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Abstract
In an attempt to identify and intervene with students in need of services, the South Korean government has implemented national mental health screening. However, concerns raised about the unintended stigmatization of the screening assessment that focuses on student deficits prompts the need for additional research. This study evaluated the potential utility of an alternative screening approach that considers student strengths, in addition to symptoms of distress. Using a sample of 1,190 Korean adolescents enrolled in grades seven to nine, two latent profile analyses were conducted to identify underlying mental health strength and distress subtypes. Results identified five subtypes of psychological strengths and three subtypes of psychological distress. As hypothesized, students with higher levels of strengths and lower levels of distress reported better quality of life, academic performance, and higher life satisfaction. Implications for school based mental health screening and future directions for researchers and practitioners are discussed.
According to the Organization for Economic Co-operation and Development (OECD, 2014), South Korean students are some of the best performing in the world academically, particularly in mathematics and reading. When compared to all other countries, however, South Korean students rated themselves as the least happy, leading to questions such as, ‘Is South Korea the world’s top producer of unhappy school children?’ (Philips, 2013, p. 1) and ‘Are Korean kids learning to be unhappy?’ (Elgin, 2013, p. 1). Due to concerns about the well-being of students, in 2012, the South Korean government implemented school-based, universal mental health screening to identify students who might benefit from mental health services. From its inception, the program elicited concerns because of its use of dichotomous diagnostic criteria that labeled students as either normal or abnormal based on their screening responses (Seo, 2012). Not only in South Korea, but in other countries such as the United States and China, mental health screening has often focused on identifying symptoms of psychopathology and has tended to overlook students’ strengths that might act as protective factors (Epstein, Rudolph, & Epstein, 2000; Wang, Zhang, & Wang, 2011).
Functional consequences of mental health problems and strengths for adolescents have been well-documented in previous research. Students with emotional and behavioral disorders are more vulnerable to negative school outcomes such as academic underachievement and school dropout (Osher, Morrison, & Bailey, 2003; Wagner, Kutash, Duchnowski, Epstein, & Sumi, 2005). Nonetheless, when students with emotional and behavioral disorders are able to form and access more external (e.g., family and school support) and internal resources (e.g., motivation and school engagement), they were found to have improved quality of life outcomes such as physical health, academic achievement, and prosocial behaviors (Evans, Marsh, & Weigel, 2010; Scales, Benson, Roehlkepartain, Sesma, & van Dulmen, 2006). These findings suggest that efforts to cultivate positive mental health, along with efforts to alleviate symptoms of mental health, are closely associated with students’ quality of life outcomes.
Through emerging research examining multicomponent approaches to mental health, it is clear that the absence of psychological distress symptoms is not equivalent with thriving psychological well-being (Keyes, 2006), which has provoked the expansion of mental health definitions to include a balance of distress and well-being symptoms. Such research has focused on the assessment and classification of students using what has been called ‘dual-continua’ or ‘dual-factor’ conceptualizations of mental health in which both psychological distress and well-being are simultaneously considered (Greenspoon & Saklofske, 2001; Suldo & Shaffer, 2008). However, there is not yet a standard of practice with regard to classifying students based on dual-factor screening approaches, with large discrepancies among classification methods. The overarching purpose of this study was to examine a dual-factor screening approach and provide an empirical rationale for classification. Findings could help educators identify and cultivate all students’ strengths to foster flourishing mental health, while attending to the existing distress of some students.
National school-based mental health screening in South Korea
The Ministry of Health, Welfare, and Family Affairs, in collaboration with the Ministry of Education, announced the early identification-counseling-treatment system to reduce students’ mental health problems via a screening system to identify and treat mental health problems (Korean Ministry of Health, Welfare, and Family Affairs, 2008). The most recent screening results indicated that out of 2.1 million students, 9.4% were identified as at risk for mental health problems—7.2% at risk and 2.2% at high risk (Korean Ministry of Education, 2013a). At-risk students are referred for support services including counseling and psychiatric assessment.
