Stability, Latent Profiles, and Sociodemographic Predictors of Student-Rated Social, Emotional, and Behavioral Risk

School Mental Health https://doi.org/10.1007/s12310-025-09757-y ORIGINAL PAPER Stability, Latent Profiles, and Sociodemographic Predictors of Student‑Rated Social, Emotional, and Behavioral Risk Brittany N. Zakszeski1 · Heather E. Ormiston2 · Tyler L. Renshaw3 · Mei‑Ki Chan3 · Daniel Osgood1 Accepted: 25 March 2025 © The Author(s) 2025 Abstract To inform the use of universal social, emotional, and behavioral (SEB) screening in secondary schools, we examined the functioning of the Social, Academic, and Emotional Behavior Risk Screener–Student Rating Scale (mySAEBRS) across three occasions (fall, winter, and spring) in a sample of secondary students (Grades 6–12). With consideration for the frequency and timing of screening administration, we analyzed the stability of mySAEBRS raw scores, risk classifications, and score latent profiles. The total scale and subscales evidenced strong raw score stability as well as moderate-to-strong classification stability with one exception (the Social Behavior risk classification across the fall–spring interval). The three latent SEB risk profiles identified for each occasion (flourishing, at low risk, and at some risk) likewise demonstrated generally strong stability over screening intervals. We also evaluated the contributions of students’ sociodemographics to screening results. Eligibility for free or reduced-price lunch and special education, respectively, significantly predicted membership in the at-low-risk and at-some-risk profiles, with the flourishing profile as the reference group. We describe how these results may inform the design of universal screening systems as well as opportunities for future research to build upon these findings. Keywords Universal screening · Behavioral risk · Measurement stability · Latent profile analysis Introduction Universal screening for social, emotional, and behavioral (SEB) risk in schools is used to drive prevention and intervention efforts within a multitiered system of support (MTSS) framework (Kiperman et al., 2024). Although essential to the identification of youth in need of more intensive supports at the Tier 2 and Tier 3 levels (Allen et al., 2019), universal screening also provides a mechanism through which schools can evaluate school-wide initiatives related to the prevention of SEB risk (Kiperman et al., 2024; Moore et al., 2023). For instance, universal screening data “can be analyzed for trends across time to determine the changing prevalence and incidence of mental health * Brittany N. Zakszeski 1 School of Education, University of Delaware, Newark, DE 19716, USA 2 Counseling and Educational Psychology Department, Indiana University Bloomington, Bloomington, IN, USA 3 Psychology Department, Utah State University, Logan, UT, USA problems…[and] allow for schools to prioritize and direct resources into the prevention or intervention efforts that are most needed” (Dowdy et al., 2010, p. 170). In other words, data can be analyzed to monitor the prevalence of mental health concerns and their responsiveness to intervention implementation. Data can also help school teams respond to fluctuations in the mental health needs of their student population over time (Dowdy et al., 2010). Further, universal screening helps to establish base rates of SEB risk, which can help clarify the school’s capacity to support students post-screening (Kilgus & Eklund, 2016). Teachers are the most common informant in universal screening yet are considered “poor reporters of student emotional problems” (von der Embse et al., 2021, p. 632) because their ratings of students’ SEB functioning generally rely on behaviors displayed in a classroom and may not reflect students’ internal states (von der Embse et al., 2019). Emerging evidence also indicates teacher bias may play a role in how teachers rate students (Fallon et al., 2023; Ormiston & Renshaw, 2023). For instance, one study examining student and teacher demographic characteristics as predictors of between-teacher variance in universal screening ratings for student SEB risk found evidence of continued Vol.:(0123456789) School Mental Health overidentification of Black youth as having higher levels of SEB risk compared to their White counterparts (Splett et al., 2018). Coupled with evidence that racially and ethnically minoritized youth are often punished or subjected to harsh disciplinary practices in lieu of referral for mental health services (So et al., 2024), it is imperative to understand the value of self-report measures for universal screening. Use of psychometrically sound self-report screening measures offers a complement to teacher referral or teacheronly screening procedures (Romer et al., 2020). Selfreport measures can tap into essential aspects of student functioning related to internalizing behaviors (Moore et al., 2015; Zakszeski et al., 2025) and overall well-being (Moore et al., 2015). Further, self-report measures are important at the secondary (i.e., middle and high school) level for several reasons. Self-report measures allow for student voice and autonomy in the identification process (Moore et al., 2015). Moreover, youth have unique access to their internal states as they mature and gain independence from caregivers and other adults (Hyland et al., 2022). Given students’ rotation among multiple teachers per day, secondary teachers may have less familiarity with students than their elementary counterparts (Margherio et al., 2019). The manifestation of SEB concerns may differ across contexts (e.g., home, school, community), and self-report tools may better capture students’ perceptions of functioning across various domains (De Los Reyes et al., 2015). Stability of Universal Screening Tools Best practice recommendations suggest universal screening should occur one to three times per year (Romer et al., 2020); however, more research is needed to guide decision-making regarding the timing and frequency of administrations (Jenkins et al., 2021). The stability of SEB functioning is an important consideration for the provision of SEB supports (Dowdy et al., 2014). In the context of screening, stability refers to the consistency of scores over time—for example, whether students found not at risk at one timepoint remain not at risk at subsequent timepoints (Miller et al., 2019). For constructs with more fluidity (e.g., depression symptoms), it is beneficial to measure the construct on multiple occasions to capture those whose symptomatology emerges or changes over time (Dever et al., 2018). Within MTSS, students who receive SEB intervention may have less stable screening results than their peers; however, the literature is devoid of studies assessing universal screeners’ responsiveness to intervention across the tiers. Numerous studies have reported the stability of teacherreport screening tools within (Kilpatrick et al., 2018; Miller et al., 2019) and across (Dever et al., 2018; WarmboldBrann et al., 2018; Yu et al., 2022) ac (...truncated)