further described by cross tabulating profile memberships and previously identified HB profile memberships (Webster et al. [<reflink idref="bib66" id="ref104">66</reflink>]) and comparing MH profiles by specific health behaviors (PA, screentime, sleep, and diet), specific MH indices (subscale composite scores), SS, and demographic information (gender, race, age). We used the χ<sups>2</sups> test to examine associations between categorical variables employing Monté Carlo simulations with 10,000 sampled tables and a 99% confidence interval (CI) for contingency tables with expected cell counts lower than five. Furthermore, standardized residuals (SR) larger than two were used to identify significant differences between observed and expected values. In addition, we used the Kruskal–Wallis <emph>H</emph> test to compare the distribution of continuous variables across latent profiles.</p> <hd id="AN0187949514-14">LPA With Covariates and a Distal Outcome</hd> <p>To address the third and fourth research questions, we specified PA, screentime, sleep, and diet as covariates of the MH latent categorical variable, and SS as a distal outcome. The mixture model with covariates and a distal outcome was estimated using a three‐step procedure that aims to avoid classification error (Asparouhov and Muthén [<reflink idref="bib2" id="ref105">2</reflink>]). These three steps are: (<reflink idref="bib1" id="ref106">1</reflink>) estimate the latent categorical variable (MH), (<reflink idref="bib2" id="ref107">2</reflink>) create a nominal variable (<emph>N</emph>) storing most likely profile memberships, and (<reflink idref="bib3" id="ref108">3</reflink>) estimate the mixture model with a distal outcome where <emph>N</emph> is an indicator of MH with the measurement error equal to the misclassification proportion estimated at first step.</p> <hd id="AN0187949514-15">Results</hd> <p></p> <hd id="AN0187949514-16">Descriptive Results</hd> <p>The (recoded) MH item with the highest average rating was "I (do not) take things that are not mine from home, school or elsewhere" (<emph>M</emph> = 2.8, SD = 0.48). In contrast, the (recoded) item with the lowest average rating was "I am (not) restless, I cannot (can) stay still for long" (<emph>M</emph> = 1.92, SD = 0.696). Results showed that MH subscale scores and the composite PA, screentime, sleep, and diet variables had indices of skewness lower than 2 and indices of kurtosis lower than 6. Participants reported exercising approximately three times on a typical week (<emph>M</emph> = 3.59, SD = 2.01). The reported screentime ranged from zero to 11 h, with an average of 5.22 h (SD = 3.218). Items measuring sleep quality had high ratings, with average ratings above 3. Most students reported that they liked their school (<emph>M</emph> = 3.10, SD = 0.783). Furthermore, most students agreed that school is a nice place to be (<emph>M</emph> = 4.34, SD = 1.71). CFA showed that items in the MH (χ<sups>2</sups><subs>(<reflink idref="bib265" id="ref109">265</reflink>)</subs> = 512.081; <emph>p</emph> < 0.001; RMSEA<subs>(90%CI)</subs> = 0.054<subs>(0.047;0.061)</subs>; CFI = 0.924; TLI = 0.917; SRMR = 0.044), diet (χ<sups>2</sups><subs>(<reflink idref="bib35" id="ref110">35</reflink>)</subs> = 247.237; <emph>p</emph> < 0.001; RMSEA<subs>(90%CI)</subs> = 0.039<subs>(0.023;0.055)</subs>; CFI = 0.960; TLI = 0.941; SRMR = 0.031), and sleep (χ<sups>2</sups><subs>(<reflink idref="bib35" id="ref111">35</reflink>)</subs> = 371.452; <emph>p</emph> < 0.001; RMSEA<subs>(90%CI)</subs> = 0.075 [0.059;0.091]; CFI = 0.925; TLI = 0.895; SRMR = 0.062) scales were good measures of the corresponding constructs. Item‐level results are available upon request.</p> <hd id="AN0187949514-17">Research Question 1: MH Latent Profiles</hd> <p>LPA results showed that the five‐profile model had the lowest AIB and BIC indices; however, the Lo–Mendell–Rubin test showed that the improvement in model fit was not statistically significant compared to the four‐profile model (Table 1). The four‐profile model had the highest entropy and had a significantly better model fit than the three‐profile model. Furthermore, the four‐profile model described distinct patterns of MH and was, therefore, selected as the optimal model. Average latent profile probabilities and classification probabilities for the four latent profiles ranged between 88.4% and 100%.</p> <p>1 Table Goodness of fit and classification precision by model.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th>Three‐profile model</th><th>Four‐profile model</th><th>Five‐profile model</th></tr></thead><tbody valign="top"><tr><td>Entropy</td><td>0.848</td><td>0.868</td><td>0.801</td></tr><tr><td>AIC</td><td>3136.101</td><td>3059.754</td><td>3021.432</td></tr><tr><td>BIC</td><td>3248.678</td><td>3209.857</td><td>3209.061</td></tr><tr><td>Sample adjusted BIC</td><td>3153.527</td><td>3082.988</td><td>3050.474</td></tr><tr><td>Lo–Mendell–Rubin adjusted LRT test</td><td /><td /><td /></tr><tr><td>Value</td><td>340.629</td><td>94.701</td><td>57.325</td></tr><tr><td>p</td><td>0.0000</td><td>0.0332</td><td>0.2053</td></tr></tbody></table> </ephtml> </p> <p>The largest latent profile was labeled Good Mental Health (GMH, <emph>n</emph> = 149). Individuals in this group had above average scores on all scales. The second largest latent profile was labeled Mild Emotional Problems (MEP, <emph>n</emph> = 129). Students in the MEP latent profile had close to average scores on all subscales; however, EmProb and HypAct scores were significantly lower than zero. The third latent profile was named Mild Conduct Problems (MCP) and included 21 students who had scores slightly below average on all subscales, with lower scores on the HypAct and ConProb scales. The smallest group, Problematic Mental Health (PMH) included 16 individuals with significantly lower scores on all subscales indicating potentially severe MH problems and highly dysfunctional behavior. Table 2 reports the LPA model estimates and the corresponding standard errors, <emph>t</emph> statistics, and <emph>p</emph> values. The distribution of all MH scale scores varied significantly across groups, indicating that latent profiles had distinct MH characteristics (Table 3).</p> <p>2 Table LPA model results.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Latent profile</th><th>Health behavior</th><th>Estimate</th><th>SE</th><th>Est./SE</th><th>Two‐tailed <italic>p</italic></th></tr></thead><tbody valign="top"><tr><td>PMH (n = 16, 5.07%)</td><td>Em_Prob</td><td>−2.821</td><td>0.112</td><td>−25.091</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">Con_Prob</td><td>−3.173</td><td>0.131</td><td>−24.202</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">HypAct</td><td>−2.901</td><td>0.076</td><td>−38.293</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">PeerProb</td><td>−3.335</td><td>0.085</td><td>−39.399</td><td>0.000<ext-link href="a" /></td></tr><tr><td /><td>ProSoc</td><td>−3.095</td><td>0.228</td><td>−13.588</td><td>0.000<ext-link href="a" /></td></tr><tr><td>MEP (n = 129, 40.95%)</td><td>Em_Prob</td><td>−0.254</td><td>0.075</td><td>−3.379</td><td>0.001<ext-link href="a" /></td></tr><tr><td align="center">Con_Prob</td><td>−0.053</td><td>0.066</td><td>−0.800</td><td>0.424</td></tr><tr><td align="center">HypAct</td><td>−0.183</td><td>0.069</td><td>−2.629</td><td>0.009<ext-link href="a" /></td></tr><tr><td align="center">PeerProb</td><td>−0.081</td><td>0.069</td><td>−1.169</td><td>0.243</td></tr><tr><td /><td>ProSoc</td><td>−0.030</td><td>0.069</td><td>−0.439</td><td>0.661</td></tr><tr><td>GMH (n = 149, 