It is laudable that the South Korean government recognized youths’ mental health needs and implemented a care service system; nonetheless, concerns have emerged about the unintended consequence of screening resulting in the labeling and stigmatizing of students. Bo Moon Choi, the chairman of the Korean Society for Medical Ethics, pointed out that the screening instruments used are deficit-oriented (Seo, 2012). Some South Korean parents have reacted negatively to their children being labeled as having mental health problems and have refused to give permission for any services (Kim, 2014). Unless the limitations of the current screening approach are addressed, concerns will remain and the program might not reach its maximum intended benefit.
Dual factor mental health screening
When deficit-based and strength-based measures are used in combination, mental health screening can offer a balanced and comprehensive perspective on students’ complete mental health, with greater attention to positive development indicators (Dowdy, Furlong, & Sharkey, 2013). Questions asked in the screening are more applicable for all students regardless of their level of distress, and provide more practical information for schools in the development of early identification and prevention efforts. For example, schools can develop strength-based services for promoting positive behavioral and emotional functioning before students demonstrate substantial impairment. Furthermore, youth mental health studies have found that consideration of both psychological strengths and distress had additive value in predicting students’ school-related outcomes, such as attendance and academic achievement (Antaramian, Huebner, Hills, & Valois, 2010; Dowdy et al., 2013).
Aligned with a dual-continua approach to mental health, several studies assessed students’ strengths (e.g., subjective well-being; SWB) and distress (e.g., psychopathology; PTH; Antaramian et al., 2010; Greenspoon & Saklofske, 2001; Lyons et al., 2012; Suldo & Shaffer, 2008). These studies have relied on logical predetermined values as decision points (e.g., raw scores, sample means, standard deviations, or T-scores) to assign students into mental health categories (see supplemental materials for a summary of previous studies that classified students into mental health groups using a dual-factor approach). In most studies, students were first classified into high or low groups according to their PTH and then classified into a high or low SWB group using rationally derived cutpoints. Finally, students were classified into one of four mental health groups based on the logical crossing of dichotomized SWB and PTH groups.
Although two groups (high SWB & low PTH, and low SWB & high PTH) show the expected patterns of mental health, two other groups (low SWB & low PTH, and high SWB & high PTH) represent empirically expected patterns, but ones that are more likely to go undetected and uncared for within traditional psychopathology-focused screening approaches (Suldo & Shaffer, 2008). Results of these studies that identified additional mental health groups suggested that there could be benefits to including both strengths and distress indicators in screening surveys. Recently, Dowdy, Furlong et al. (2015) described the dual-continua approach for use in mental health screening. Instead of classifying students into dichotomous (i.e., high or low) strength and distress groups, they used z-scores to classify students into nine logical groups, suggesting that there may be more complex subtypes of adolescent psychological functioning.
There are limitations of the use of predetermined logical cutpoints to identify and classify students. In previous studies, students who did not meet the criteria (e.g., ≤30th percentile for low SWB and ≥T-score of 60 for high PTH) were placed into another group (e.g., high SWB and low PTH groups). Using these classification criteria, a student with a SWB score at the 31st percentile would be considered to have high SWB and would be placed in the same group as students scoring at the 99th percentile. These simplified categorizations compress students into the middle ranges and may not identify all empirically derived students’ mental health profiles. The use of cutpoints also does not identify underlying psychological profiles, but rather creates rationally derived groups. This absence of an empirical rationale for classifying students’ mental health could result in discrepancies with regard to the identification of students in need of support. Hence, there is a need to begin to explore the viability of an empirically-supported criterion to evaluate how psychological distress information and strength information can be used to identify students in need of follow-up mental health services. Such an approach has direct relevance and implications for screening procedures in South Korea and in other national contexts.
Study purpose
The dual-factor approach has used logical-rationale, as opposed to empirical, strategies to evaluate the meaning of lower and higher scores on measures of personal distress and personal strengths. This study aims to explore the underlying profiles reflective of psychological distress and strength among South Korean adolescents, using an empirical approach to classification. The emerged profiles of covitality (i.e., a combination of positive psychological traits) and distress will be cross-tabulated to examine how a students’ empirically derived positive mental health class compares with their classification on negative mental health indicators.
Previous dual-factor studies suggested that students with higher levels of SWB had more positive quality of life outcomes, including academic achievement, physical health, social functioning, and student engagement (Antaramian et al., 2010; Suldo & Shaffer, 2008), highlighting the importance of psychological strengths in life experiences among adolescents. The current study will examine how dual-factor mental health groups vary across quality of life indicators, including life satisfaction and self-reported grades. Results could provide guidance on how schools can meaningfully conduct and interpret the results of schoolwide mental health screening.
Method
Participants
In the 2012–2013 school year, 1,190 students from three randomly selected urban middle schools in South Korea completed surveys regarding their psychological strengths and distress. Participants were in Grades 7 through 9, with a mean age of 14 years (SD = 0.81). Parent consent was waived following Korean IRB regulations, and students were informed that they could decline to participate in the survey at any time (172 out of 1,400 students opted out). All students self-reported their ethnicity as Korean, which was representative of the student population in South Korea (i.e., 0.86% of non-Korean students; Korean Ministry of Education , 2013b). Participants consisted of 652 (54.8%) male students, and 272 (22.9%) seventh-grade, 311 (26.2%) eighth-grade, and 606 (51%) ninth-grade students.
Measures
Social and emotional health survey–secondary (SEHS-S)
Furlong, You, Renshaw, Smith, and O’Malley (2014) developed the SEHS-S, a 36-item strength-based measure, to assess positive social-emotional constructs. The SEHS-S has 12 subscales (three items per subscale) that load onto four domains: Belief-in-self (i.e., self awareness, persistence, self-efficacy), belief-in-others (i.e., peer support, school support, family support), emotional competence (i.e., empathy, emotional regulation, behavioral self-control), and engaged living (i.e., gratitude, zest, optimism). These four domains combine to create an overall covitality score. All subscales use a four-point response scale (1 = not at all true of me, 4 = very much true of me), except for the zest subscales with a five-point response scale (1 = not at all, 5 = extremely). The SEHS-S has demonstrated good factor structure, reliability, and validity with US samples (e.g., Furlong et al., 2014, You et al., 2014) and with Asian samples including Korean (Lee, You, & Furlong, 2015), Chinese (Pan, Zhang, Chen, & Liu, 2016), and Japanese adolescents (Ito, Smith, You, Shimoda, & Furlong, 2015). In these previous studies (Furlong et al., 2014; Lee et al., 2015; Pan et al., 2016; You et al., 2014), Cronbach’s alpha ranges for the four domains were: 0.78–0.84 (belief-in-self), 0.85–0.87 (belief-in-others), 0.82 (emotional competence), and 0.88 (engaged living). Furthermore, the SEHS-S had a strong concurrent validity with subjective well-being (r’s = 0.67–0.78).
Because there are no separate Korean words to substitute the three gratitude adjectives used in the original English version, the Korean version of the SEHS-S used three alternate items with the highest loadings from the gratitude questionnaire-6 (GQ-6; McCullough, Emmons, & Tsang, 2002). Except for this modification, all other SEHS–S items were used. For the present study, internal consistency reliabilities were: Belief-in-self (α = 0.84), belief-in-others (α = 0.85), emotional competence (α = 0.82), engaged living (α = 0.88), and covitality (α = 0.94).