47.30%)</td><td>Em_Prob</td><td>0.602</td><td>0.064</td><td>9.450</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">Con_Prob</td><td>0.610</td><td>0.040</td><td>15.152</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">HypAct</td><td>0.648</td><td>0.055</td><td>11.843</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">PeerProb</td><td>0.499</td><td>0.045</td><td>11.036</td><td>0.000<ext-link href="a" /></td></tr><tr><td /><td>ProSoc</td><td>0.452</td><td>0.055</td><td>8.186</td><td>0.000<ext-link href="a" /></td></tr><tr><td>MCP (n = 21, 6.67%)</td><td>Em_Prob</td><td>−0.403</td><td>0.190</td><td>−2.122</td><td>0.034<ext-link href="a" /></td></tr><tr><td align="center">Con_Prob</td><td>−1.345</td><td>0.102</td><td>−13.211</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">HypAct</td><td>−1.037</td><td>0.174</td><td>−5.977</td><td>0.000<ext-link href="a" /></td></tr><tr><td align="center">PeerProb</td><td>−0.377</td><td>0.235</td><td>−1.604</td><td>0.109</td></tr><tr><td align="center">ProSoc</td><td>−0.533</td><td>0.205</td><td>−2.602</td><td>0.009<ext-link href="a" /></td></tr></tbody></table> </ephtml> </p> <p>1 Abbreviations: GMH, Good Mental Health; MCP, Mild Conduct Problems; MEP, Mild Emotional Problems; PMH, Problematic Mental Health.</p> <ulist> <item>2 a Significantly different from zero.</item> <item>3 Table HB profile membership, HB scale scores, MH subscale scores, SS, and demographic distribution by MH latent profile.</item> </ulist> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th /><th align="center">MH latent profile</th></tr><tr valign="bottom"><th /><th /><th>PMH (<italic>n</italic> = 16, 5.07%)</th><th>MEP (<italic>n</italic> = 129, 40.95%)</th><th>GMH (<italic>n</italic> = 149, 47.30%)</th><th>MCP (<italic>n</italic> = 21, 6.67%)</th></tr></thead><tbody valign="top"><tr><td>HB latent profiles</td><td>Poor Diet and Sleep</td><td /><td /><td /><td /></tr><tr><td>(χ<sup>2</sup><sub>(9)</sub> = 137.869, Monte Carlo p 99% CI: < 0.001; < 0.001)</td><td>Count</td><td>12</td><td>13</td><td>5</td><td>5</td></tr><tr><td /><td>% within HB latent profile</td><td>34.3%</td><td>37.1%</td><td>14.3%</td><td>14.3%</td></tr><tr><td /><td>% within MH latent profile</td><td>75.0%</td><td>10.1%</td><td>3.4%</td><td>23.8%</td></tr><tr><td /><td>Standardized residual</td><td>7.7</td><td>−0.4</td><td>−2.8</td><td>1.7</td></tr><tr><td /><td>Average Health Behaviors</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>1</td><td>96</td><td>71</td><td>12</td></tr><tr><td /><td>% within HB latent profile</td><td>0.6%</td><td>53.3%</td><td>39.4%</td><td>6.7%</td></tr><tr><td /><td>% within MH latent profile</td><td>6.3%</td><td>74.4%</td><td>47.7%</td><td>57.1%</td></tr><tr><td /><td>Standardized residual</td><td>−2.7</td><td>2.6</td><td>−1.5</td><td>0.0</td></tr><tr><td /><td>High Health Behaviors</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>1</td><td>11</td><td>69</td><td>1</td></tr><tr><td /><td>% within HB latent profile</td><td>1.2%</td><td>13.4%</td><td>84.1%</td><td>1.2%</td></tr><tr><td /><td>% within MH latent profile</td><td>6.3%</td><td>8.5%</td><td>46.3%</td><td>4.8%</td></tr><tr><td /><td>Standardized residual</td><td>−1.6</td><td>−3.9</td><td>4.9</td><td>−1.9</td></tr><tr><td /><td>Low Health Behaviors</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>2</td><td>9</td><td>4</td><td>3</td></tr><tr><td /><td>% within HB latent profile</td><td>11.1%</td><td>50.0%</td><td>22.2%</td><td>16.7%</td></tr><tr><td /><td>% within MH latent profile</td><td>12.5%</td><td>7.0%</td><td>2.7%</td><td>14.3%</td></tr><tr><td /><td>Standardized residual</td><td>1.1</td><td>0.6</td><td>−1.5</td><td>1.6</td></tr><tr><td>PA</td><td>M</td><td>−0.068</td><td>−0.17</td><td>0.189</td><td>−0.249</td></tr><tr><td>H<sub>(3)</sub> = 11.460, p = 0.009</td><td>SD</td><td>0.787</td><td>0.956</td><td>1.023</td><td>1.052</td></tr><tr><td>Screentime</td><td>M</td><td>−0.03</td><td>0.259</td><td>−0.344</td><td>0.871</td></tr><tr><td>H<sub>(3)</sub> = 40.205, p < 0.001</td><td>SD</td><td>0.685</td><td>1.013</td><td>0.876</td><td>0.988</td></tr><tr><td>Sleep</td><td>M</td><td>−1.569</td><td>−0.208</td><td>0.416</td><td>−0.475</td></tr><tr><td>H<sub>(3)</sub> = 75.551, p < 0.001</td><td>SD</td><td>1.071</td><td>0.866</td><td>0.864</td><td>0.849</td></tr><tr><td>Diet</td><td>M</td><td>−1.944</td><td>−0.139</td><td>0.425</td><td>−0.682</td></tr><tr><td>H<sub>(3)=</sub>90.233, p < 0.001</td><td>SD</td><td>1.08</td><td>0.903</td><td>0.669</td><td>1.141</td></tr><tr><td>EmProb</td><td>M</td><td>−2.82</td><td>−0.269</td><td>0.596</td><td>−0.429</td></tr><tr><td>H<sub>(3)</sub> = 142.167, p < 0.001</td><td>SD</td><td>0.464</td><td>0.662</td><td>0.589</td><td>0.693</td></tr><tr><td>ConProb</td><td>M</td><td>−3.172</td><td>−0.092</td><td>0.619</td><td>−1.412</td></tr><tr><td>H<sub>(3)</sub> = 193.221, p < 0.001</td><td>SD</td><td>0.541</td><td>0.474</td><td>0.337</td><td>0.382</td></tr><tr><td>HypAct</td><td>M</td><td>−2.9</td><td>−0.211</td><td>0.649</td><td>−1.101</td></tr><tr><td>H<sub>(3)</sub> = 178.128, p < 0.001</td><td>SD</td><td>0.3127</td><td>0.569</td><td>0.497</td><td>0.539</td></tr><tr><td>PeerProb</td><td>M</td><td>−3.335</td><td>‐0.108</td><td>0.512</td><td>−0.431</td></tr><tr><td>H<sub>(3)</sub> = 120.379, p < 0.001</td><td>SD</td><td>0.349</td><td>0.613</td><td>0.447</td><td>0.773</td></tr><tr><td>ProSoc</td><td>M</td><td>−3.095</td><td>−0.063</td><td>0.464</td><td>−0.546</td></tr><tr><td>H<sub>(3)</sub> = 100.521, p < 0.001</td><td>SD</td><td>0.941</td><td>0.647</td><td>0.529</td><td>0.815</td></tr><tr><td>SS</td><td>M</td><td>−0.601</td><td>−0.291</td><td>0.424</td><td>−0.762</td></tr><tr><td>H<sub>(3)</sub> = 59.958, p < 0.001</td><td>SD</td><td>1.211</td><td>0.947</td><td>0.833</td><td>0.964</td></tr><tr><td>Age</td><td>M</td><td>11.19</td><td>11.74</td><td>11.02</td><td>12</td></tr><tr><td>H<sub>(3)</sub> = 13.595, p = 0.004</td><td>SD</td><td>1.471</td><td>2.206</td><td>1.876</td><td>2.145</td></tr><tr><td>Gender identification</td><td>Female</td><td /><td /><td /><td /></tr><tr><td>(χ<sup>2</sup><sub>(9)</sub> = 14.608, Monte Carlo p 99%CI: 0.097–0.113)</td><td>Count</td><td>4</td><td>62</td><td>70</td><td>6</td></tr><tr><td /><td>% within gender</td><td>2.8%</td><td>43.7%</td><td>49.3%</td><td>4.2%</td></tr><tr><td /><td>% within latent profile</td><td>25.0%</td><td>48.1%</td><td>47.0%</td><td>28.6%</td></tr><tr><td /><td>Standardized residual</td><td>−1.2</td><td>0.5</td><td>0.3</td><td>−1.1</td></tr><tr><td /><td>Male</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>12</td><td>54</td><td>74</td><td>12</td></tr><tr><td /><td>% within gender</td><td>7.9%</td><td>35.5%</td><td>48.7%</td><td>7.9%</td></tr><tr><td /><td>% within latent profile</td><td>75.0%</td><td>41.9%</td><td>49.7%</td><td>57.1%</td></tr><tr><td /><td>Standardized residual</td><td>1.5</td><td>−1.0</td><td>0.2</td><td>0.6</td></tr><tr><td /><td>Other</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>0</td><td>8</td><td>3</td><td>2</td></tr><tr><td /><td>% within gender</td><td>0.0%</td><td>61.5%</td><td>23.1%</td><td>15.4%</td></tr><tr><td /><td>% within latent profile</td><td>0.0%</td><td>6.2%</td><td>2.0%</td><td>9.5%</td></tr><tr><td /><td>Standardized residual</td><td>−0.8</td><td>1.2</td><td>−1.3</td><td>1.2</td></tr><tr><td /><td>"Prefer not to say"</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>0</td><td>5</td><td>2</td><td>1</td></tr><tr><td /><td>% within gender</td><td>0.0%</td><td>62.5%</td><td>25.0%</td><td>12.5%</td></tr><tr><td /><td>% within latent profile</td><td>0.0%</td><td>3.9%</td><td>1.3%</td><td>4.8%</td></tr><tr><td /><td>Standardized residual</td><td>−0.6</td><td>1.0</td><td>−0.9</td><td>0.6</td></tr><tr><td>Racial/ethnic identification</td><td>American Indian or Alaska