The strengths and difficulties questionnaire – self report (SDQ)
The SDQ is a widely used 25-item questionnaire that measures emotional and behavioral problems in 11- to 16-year-olds (Goodman, 1997). There are five items on each of the following five subscales: Emotional symptoms, conduct problems, hyperactivity, peer problems, and prosocial behavior. All items use a three-point scale (0 = not true, 1 = somewhat true, 2 = certainly true). The current study used two subscales, emotional symptoms and conduct problems, as negative indicators of mental health to measure internalizing (INT) and externalizing (EXT) problems. The Korean version of the SDQ was used in this study (www.sdqinfo.org). Kim, Ahn, and Min (2015) examined the reliability and validity of the self-report Korean version of SDQ and reported alphas for conduct problems (0.28–0.46) and emotional symptoms (0.64–0.83) subscales. Furthermore, the SDQ has been shown to distinguish between a community and a clinic sample – for the total difficulties score, the clinic sample was seven times more likely than the community sample to have a score in the abnormal range. For the current sample, Cronbach’s alpha was 0.72 for emotional symptoms and 0.70 for conduct problems, which is consistent with the recommended criteria for scales (α >0.70; DeVellis, 2003).
Quality of life indicators
The Student life satisfaction scale (SLSS; Huebner, 1991) was used to measure global life satisfaction, and was translated into Korean for this study using a translation and back-translation process. It is comprised of seven items (e.g., ‘My life is going well’) using a six-point Likert scale, ranging from 1 (strongly disagree) to 6 (strongly agree). The SLSS has been reported to have sound psychometric properties with school-aged populations (Cronbach’s α range = 0.81–0.86; Huebner, 1991; Lyons, Huebner, Hills, & Shinkareva, 2012). Cronbach’s α for the current sample was 0.82, with Mitem response = 3.99 (range 1–6), SD = 0.94. Additionally, students were presented with five options to describe their current academic grade point average (GPA). Scores indicated the following: 1 = <65 (D’s and below), 2 = 65–75 (C’s and D’s), 3 = 76–85 (B’s and C’s), 4 = 86–95 (A’s and B’s), and 5 = >95 (mostly A’s).
Procedure
In 2013–2014, surveys including both positive (SEHS-S) and negative (SDQ) indicators of mental health were collected in three South Korean middle schools along with self-reported life satisfaction and grades. In November and December 2013, students in ten classes in the first school and four classes in the second school completed a paper-and-pencil survey within their classrooms. Teachers and research assistants distributed the anonymous survey and provided instructions. Students in eight classes at the third participating school completed the survey in March 2014 following the same procedure. Teachers randomly selected 14 classes for participation. After students completed the survey, research assistants collected the surveys and entered and merged the data.
Analysis
In contrast to traditional dual-factor classification methods that create groups based on logical-rational cutpoints, Latent Profile Analysis (LPA) attempts to identify underlying latent profiles; that is, clusters of students who have similar responses across all items (Nylund, Asparouhov, & Muthen, 2007). Prior to conducting the LPAs, the data (N = 1,228) were screened for statistical assumptions including multivariate outliers, heteroscedacticity, and normality. A total of 38 cases were removed due to careless and inattentive responses (N = 8), missing items (N = 23), and outliers (N = 7), resulting in the final sample consisting of 1,190 cases for the present study. See Table 1 for means, Standard Deviations, and correlations for all variables.
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Table 1. Correlation matrix, means, and standard deviations for all variables for total sample (N = 1,190).
Steps I and II: Covitality and psychological distress profiles
In step I, using Mplus version 7.1 (Muthén & Muthén, 1998–2012), an unconditional LPA with one to seven classes was estimated to identify underlying profiles of covitality (i.e., psychological strengths) using the four continuous indicators of the SEHS-S (i.e., belief-in-self, belief-in-others, emotional competence, and engaged living). Full information maximum likelihood (FIML) estimation was used to handle missing data. Means on these four domains of the SEHS-S were used in the LPA. To decide the best fitting model, various fit statistics were reviewed, including the Bayesian information criteria (BIC; Schwartz, 1978), the Adjusted BIC (ABIC; Sclove, 1987), p-values for the Lo-Mendell-Rubin test (LMRT; Lo, Mendell, & Rubin, 2001), and the Bootstrap likelihood ratio test (BLRT; McLachlan & Peel, 2000). Next, the accuracy with which models classify individuals into their most likely class was examined using Entropy. In step II, an unconditional LPA with one to four classes was estimated to identify underlying profiles among the two indicators of psychological distress (i.e., emotional symptoms and conduct problems domains of the SDQ). The same statistical criteria from step I were evaluated to determine the optimal fitting model.
Step III: Mental health profiles
In step III, consistent with the dual-factor approach, the profiles of covitality identified by LPA (step I) and the profiles of psychological distress identified by LPA (step II) were cross-tabulated to explore profiles of mental health of South Korean youths. Then, two ANOVAs were conducted using SPSS version 21 to examine whether the cross-tabulated mental health groups were significantly different from each other with regard to quality of life indicators.
Results
Covitality profiles among Korean youths
Fit statistics and Entropy values for each tested model are presented in Table 2. Overall, the five-class LPA solution provided the optimal balance between statistical fit, model parsimony, and substantive interpretation (BIC = 6215.831, ABIC = 6126.894, LMRT and BLRT, p-values < 0.001), suggesting that there are five meaningful covitality groups of South Korean adolescents. The profile plot (see Figure 1) of the estimated mean values for each SEHS-S domain by each class revealed that the profiles are in an ordered pattern, suggesting that students in each class are likely to have similar mean scores across the four SEHS-S domains.
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Table 2. Fit information and entropy values for LPA step 1 with 1–7 classes for social emotional health survey–secondary.
Class 1 had the lowest mean values across the four SEHS-S domains (N = 65, 5.5%) and was labeled low covitality. Class 2, labeled as below average covitality, had the second lowest mean values (N = 313, 26.3%). Class 3 was labeled as average covitality (N = 464, 39%). Class 4 had the second highest mean values and was labeled as above average covitality (N = 284, 23.9%). The final subgroup identified, class 5 (N = 64, 5.4%) was characterized by the highest mean values on the covitality indicators and labeled as high covitality. The distribution of cases in the five ordered covitality groups suggests that covitality is approximately normally distributed in South Korean adolescents.
Psychological distress profiles
Fit information and entropy values for each tested model are presented in Table 3. See Figure 2 for BIC plots. Fit statistics suggested a four-class model as the best fitting model (BIC = 2110.726, ABIC = 2069.433, LMRT and BLRT, p-values < 0.001), with the highest entropy value. However, the last class in the four-class model consisted of a very small number of students (i.e., 2.2%, N = 26), which often results in low power and precision relative to the other larger classes (Lubke & Neale, 2006). Furthermore, the SDQ is a screening measure designed to identify relatively larger number of students for follow up, rather than identifying a small group of students with clinically significant distress (Goodman, Ford, Simmons, Gatward, & Meltzer, 2000). Hence, the three-class model was selected as the final preferred model (BIC = 2146.994, ABIC = 2115.230, LMRT and BLRT, p-values < 0.001, entropy = 0.77).
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Table 3. Fit information and entropy values for LPA with 1–5 classes for strengths and difficulties questionnaire.
The profile plot (see Figure 2), with the two indicators of psychological distress on the x-axis and the estimated means on the y-axis, shows the three profiles of South Korean adolescents’ psychological distress. Two classes appeared to be in an ordered pattern, starting with the low distress group (class 1: Low in both internal and external distress; N = 738, 61.2%), followed by a dual risk group (class 2: Elevated in both internal and external distress; N = 344, 28.9%). The profile of class 3 crossed the other profiles, endorsing the items of emotional symptoms (at-risk in internal distress), but not the items of conduct problems (low in external distress), and thus was labeled as internal only risk group (class 3; N = 108, 9.1%).
Multiple profiles of mental health
The five covitality profiles and the three psychological distress profiles were cross-tabulated to examine how each covitality subtype is related to each subtype of psychological distress, with the results presented in Figure 3. This logically produced 15 distinct mental health groups of adolescents.