Native</td><td /><td /><td /><td /></tr><tr><td>(χ<sup>2</sup><sub>(21)</sub> = 47.735, Monte Carlo p 99%CI: < 0.001–0.002)</td><td>Count</td><td>0</td><td>1</td><td>5</td><td>0</td></tr><tr><td /><td>% within race</td><td>0.0%</td><td>16.7%</td><td>83.3%</td><td>0.0%</td></tr><tr><td /><td>% within latent profile</td><td>0.0%</td><td>0.8%</td><td>3.4%</td><td>0.0%</td></tr><tr><td /><td>Standardized residual</td><td>−0.6</td><td>−0.9</td><td>1.3</td><td>−0.6</td></tr><tr><td /><td>Asian</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>0</td><td>18</td><td>27</td><td>4</td></tr><tr><td /><td>% within race</td><td>0.0%</td><td>36.7%</td><td>55.1%</td><td>8.2%</td></tr><tr><td /><td>% within latent profile</td><td>0.0%</td><td>14.0%</td><td>18.1%</td><td>19.0%</td></tr><tr><td /><td>Standardized residual</td><td>−1.6</td><td>−0.5</td><td>0.8</td><td>0.4</td></tr><tr><td /><td>Black or African American</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>3</td><td>11</td><td>5</td><td>1</td></tr><tr><td /><td>% within race</td><td>15.0%</td><td>55.0%</td><td>25.0%</td><td>5.0%</td></tr><tr><td /><td>% within latent profile</td><td>18.8%</td><td>8.5%</td><td>3.4%</td><td>4.8%</td></tr><tr><td /><td>Standardized residual</td><td>2.0</td><td>1.0</td><td>−1.5</td><td>−0.3</td></tr><tr><td /><td>Hispanic or Latino</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>3</td><td>9</td><td>6</td><td>2</td></tr><tr><td /><td>% within race</td><td>15.0%</td><td>45.0%</td><td>30.0%</td><td>10.0%</td></tr><tr><td /><td>% within latent profile</td><td>18.8%</td><td>7.0%</td><td>4.0%</td><td>9.5%</td></tr><tr><td /><td>Standardized residual</td><td>2.0</td><td>0.3</td><td>−1.1</td><td>0.6</td></tr><tr><td /><td>Hawaiian or Other Pacific Islander</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>5</td><td>4</td><td>7</td><td>1</td></tr><tr><td /><td>% within race</td><td>29.4%</td><td>23.5%</td><td>41.2%</td><td>5.9%</td></tr><tr><td /><td>% within latent profile</td><td>31.3%</td><td>3.1%</td><td>4.7%</td><td>4.8%</td></tr><tr><td /><td>Standardized residual</td><td>4.5</td><td>−1.1</td><td>−0.4</td><td>−0.1</td></tr><tr><td /><td>White</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>2</td><td>46</td><td>44</td><td>5</td></tr><tr><td /><td>% within race</td><td>2.1%</td><td>47.4%</td><td>45.4%</td><td>5.2%</td></tr><tr><td /><td>% within latent profile</td><td>12.5%</td><td>35.7%</td><td>29.5%</td><td>23.8%</td></tr><tr><td /><td>Standardized residual</td><td>−1.3</td><td>1.0</td><td>−0.3</td><td>−0.6</td></tr><tr><td /><td>Other</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>3</td><td>26</td><td>42</td><td>6</td></tr><tr><td /><td>% within race</td><td>3.9%</td><td>33.8%</td><td>54.5%</td><td>7.8%</td></tr><tr><td /><td>% within Latent Profile</td><td>18.8%</td><td>20.2%</td><td>28.2%</td><td>28.6%</td></tr><tr><td /><td>Standardized residual</td><td>−0.5</td><td>−1.0</td><td>0.9</td><td>0.4</td></tr><tr><td /><td>"Prefer not to say"</td><td /><td /><td /><td /></tr><tr><td /><td>Count</td><td>0</td><td>14</td><td>13</td><td>2</td></tr><tr><td /><td>% within race</td><td>0.0%</td><td>48.3%</td><td>44.8%</td><td>6.9%</td></tr><tr><td /><td>% within latent profile</td><td>0.0%</td><td>10.9%</td><td>8.7%</td><td>9.5%</td></tr><tr><td /><td>Standardized residual</td><td>−1.2</td><td>0.6</td><td>−0.2</td><td>0.0</td></tr></tbody></table> </ephtml> </p> <p>3 Abbreviations: GMH, Good Mental Health; MCP, Mild Conduct Problems; MEP, Mild Emotional Problems; PMH, Problematic Mental Health.</p> <hd id="AN0187949514-18">Research Question 2: Differences in MH Profiles by HB Profiles, Specific Health Behaviors, SS...</hd> <p>As indicated in Table 3, HB latent profiles had a disproportionate distribution across MH latent profiles (χ<sups>2</sups><subs>(<reflink idref="bib9" id="ref112">9</reflink>)</subs> = 137.869, Monte Carlo <emph>p</emph> 99% CI [< 0.001, < 0.001]). PHM included a significantly higher proportion of individuals with Poor Diet and Sleep (SR = 7.7), and a significantly lower proportion of individuals with Average Health Behaviors (SR = −2.7). In contrast, GMH included a significantly higher proportion of individuals with High Health Behaviors (SR = 4.9) and a significantly lower proportion of individuals with Poor Diet and Sleep (SR = −2.8). The MEP profile included a significantly lower percentage of individuals with High Health Behaviors (SR = −3.9) and more individuals with Average Health Behavior (SR = 2.6). Figure 1 illustrates the distribution of HB profile memberships across MH latent profiles.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01oct25/pits23592-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="pits23592-fig-0001.jpg" title="1 Mental health profile membership by health behavior latent profile membership." /> </p> <p></p> <p>Regarding specific health behaviors, diet (<emph>H</emph><subs>(<reflink idref="bib3" id="ref113">3</reflink>)</subs> = 90, <emph>p</emph> < 0.001) and sleep (<emph>H</emph><subs>(<reflink idref="bib3" id="ref114">3</reflink>)</subs> = 75.551, <emph>p</emph> < 0.001) scores varied significantly across MH latent profiles. GMH had the highest average Diet and Sleep scores, followed by MEP, MCP, and PMH. Screentime also varied significantly across MH latent profiles (<emph>H</emph><subs>(<reflink idref="bib3" id="ref115">3</reflink>)</subs> = 40.205, <emph>p</emph> < 0.001). GMH had the lowest average screentime score, followed by PMH, MEP, and MCP. Furthermore, the PA score distribution varied significantly across the MH latent profiles (<emph>H</emph><subs>(<reflink idref="bib3" id="ref116">3</reflink>)</subs> = 11.460, <emph>p</emph> = 0.009). Specifically, GMH had the highest PA scores, followed by PMH, MEP, and MCP. The GMH profile also had the highest average SS scores, followed by MEP, PMH, and MCP. SS distributional differences between MH latent profiles were statistically significant (<emph>H</emph><subs>(<reflink idref="bib3" id="ref117">3</reflink>)</subs> = 59.958, <emph>p</emph> < 0.001). Figure 2 shows PA, screentime, sleep, diet, MH subscale scores, and SS by MH latent profile.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01oct25/pits23592-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="pits23592-fig-0002.jpg" title="2 PA, sleep, screentime, diet, MH subscale scores, and SS by MH latent profile." /> </p> <p></p> <p>In terms of demographic characteristics, age distribution varied significantly across latent profiles (<emph>H</emph><subs>(<reflink idref="bib3" id="ref118">3</reflink>)</subs> = 13.595, <emph>p</emph> = 0.004). MCP (<emph>M</emph> = 12, SD = 2.145) had a slightly higher average age than the other groups. A closer examination of the GMH and MEP distributions showed that the MEP distribution included a few outliers at the lower end. Gender identification did not differ significantly across MH latent profiles (χ<sups>2</sups><subs>(<reflink idref="bib9" id="ref119">9</reflink>)</subs> = 14.608, Monte Carlo <emph>p</emph> 99% CI: [0.097, 0.113]). Racial identification varied significantly by latent profile (χ<sups>2</sups><subs>(<reflink idref="bib21" id="ref120">21</reflink>)</subs> = 47.735, Monte Carlo <emph>p</emph> 99% CI [< 0.001, 0.002]) due to a higher‐than‐expected proportion of individuals identifying as Hawaiian or Pacific Islander (SR = 4.5), Hispanic or Latino (SR = 2), and Black or African American (SR = 2) in the PMH group.</p> <p>Post‐hoc pairwise comparisons are presented in Table 4. There were significant Diet and Sleep distributional differences between GMH and all other MH latent profiles, and between MEP and PMH; significant screentime distributional differences between GMH and MEP and between GMH and MCP; significant PA distributional differences between GMH and MEP; significant SS differences between GMH and all other MH latent profiles; and significant age distributional differences between GMH and MEP.