Among the low distress group, 84.4% of students were in the high covitality group, compared to the above average (62.0%), average (69.2%), below average (49.8%), and low covitality (47.7%) groups, indicating that students with higher reported covitality levels were more likely to report low psychological distress. Similarly, the percentage of students in the dual risk group decreased as the level of covitality increased. However, the low to above average covitality classes had the same or similar percentage of students (7.7%, 11.2%, 8.8%, and 8.8%, respectively) classified into the internal only risk group. However, when students had high covitality levels (i.e., high covitality group), the percentage of students in both dual-risk and internal only risk groups decreased to 12.5% and 3.1%, respectively.
Quality of life indicators
Means and Standard Deviations of quality of life indicators (i.e., life satisfaction, grades) across all 15 mental health groups are presented in Table 4. To more fully explore the mental health groups that are not identified using traditional dual-factor classification approaches, we used a series of ANOVAs to examine group differences in quality of life among the nine middle mental health groups (shaded groups in Table 4). Significant group differences were found for both life satisfaction, F(8, 1048) = 53.01, p < 0.001, and grades, F(8, 1036) = 8.44, p < 0.001 (see Table 5).
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Table 4. Means and standard deviations of life satisfaction and grade point average (GPA) for 15 mental health groups.
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Table 5. Post hoc mean comparisons on quality of life indicators among the three middle covitality profiles (below average [BA], average [A], and above average [AA]) for all three distress profiles.
Within each traditional distress group, the levels of life satisfaction and course grades were found to be different when strengths were considered. For life satisfaction, all pairs except one pair of groups (i.e., group 12 versus 13) had significant differences; higher covitality groups had higher life satisfaction than lower covitality groups in all distress groups. Additionally, students with higher covitality showed significantly higher grades than lower covitality in both low distress and dual risk groups, but the level of covitality had no significant relation with grades in the internal only risk group.
Discussion
In support of dual-continua mental health approaches, this study examined positive and negative indicators of mental health to develop a composite of adolescents’ psychological functioning. Underlying typologies of mental health were empirically identified to explore a complementary method of classifying students into mental health groups. Results identified five distinct covitality subtypes among South Korean adolescents, representing low, below average, average, above average, and high covitality. This expands the traditional dual-factor way of looking at positive mental health as either high or low by showing that there are more complex subtypes of positive psychological strengths. Furthermore, the ordered pattern may indicate that these domains of covitality are most likely to be developed together or boosted by each other. This emphasizes the importance of identifying and promoting students’ strengths to optimize their positive psychological functioning. Additionally, results revealed three distinct psychological distress profiles (i.e., low distress, dual risk, and internal only risk). Previous studies that used deficit-based screeners frequently reported three groups of individuals experiencing internal, external, or combined/total symptoms (e.g., Achenbach, 1991). The current study reported similar findings, suggesting that the distress measure (i.e., the SDQ) identified mental distress groups as would be expected, with the exception of a group with only externalizing symptoms.
A unique contribution of the present study was to examine the cross-tabulation of the five identified covitality profiles and the three identified psychological distress profiles, which yielded 15 mental health profiles. By using a dual-factor method following an empirical approach to classification, groups of students that would not have been identified by the traditional symptom-focused classification method were identified. For example, 38% of students with above average levels of covitality and 15.6% of students with high levels of covitality still reported elevated levels of psychological distress. Another group of students reported that they did not have substantial psychological distress, but also do not have substantial internal and external strengths (47.7% of the low covitality group). These findings indicate that the current deficit-oriented, national mental health screening in South Korea is, by design, missing an opportunity to identify these students who are not yet symptomatic, but might be vulnerable for psychological distress because they have low positive resilience-promoting psychological strengths. Results highlight the value of including a balance of positive and negative content in South Korean mental health screening to comprehensively evaluate mental health among students and help them not only not to be sad or distressed, but also to be happy and have a sense of satisfaction about their lives.