</p> <p>4 Table Pairwise comparisons of MH latent profiles.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Sample 1‐sample 2</th><th>Test statistic</th><th>SE</th><th>Std. test statistic</th><th><italic>p</italic></th><th>Adj. <italic>p</italic><ext-link /><sup>a</sup></th></tr></thead><tbody valign="top"><tr><td>PA</td><td /><td /><td /><td /><td /></tr><tr><td>MCP‐MEP</td><td>9.550</td><td>21.359</td><td>0.447</td><td>0.655</td><td>1.000</td></tr><tr><td>MCP‐PMH</td><td>20.695</td><td>30.121</td><td>0.687</td><td>0.492</td><td>1.000</td></tr><tr><td>MCP‐GMH</td><td>43.369</td><td>21.157</td><td>2.050</td><td>0.040</td><td>0.242</td></tr><tr><td>MEP‐PMH</td><td>11.145</td><td>24.059</td><td>0.463</td><td>0.643</td><td>1.000</td></tr><tr><td>MEP‐GMH</td><td>−33.819</td><td>10.916</td><td>−3.098</td><td>0.002</td><td>0.012</td></tr><tr><td>PMH‐GMH</td><td>−22.674</td><td>23.880</td><td>−0.950</td><td>0.342</td><td>1.000</td></tr><tr><td>Sleep</td><td /><td /><td /><td /><td /></tr><tr><td>PMH‐MCP</td><td>−52.738</td><td>30.197</td><td>−1.746</td><td>0.081</td><td>0.484</td></tr><tr><td>PMH‐MEP</td><td>−79.050</td><td>24.119</td><td>−3.278</td><td>0.001</td><td>0.006</td></tr><tr><td>PMH‐GMH</td><td>−150.336</td><td>23.940</td><td>−6.280</td><td>< 0.001</td><td>0.000</td></tr><tr><td>MCP‐MEP</td><td>26.312</td><td>21.413</td><td>1.229</td><td>0.219</td><td>1.000</td></tr><tr><td>MCP‐GMH</td><td>97.597</td><td>21.211</td><td>4.601</td><td>< 0.001</td><td>0.000</td></tr><tr><td>MEP‐GMH</td><td>−71.285</td><td>10.944</td><td>−6.514</td><td>< 0.001</td><td>0.000</td></tr><tr><td>Screentime</td><td /><td /><td /><td /><td /></tr><tr><td>GMH‐PMH</td><td>35.300</td><td>23.816</td><td>1.482</td><td>0.138</td><td>0.830</td></tr><tr><td>GMH‐MEP</td><td>55.082</td><td>10.887</td><td>5.059</td><td>< 0.001</td><td>0.000</td></tr><tr><td>GMH‐MCP</td><td>−103.768</td><td>21.101</td><td>−4.918</td><td>< 0.001</td><td>0.000</td></tr><tr><td>PMH‐MEP</td><td>−19.783</td><td>23.994</td><td>−0.824</td><td>0.410</td><td>1.000</td></tr><tr><td>PMH‐MCP</td><td>−68.469</td><td>30.040</td><td>−2.279</td><td>0.023</td><td>0.136</td></tr><tr><td>MEP‐MCP</td><td>−48.686</td><td>21.302</td><td>−2.286</td><td>0.022</td><td>0.134</td></tr><tr><td>Diet</td><td /><td /><td /><td /><td /></tr><tr><td>PMH‐MCP</td><td>−42.863</td><td>30.172</td><td>−1.421</td><td>0.155</td><td>0.933</td></tr><tr><td>PMH‐MEP</td><td>−85.761</td><td>24.099</td><td>−3.559</td><td>< 0.001</td><td>0.002</td></tr><tr><td>PMH‐GMH</td><td>−160.981</td><td>23.920</td><td>−6.730</td><td>< 0.001</td><td>0.000</td></tr><tr><td>MCP‐MEP</td><td>42.898</td><td>21.395</td><td>2.005</td><td>0.045</td><td>0.270</td></tr><tr><td>MCP‐GMH</td><td>118.118</td><td>21.193</td><td>5.573</td><td>< 0.001</td><td>0.000</td></tr><tr><td>MEP‐GMH</td><td>−75.220</td><td>10.935</td><td>−6.879</td><td>< 0.001</td><td>0.000</td></tr><tr><td>Age</td><td /><td /><td /><td /><td /></tr><tr><td>GMH‐PMH</td><td>4.915</td><td>23.700</td><td>0.207</td><td>0.836</td><td>1.000</td></tr><tr><td>GMH‐MCP</td><td>−33.663</td><td>20.998</td><td>−1.603</td><td>0.109</td><td>0.653</td></tr><tr><td>GMH‐MEP</td><td>38.495</td><td>10.834</td><td>3.553</td><td>< 0.001</td><td>0.002</td></tr><tr><td>PMH‐MCP</td><td>−28.749</td><td>29.894</td><td>−0.962</td><td>0.336</td><td>1.000</td></tr><tr><td>PMH‐MEP</td><td>−33.580</td><td>23.877</td><td>−1.406</td><td>0.160</td><td>0.958</td></tr><tr><td>MCP‐MEP</td><td>4.832</td><td>21.198</td><td>0.228</td><td>0.820</td><td>1.000</td></tr><tr><td>SS</td><td /><td /><td /><td /><td /></tr><tr><td>MCP‐PMH</td><td>22.845</td><td>30.034</td><td>0.761</td><td>0.447</td><td>1.000</td></tr><tr><td>MCP‐MEP</td><td>37.320</td><td>21.297</td><td>1.752</td><td>0.080</td><td>0.478</td></tr><tr><td>MCP‐GMH</td><td>108.139</td><td>21.096</td><td>5.126</td><td>< 0.001</td><td>0.000</td></tr><tr><td>PMH‐MEP</td><td>−14.475</td><td>23.989</td><td>−0.603</td><td>0.546</td><td>1.000</td></tr><tr><td>PMH‐GMH</td><td>−85.294</td><td>−23.811</td><td>−3.582</td><td>< 0.001</td><td>0.002</td></tr><tr><td>MEP‐GMH</td><td>−70.819</td><td>10.885</td><td>−6.506</td><td>< 0.001</td><td>0.000</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 <emph>Note:</emph> Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances (two‐sided tests) are displayed. The significance level is 0.050.</item> <item>5 a Significance values have been adjusted by the Bonferroni correction for multiple tests.</item> </ulist> <hd id="AN0187949514-21">Research Questions 3 and 4: Predictors of MH Profiles and SS</hd> <p>Table 5 presents the results for the third and fourth research questions. In reference to the GMH group, lower diet scores predicted higher probability of membership to the PMH and MCP profiles; increased screentime predicted a higher probability of membership to the MEP and MCP profiles; and poor sleep habits predicted lower probability of membership to the MEP group. In reference to GMH, membership to all other profiles predicted significantly lower levels of school satisfaction. Table 5 reports the path coefficients and the corresponding standard errors, <emph>t</emph> statistics and <emph>p</emph> values.</p> <p>5 Table Relationships between MH latent profile membership, covariates, and the distal outcome.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th>Estimate</th><th>SE</th><th>Est./SE</th><th>Two‐tailed <italic>p</italic></th></tr></thead><tbody valign="top"><tr><td>Covariates ‐> MH latent profiles</td><td /><td /><td /><td /></tr><tr><td>Reference: MCP</td><td /><td /><td /><td /></tr><tr><td>PA ‐ > PMH</td><td>−0.176</td><td>0.332</td><td>−0.529</td><td>0.597</td></tr><tr><td>Sleep ‐> PMH</td><td>−0.183</td><td>0.640</td><td>−0.286</td><td>0.775</td></tr><tr><td>Screentime ‐> PMH</td><td>−1.049</td><td>0.399</td><td>−2.632</td><td>0.008<ext-link href="a" /></td></tr><tr><td>Diet ‐> PMH</td><td>−0.661</td><td>0.617</td><td>−1.072</td><td>0.284</td></tr><tr><td>PA ‐ > MEP</td><td>−0.055</td><td>0.255</td><td>−0.214</td><td>0.830</td></tr><tr><td>Sleep ‐> MEP</td><td>−0.293</td><td>0.361</td><td>−0.810</td><td>0.418</td></tr><tr><td>Screentime ‐> MEP</td><td>−0.605</td><td>0.321</td><td>−1.885</td><td>0.059</td></tr><tr><td>Diet ‐> MEP</td><td>0.672</td><td>0.380</td><td>1.768</td><td>0.077</td></tr><tr><td>PA ‐ > GMH</td><td>0.203</td><td>0.277</td><td>0.732</td><td>0.464</td></tr><tr><td>Sleep ‐> GMH</td><td>0.336</td><td>0.406</td><td>0.828</td><td>0.408</td></tr><tr><td>Screentime ‐> GMH</td><td>−1.171</td><td>0.326</td><td>−3.589</td><td>0.000<ext-link href="a" /></td></tr><tr><td>Diet ‐> GMH</td><td>1.207</td><td>0.552</td><td>2.184</td><td>0.029<ext-link href="a" /></td></tr><tr><td>Reference: PMH</td><td /><td /><td /><td /></tr><tr><td>PA ‐ > MEP</td><td>0.121</td><td>0.270</td><td>0.447</td><td>0.655</td></tr><tr><td>Sleep ‐> MEP</td><td>−0.109</td><td>0.543</td><td>−0.202</td><td>0.840</td></tr><tr><td>Screentime ‐> MEP</td><td>0.444</td><td>0.338</td><td>1.312</td><td>0.189</td></tr><tr><td>Diet ‐> MEP</td><td>1.333</td><td>0.502</td><td>2.655</td><td>0.008<ext-link href="a" /></td></tr><tr><td>PA ‐ > GMH</td><td>0.379</td><td>0.310</td><td>1.223</td><td>0.221</td></tr><tr><td>Sleep ‐> GMH</td><td>0.519</td><td>0.599</td><td>0.867</td><td>0.386</td></tr><tr><td>Screentime ‐> GMH</td><td>−0.121</td><td>0.343</td><td>−0.353</td><td>0.724</td></tr><tr><td>Diet ‐> GMH</td><td>1.868</td><td>0.670</td><td>2.788</td><td>0.005<ext-link href="a" /></td></tr><tr><td>PA ‐ > MCP</td><td>0.176</td><td>0.332</td><td>0.529</td><td>0.597</td></tr><tr><td>Sleep ‐> MCP</td><td>0.183</td><td>0.640</td><td>0.286</td><td>0.775</td></tr><tr><td>Screentime ‐> MCP</td><td>1.049</td><td>0.399</td><td>2.632</td><td>0.008<ext-link href="a" /></td></tr><tr><td>Diet ‐> MCP</td><td>0.661</td><td>0.617</td><td>1.072</td><td>0.284</td></tr><tr><td>Reference MEP</td><td /><td /><td /><td /></tr><tr><td>PA ‐ > PMH</td><td>−0.121</td><td>0.270</td><td>−0.447</td><td>0.655</td></tr><tr><td>Sleep ‐> PMH</td><td>0.109</td><td>0.543</td><td>0.202</td><td>0.840</td></tr><tr><td>Screentime ‐> PMH</td><td>−0.444</td><td>0.338</td><td>−1.312</td><td>0.189</td></tr><tr><td>Diet ‐> PMH</td><td>−1.333</td><td>0.502</td><td>−2.655</td><td>0.008<ext-link href="a" /></td></tr><tr><td>PA ‐ > GMH</td><td>0.257</td><td>0.190</td><td>1.352</td><td>0.176</td></tr><tr><td>Sleep ‐> GMH</td><td>0.629</td><td>0.277</td><td>2.269</td><td>0.023<ext-link href="a" /></td></tr><tr><td>Screentime ‐> GMH</td><td>−0.565</td><td>0.171</td><td>−3.297</td><td>0.001<ext-link href="a" /></td></tr><tr><td>Diet ‐> GMH</td><td>0.534</td><td>0.468</td><td>1.142</td><td>0.254</td></tr><tr><td>PA ‐ > MCP</td><td>0.055</td><td>0.255</td><td>0.214</td><td>0.830</td></tr><tr><td>Sleep ‐> MCP</td><td>0.293</td><td>0.361</td><td>0.810</td><td>0.418</td></tr><tr><td>Screentime ‐> MCP</td><td>0.605</td><td>0.321</td><td>1.885</td><td>0.059</td></tr><tr><td>Diet ‐> MCP</td><td>−0.672</td><td>0.380</td><td>−1.768</td><td>0.077</td></tr><tr><td>Reference: GMH</td><td /><td /><td /><td /></tr><tr><td>PA ‐ > PMH</td><td>−0.379</td><td>0.310</td><td>−1.223</td><td>0.221</td></tr><tr><td>Sleep ‐> PMH</td><td>−0.519</td><td>0.599</td><td>−0.867</td><td>0.386</td></tr><tr><td>Screentime ‐> PMH</td><td>0.121</td><td>0.343</td><td>0.353</td><td>0.724</td></tr><tr><td>Diet ‐> PMH</td><td>−1.868</td><td>0.670</td><td>−2.788</td><td>0.005<ext-link href="a" /></td></tr><tr><td>PA ‐ > MEP</td><td>−0.257</td><td>0.190</td><td>−1.352</td><td>0.176</td></tr><tr><td>Sleep ‐> MEP</td><td>−0.629</td><td>0.277</td><td>−2.269</td><td>0.023<ext-link href="a" /></td></tr><tr><td>Screentime ‐> MEP</td><td>0.565</td><td>0.171</td><td>3.297</td><td>0.001<ext-link href="a" /></td></tr><tr><td>Diet ‐> MEP</td><td>−0.534</td><td>0.468</td><td>−1.142</td><td>0.254</td></tr><tr><td>PA ‐ > MCP</td><td>−0.203</td><td>0.277</td><td>−0.732</td><td>0.464</td></tr><tr><td>Sleep ‐> MCP</td><td>−0.336</td><td>0.406</td><td>−0.828</td><td>0.408</td></tr><tr><td>Screentime ‐> MCP</td><td>1.171</td><td>0.326</td><td>3.589</td><td>0.000<ext-link href="a" /></td></tr><tr><td>Diet ‐> MCP</td><td>−1.207</td><td>0.552</td><td>−2.184</td><td>0.029<ext-link href="a" /></td></tr><tr><td>MH latent profiles ‐> distal outcome</td><td /><td /><td /><td /></tr><tr><td>Reference: MCP</td><td /><td /><td /><td /></tr><tr><td>PMH ‐ > SS</td><td>0.150</td><td>0.344</td><td>0.435</td><td>0.664</td></tr><tr><td>MEP ‐ > SS</td><td>0.402</td><td>0.220</td><td>1.824</td><td>0.068</td></tr><tr><td>GMH ‐ > SS</td><td>1.645</td><td>0.285</td><td>5.762</td><td>0.000<ext-link href="a" /></td></tr><tr><td>Reference: PMH</td><td /><td /><td /><td /></tr><tr><td>MEP ‐ > SS</td><td>0.252</td><td>0.300</td><td>0.842</td><td>0.400</td></tr><tr><td>GMH ‐ > SS</td><td>1.495</td><td>0.343</td><td>4.359</td><td>0.000<ext-link href="*" /></td></tr><tr><td>MCP ‐ > SS</td><td>−0.150</td><td>0.344</td><td>−0.435</td><td>0.664</td></tr><tr><td>Reference MEP</td><td /><td /><td /><td /></tr><tr><td>PMH ‐ > SS</td><td>−0.252</td><td>0.300</td><td>−0.842</td><td>0.400</td></tr><tr><td>GMH ‐ > SS</td><td>1.243</td><td>0.242</td><td>5.146</td><td>0.000<ext-link href="a" /></td></tr><tr><td>MCP ‐ > SS</td><td>−0.402</td><td>0.220</td><td>−1.824</td><td>0.068</td></tr><tr><td>Reference: GMH</td><td /><td /><td /><td /></tr><tr><td>PMH ‐ > SS</td><td>−1.495</td><td>0.343</td><td>−4.359</td><td>0.000<ext-link href="a" /></td></tr><tr><td>MEP ‐ > SS</td><td>−1.243</td><td>0.242</td><td>−5.146</td><td>0.000<ext-link href="a" /></td></tr><tr><td>MCP ‐ > SS</td><td>−1.645</td><td>0.285</td><td>−5.762</td><td>0.000<ext-link href="a" /></td></tr></tbody></table> </ephtml> </p> <p>6 a Results using other latent profiles as reference are available upon request.</p> <hd id="AN0187949514-22">Discussion</hd> <p>Children and adolescents' MH plays a key role in their academic success at school (Centers for Disease Control and Prevention [<reflink idref="bib9" id="ref121">9</reflink>]. Identifying modifiable determinants and proximal outcomes for intervention and tailoring support for MH at the individual level are important pursuits in educational research and practice. Based on previous research (Webster et al. [<reflink idref="bib66" id="ref122">66</reflink>]; Bell et al. [<reflink idref="bib5" id="ref123">5</reflink>]; Blake et al. [<reflink idref="bib6" id="ref124">6</reflink>]; DeSantis King et al. [<reflink idref="bib13" id="ref125">13</reflink>]; Due et al. [<reflink idref="bib14" id="ref126">14</reflink>]; Guddal et al. [<reflink idref="bib19" id="ref127">19</reflink>]; Horanicova et al. [<reflink idref="bib22" id="ref128">22</reflink>]; Hosker et al. [<reflink idref="bib23" id="ref129">23</reflink>]; Jacka et al. [<reflink idref="bib24" id="ref130">24</reflink>]; Jovаnovic and Jerkovic [<reflink idref="bib26" id="ref131">26</reflink>]; Kohlboeck et al. [<reflink idref="bib28" id="ref132">28</reflink>]; Musshafen et al. [<reflink idref="bib40" id="ref133">40</reflink>]; O'Neil et al. [<reflink idref="bib47" id="ref134">47</reflink>]; Sampasa‐Kanyinga et al. [<reflink idref="bib53" id="ref135">53</reflink>]; Sánchez‐Miguel et al. [<reflink idref="bib55" id="ref136">55</reflink>]; Short et al. [<reflink idref="bib58" id="ref137">58</reflink>]; Valois et al. [<reflink idref="bib64" id="ref138">64</reflink>]), the present study focused on associations between students' MH, health behaviors, and school satisfaction. This study drew upon Baker et al.'s ([<reflink idref="bib3" id="ref139">3</reflink>]) developmental ecological perspective to position school satisfaction as an outcome of MH and students' health behaviors as determinants of MH. Furthermore, we approached this study from a person‐centered perspective in line with emerging evidence that distinct patterns of health behavior are not only distinguishable but meaningful in their relationship to MH (Webster et al. [<reflink idref="bib66" id="ref140">66</reflink>]; Bang et al. [<reflink idref="bib4" id="ref141">4</reflink>]; Mahon et al. [<reflink idref="bib35" id="ref142">35</reflink>]; Wilhite et al. [<reflink idref="bib67" id="ref143">67</reflink>]). Specifically, to extend our initial research in which we identified latent profiles of students' health behavior (Webster et al. [<reflink idref="bib66" id="ref144">66</reflink>]), we addressed four research questions: (a) What latent profiles of MH underlie the data? (b) Do MH profiles differ by health behavior profiles, specific health behaviors, school satisfaction, and demographic characteristics? (c) Are specific health behaviors significant predictors of MH profiles? and (d) Are MH profiles significant predictors of school satisfaction? Based on previous theory and research, we hypothesized that healthier MH profiles would include more older students and boys than younger students and girls (Mahon et al. [<reflink idref="bib35" id="ref145">35</reflink>]; Webster et al. [<reflink idref="bib66" id="ref146">66</reflink>]), health behaviors would be significant predictors of MH profile membership (Baker et al. [<reflink idref="bib3" id="ref147">3</reflink>]), and MH profiles would significantly predict school satisfaction (Baker et al. [<reflink idref="bib3" id="ref148">3</reflink>]).</p> <hd id="AN0187949514-23">Research Question 1: Mental Health Profiles</hd> <p>Due to a lack of previous research addressing students' MH profiles Dubai‐based British schools or more broadly in the UAE, the first research question was exploratory. We identified a four‐profile solution as optimal: GMH, MEP, MCP, and PMH. Overall, it is reassuring that GMH was the largest profile in this study (consisting of about 47% of participants), while PMH was the smallest profile (consisting of about 5% of participants). Some concern is warranted, however, with respect to our finding that the second largest profile (MEP) included approximately 41% of the participants in our sample. Membership in the MEP profile signifies increased emotional problems and hyperactivity/inattention compared to the average. Our results mirror global statistics, which indicate that emotional problems and hyperactivity/inattention are common MH disorders in children and adolescents (World Health Organization [<reflink idref="bib71" id="ref149">71</reflink>]). While further research is needed regarding the specific context investigated in the present study, there may be a need for MH resources in Dubai‐based British schools that primarily address emotional problems and hyperactivity/inattention. MH programming may also involve providing support for smaller groups of students with conduct problems (i.e., the MCP profile in our study) and more severe MH problems (i.e., the PMH profile).</p> <hd id="AN0187949514-24">Research Question 2: Differences in Mental Health Profiles by Health Behavior Profiles, Speci...</hd> <p>Similar to the first research question, the second research question was mostly exploratory owing to scant empirical evidence pertaining to students' MH profiles, health profiles, and school satisfaction in Dubai‐based British schools. Health behavior profile membership was significantly associated with MH profile membership. The Poor Diet and Sleep profile included a high proportion of students fitting the PMH latent profile and a low proportion of students fitting the GMH profile. These results bolster our previous research with the same sample of students (Webster et al. [<reflink idref="bib66" id="ref150">66</reflink>]), in which the Poor Diet and Sleep profile had the lowest MH scores (a standardized score representing total MH), thus underscoring the importance of giving increased consideration in future research and practice to the combination of Poor Diet and Sleep as an important factor associated with students' MH. In addition, we found in the present investigation that the High Health Behaviors profile included a large proportion of students fitting the GMH profile. This further reinforces our initial study (Webster et al. [<reflink idref="bib66" id="ref151">66</reflink>]), which showed that the High Health Behaviors profile had the highest MH scores. However, by considering MH profiles in the current study, the results uniquely demonstrated variance with respect to the MEP profile, with a low proportion of students who fit this profile being included in the High Health Behaviors profile, but a larger proportion of students from the MEP profile being included in the Average Health Behaviors profile. We therefore suggest that, in the context of Dubai‐based British schools, MH support for students with emotional problems or hyperactivity/inattention may need to incorporate a focus on improving all health behaviors measured in this study (physical activity, screentime, sleep quality, and dietary habits).</p> <p>Associations between specific health behaviors and the MH profiles showed that the GMH profile had the highest scores for all health behaviors, which aligns with our above‐mentioned results showing that the High Health Behaviors profile included a high proportion of participants fitting the GMH profile. Given these findings, we were surprised that while the PMH profile had the lowest Sleep and Diet scores, this profile also had higher PA scores than both the MEP and MCP profiles. Students scoring relatively low on all subscales of the Strengths and Difficulties Questionnaire may require more intervention support to improve their sleep quality and dietary habits than to increase their physical activity. In addition, we found that the MCP profile had the highest screentime scores, which may indicate a heightened need for reducing screentime among students with conduct problems or hyperactivity/inattention.</p> <p>MH profile membership also varied by school satisfaction. The GMH profile had the highest SS scores, which is consistent with previous studies in which authors reported significant associations between MH (lower emotional distress, reduced internalizing and externalizing behaviors) and school satisfaction in youth (DeSantis King et al. [<reflink idref="bib13" id="ref152">13</reflink>]; Jovаnovic and Jerkovic [<reflink idref="bib26" id="ref153">26</reflink>]). Furthermore, we found that school satisfaction scores decreased across the MEP, PMH, and MCP profiles, respectively, suggesting that students with conduct problems or hyperactivity/inattention in Dubai‐based British schools may need the most support developing positive perceptions of their school experience.</p> <p>Regarding demographic characteristics, we expected more desirable MH profiles to include a greater proportion of older students and boys than younger students and girls (Mahon et al. [<reflink idref="bib35" id="ref154">35</reflink>]; Webster et al. [<reflink idref="bib66" id="ref155">66</reflink>]). There was a significant difference in age between the GHM and MEP groups. These groups consisted of students with similar average ages, but the GHM group included an outlier at the higher end of the distribution while the MEP group included several outliers (younger participants) at the lower end of the distribution. This result reflects global statistics, which indicate that emotional problems are more prevalent in older versus younger youth (World Health Organization [<reflink idref="bib71" id="ref156">71</reflink>]). However, gender was not a meaningful factor in distinguishing profile membership in the present study. This could be due to the difference in age between participants in our study, who were about 11 years old on average, and participants in the Mahon et al. ([<reflink idref="bib35" id="ref157">35</reflink>]) study, who were almost 15 years old on average. Other research has shown that boys tend to have more MH difficulties than girls until puberty (around ages 10 or 11), at which point girls report double the rate of MH problems than boys (Cyranowski et al. [<reflink idref="bib12" id="ref158">12</reflink>]; NHS Digital [<reflink idref="bib43" id="ref159">43</reflink>]; Patton and Viner [<reflink idref="bib48" id="ref160">48</reflink>]). MH profiles of boys and girls in our sample may therefore have been more mixed given the onset of puberty. The current investigation also explored the role of race/ethnicity in students' MH profiles, with results showing significant variation across MH latent profiles with a higher‐than‐expected proportion of individuals identifying as Hawaiian or Pacific Islander, Hispanic or Latino, and Black or African American in the PMH group. These findings align with previous literature demonstrating increased mental ill health among ethnoracial minoritized groups (Kirkbride et al. [<reflink idref="bib27" id="ref161">27</reflink>]), though the present study is distinct in its consideration of students' race/ethnicity from a person‐centered perspective and in the context of Dubai‐based British schools. Overall, the demographic findings from this study reinforce recommendations for early intervention with youth to help prevent the development of MH disorders in adolescence, as well as culturally adapted MH care (e.g., parent training programs) for children and adolescents from marginalized racial and ethnic groups (Kirkbride et al. [<reflink idref="bib27" id="ref162">27</reflink>]).</p> <hd id="AN0187949514-25">Research Question 3: Specific Health Behaviors as Predictors of Mental Health Profiles</hd> <p>The results in reference to the third research question strengthened the findings related to the second research question and mostly supported our hypothesis that health behaviors would be significant predictors of MH profiles, based on Baker et al.'s (2003) proposed importance of identifying modifiable determinants of students' MH, and previous studies highlighting associations between health behaviors and MH in youth (Bell et al. [<reflink idref="bib5" id="ref163">5</reflink>]; Blake et al. [<reflink idref="bib6" id="ref164">6</reflink>]; Due et al. [<reflink idref="bib14" id="ref165">14</reflink>]; Guddal et al. [<reflink idref="bib19" id="ref166">19</reflink>]; Hosker et al. [<reflink idref="bib23" id="ref167">23</reflink>]; Jacka et al. [<reflink idref="bib24" id="ref168">24</reflink>]; Kohlboeck et al. [<reflink idref="bib28" id="ref169">28</reflink>]; Musshafen et al. [<reflink idref="bib40" id="ref170">40</reflink>]; O'Neil et al. [<reflink idref="bib47" id="ref171">47</reflink>]; Sampasa‐Kanyinga et al. [<reflink idref="bib53" id="ref172">53</reflink>]; Sánchez‐Miguel et al. [<reflink idref="bib55" id="ref173">55</reflink>]; Short et al. [<reflink idref="bib58" id="ref174">58</reflink>]; Valois et al. [<reflink idref="bib64" id="ref175">64</reflink>]). Specifically, the diet, sleep, and screentime covariates were significant predictors of MH profile memberships. Their predictive strength varied by MH profile and based on the profile of reference; however, overall, Poor Diet and Sleep predicted higher probabilities of membership to less adaptive MH profiles (PMH, MCP, and MEP). In contrast, better dietary and sleep habits were significant predictors of membership to more adaptive MH latent profiles (GMH). Similarly, increased screentime was a significant predictor of membership to less adaptive MH latent profiles (MEP or MCP). PA was the only health behavior that was not a significant predictor of MH profile membership, although this finding resonates with our results related to Research Question 2, which showed higher PA scores for the PMH profile than for the MEP and MCP profiles. Future research should further investigate the effectiveness of promoting physical activity compared to other health behaviors in MH interventions for school‐aged youth.</p> <hd id="AN0187949514-26">Research Question 4: Mental Health Profiles as Predictors of School Satisfaction</hd> <p>The final research question was whether MH profiles are significant predictors of school satisfaction. Drawing from Baker et al.'s (2003) developmental ecological perspective of school satisfaction, which positions school satisfaction as an outcome in relation to individual needs' satisfaction (e.g., appropriately supported MH), we hypothesized that MH profiles would significantly predict school satisfaction. As expected, in reference to the GMH profile, membership to all other profiles predicted significantly lower levels of school satisfaction. Furthermore, we found that sleep, diet, and screentime indirectly influenced SS through MH profiles. Ensuring students feel satisfied with school may require school staff to provide increased support for students in the MEP, MCP, and PMH profiles and include focused strategies in school‐based programming to improve sleep quality and dietary habits and reduce screentime. An important direction for future research is determining what kind of behavioral support students in each MH profile need in relation to satisfaction with school. For instance, studies might examine the effects of interventions targeting specific health behaviors (e.g., sleep quality, dietary habits, screentime) on the school satisfaction of students fitting different MH profiles (e.g., students with increased conduct problems, students with increased emotional problems). Such research can not only build on the present study to enhance policy‐driven MH support in the UAE through the education sector but can also be used to further customize MH support in line with Lowry et al.'s ([<reflink idref="bib33" id="ref176">33</reflink>]) recommendations for adopting more individualized approaches to the Health Promoting Schools model. Specifically, continued emphasis on understanding determinants and outcomes of MH from a person‐centered perspective and within specific contexts will enable multiple stakeholders across the school system (e.g., educational policymakers, teacher educators, principals, teachers, school counselors, community partners) to design, develop, and implement Health Promoting School initiatives that are more responsive to contextual nuances and students' individual needs.</p> <hd id="AN0187949514-27">Limitations</hd> <p>This study has several limitations. First, our use of a small convenience sample from two schools limits the generalizability of the results. Future investigations should randomly sample students from all British curriculum schools in Dubai, if possible. Second, despite asking teachers to assist students during survey administration, some of the data may have been confounded by the dietary habits and MH measures, which had somewhat lower reliability coefficients than the other scales in this study. These measures were not originally validated for young children, but it was impractical to administer multiple versions of the questionnaires to participants, and our modifications may not have been ideal in all cases. In general, achieving high internal consistencies when assessing younger children can be challenging (Taber [<reflink idref="bib62" id="ref177">62</reflink>]). Moreover, the diet scale may have had lower internal consistency because it assesses several dietary habits, and lower Cronbach's alphas may be expected for instruments measuring a range of distinct concepts or behaviors (Taber [<reflink idref="bib62" id="ref178">62</reflink>]). A third limitation of this study is that students' self‐reports of their health behaviors may reflect participant biases. We recommend that researchers use objective measures of children's physical activity, screentime, sleep, and diet in subsequent studies. Finally, as this study used a correlational design, it is not possible to confirm the directionality of associations between variables. Experimental research is needed to establish cause‐effect relationships between determinants and outcomes of students' MH profiles.</p> <hd id="AN0187949514-28">Conclusions</hd> <p>This study identified distinct profiles of MH and associations between profile membership, health behaviors, and school satisfaction among school‐aged youth enrolled in British curriculum schools in Dubai. The results carry important practical implications for health and education authorities in Dubai. Given the key role of school satisfaction in students' academic success (Horanicova et al. [<reflink idref="bib22" id="ref179">22</reflink>]; Jovаnovic and Jerkovic [<reflink idref="bib26" id="ref180">26</reflink>]), school health programming may need to be customized according to assessments of students' MH profile membership. While the MH profiles identified in the current study provide initial guidance for such assessments, continued person‐centered research with larger, representative samples of school‐aged youth is recommended to establish stronger MH profile data that can guide professional practice in Dubai‐based British curriculum schools and other specific school contexts where students' MH is a pronounced concern.</p> <hd id="AN0187949514-29">Author Contributions</hd> <p>All authors contributed to the study conception and design. Material preparation and data collection were performed by Collin A. Webster, Anthony D. Murphy, Ivana Banićević, Dušan Perić, Dragan Stankić, and Željko Banićević. Diana Mîndrila conducted the analyses. The first draft of the manuscript was written by Collin A. Webster and Diana Mîndrila, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.</p> <hd id="AN0187949514-30">Ethics Statement</hd> <p>Approval to conduct this study was obtained from the School of Sport, Exercise, and Rehabilitation Sciences Research Ethics Board at the University of Birmingham.</p> <hd id="AN0187949514-31">Consent</hd> <p>Informed consent from students (18 or older) or students' parents was secured in accordance with Dubai government regulations.</p> <hd id="AN0187949514-32">Conflicts of Interest</hd> <p>The authors declare no conflicts of interest.</p> <hd id="AN0187949514-33">Data Availability Statement</hd> <p>The data collected for this study are confidential and participants did not agree to have the data shared beyond the scope of this study.</p> <ref id="AN0187949514-34"> <title> References </title> <blist> <bibl id="bib1" idref="ref5" type="bt">1</bibl> <bibtext> Agnafors, S., M. Barmark, and G. Sydsjö. 2021. 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Header DbId: eric
DbLabel: ERIC
An: EJ1483564
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
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IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Students' Mental Health Profiles and Their Association with Health Behaviors and School Satisfaction in Dubai-Based British Curriculum Schools
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Collin+A%2E+Webster%22">Collin A. Webster</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1680-9149">0000-0003-1680-9149</externalLink>)<br /><searchLink fieldCode="AR" term="%22Diana+Mîndrila%22">Diana Mîndrila</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8374-9524">0000-0001-8374-9524</externalLink>)<br /><searchLink fieldCode="AR" term="%22Anthony+D%2E+Murphy%22">Anthony D. Murphy</searchLink><br /><searchLink fieldCode="AR" term="%22Ivana+Banicevic%22">Ivana Banicevic</searchLink><br /><searchLink fieldCode="AR" term="%22Dušan+Peric%22">Dušan Peric</searchLink><br /><searchLink fieldCode="AR" term="%22Dragan+Stankic%22">Dragan Stankic</searchLink><br /><searchLink fieldCode="AR" term="%22Željko+Banicevic%22">Željko Banicevic</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Psychology+in+the+Schools%22"><i>Psychology in the Schools</i></searchLink>. 2025 62(10):4023-4040.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 18
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22International+Schools%22">International Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink><br /><searchLink fieldCode="DE" term="%22Profiles%22">Profiles</searchLink><br /><searchLink fieldCode="DE" term="%22Health+Behavior%22">Health Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Satisfaction%22">Student Satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Activities%22">Physical Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Use%22">Computer Use</searchLink><br /><searchLink fieldCode="DE" term="%22Sleep%22">Sleep</searchLink><br /><searchLink fieldCode="DE" term="%22Dietetics%22">Dietetics</searchLink><br /><searchLink fieldCode="DE" term="%22Age%22">Age</searchLink><br /><searchLink fieldCode="DE" term="%22Race%22">Race</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22United+Arab+Emirates%22">United Arab Emirates</searchLink><br /><searchLink fieldCode="DE" term="%22United+Kingdom+%28Great+Britain%29%22">United Kingdom (Great Britain)</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/pits.23592
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0033-3085<br />1520-6807
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Health behavior, mental health, and school satisfaction are associated in school-aged youth. However, most previous research examining such associations does not account for unique groupings of these variables based on person-centered analyses. This study examined student mental health profiles and their association with health behaviors and school satisfaction. Students (N = 315, M[subscript age] = 11.39) from two British curriculum schools in Dubai, the United Arab Emirates, self-reported their mental health, physical activity, screentime, sleep quality, dietary habits, and school satisfaction. LPA revealed a four-profile solution as an optimal fit to the data. Significant differences in profile membership were found based on students' health behavior profiles, health behaviors, school satisfaction, age, and race/ethnicity. All health behaviors except physical activity predicted mental health profiles, and mental health profiles predicted school satisfaction. This study provides initial evidence of distinct mental health profiles that may require customized support in health programming for students in Dubai-based British curriculum schools.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1483564
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1483564
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/pits.23592
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 4023
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: International Schools
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Mental Health
        Type: general
      – SubjectFull: Profiles
        Type: general
      – SubjectFull: Health Behavior
        Type: general
      – SubjectFull: Student Satisfaction
        Type: general
      – SubjectFull: Physical Activities
        Type: general
      – SubjectFull: Computer Use
        Type: general
      – SubjectFull: Sleep
        Type: general
      – SubjectFull: Dietetics
        Type: general
      – SubjectFull: Age
        Type: general
      – SubjectFull: Race
        Type: general
      – SubjectFull: Ethnicity
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: United Arab Emirates
        Type: general
      – SubjectFull: United Kingdom (Great Britain)
        Type: general
    Titles:
      – TitleFull: Students' Mental Health Profiles and Their Association with Health Behaviors and School Satisfaction in Dubai-Based British Curriculum Schools
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Collin A. Webster
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          Name:
            NameFull: Diana Mîndrila
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            NameFull: Anthony D. Murphy
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            NameFull: Ivana Banicevic
      – PersonEntity:
          Name:
            NameFull: Dušan Peric
      – PersonEntity:
          Name:
            NameFull: Dragan Stankic
      – PersonEntity:
          Name:
            NameFull: Željko Banicevic
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          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0033-3085
            – Type: issn-electronic
              Value: 1520-6807
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            – Type: volume
              Value: 62
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              Value: 10
          Titles:
            – TitleFull: Psychology in the Schools
              Type: main
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