Comparisons of dual-factor mental health groups on the quality of life indicators indicated that the level of covitality was significantly positively associated with life satisfaction and grades, regardless of distress level. One exception was that when students were experiencing internalizing distress only, average levels of covitality did not appear to be enough to significantly boost their happiness and grades. These results may suggest that positive psychological assets are more important factors contributing to South Korean adolescents’ evaluation of their own life satisfaction and their academic performance than psychological distress. Furthermore, among a group of students who are classically not identified by traditional deficit-based screeners (i.e., low distress group), students with below average covitality had the lowest means in life satisfaction and grades, when compared with those with average and above average covitality. These students would not be identified and followed up on when screened with the current South Korean mental health screening as it focuses only on students with substantial distress. These results confirm the incomplete information provided by the current deficit-based approach in South Korean student mental health screening and argue for the inclusion of both positive and negative indicators to understand adolescents’ psychological strengths that are closely related to their quality of life.
Results partially indicated that students with lower psychological distress appeared less frequently in higher covitality groups. The percentage of students from the dual risk group decreased as the level of covitality increased, suggesting that having higher covitality is closely associated with lower internalizing and externalizing symptoms. Internal strengths and resources may act as an important buffer against the development of psychological distress; that is they may act as a resilience or protective factor. It is also possible that low psychological distress may allow students to cultivate their strengths and better embrace resources available for them. However, the percentage of students who only had at-risk levels of internalizing symptoms remained similar across the low to above average groups (i.e., 7.7% to 8.8%), but only appeared to decrease when students had high levels of covitality (i.e., 3.1% in the high covitality group). This finding suggests that the presence of more elevated levels of internalizing symptoms may abate the protective role of covitality against the development of psychological distress, unless there are sufficiently high levels of covitality.
Implications for research and practice
By using more robust indicators of psychological strengths and statistical analyses, more complex mental health groups of adolescents with distinct patterns of covitality and psychological distress emerged. Instead of only logically creating a certain number of groups, the current study empirically identified multiple underlying profiles of adolescents’ mental health, suggesting that the logical approach to forming groups might incompletely captures students’ mental health profiles. Results complement and extend previous research on dual-factor mental health, showing that more complex profiles exist and that they are distinctly and closely related to adolescents’ quality of life.
The identification and use of these psychological strengths and distress profiles in Korean student mental health screening may translate well into interventions, whereby different subtypes of students are linked to additional targeted mental health supports and services. Starting with the highest risk group reporting dual risk and low levels of covitality, schools can provide resources and referrals to address the specific needs of these students. Often, these students are provided with more intensive school and community mental health services such as individual cognitive behavioral therapy to reduce existing symptoms, along with interventions to promote strengths to prevent further development of distress. In addition to strength-based programs, the internal only risk group can be provided with treatments (e.g., anxiety or depression intervention) to reduce internalizing symptoms. Students from the low distress group might benefit from group-level or schoolwide, strength-based prevention and intervention supports (e.g., mentoring program and gratitude interventions), as all students could benefit from cultivating positive mental health.
Another important implication of dual-factor screening is that, whereas focusing on pathology can induce guilt or shame among parents, students, and educators, the screening efforts with a focus on optimal student development may reduce these negative impressions. An emphasis on students’ strengths when communicating the screening findings may enhance students’ sense of empowerment and self-esteem, and in turn improve their motivation and treatment compliance (LeBuffe & Shapiro, 2004). Furthermore, parents and educators may feel more comfortable to be engaged in screening and treatment supports when students’ strengths and deficits are addressed together (LeBuffe & Shapiro, 2004). In sum, the current study supports the widening of school personnel’s lenses when interpreting student mental health to include both positive and negative indicators, as psychological functioning likely influences students’ overall life experiences.
Limitations and future directions
This study has several limitations that offer avenues for future research. First, although large, the sample consisted of only three middle schools in urban cities in South Korea, which limits the generalizability of findings and suggests the inclusion of students with more diverse characteristics. Another limitation was the limited range of content and response options on the SDQ (i.e., not true, somewhat true, certainly true), which may not have sufficiently captured the degree of distress students experienced. Additionally, although higher than the recommended criteria, Cronbach’s alphas for the SDQ subscales were relatively low. Future studies may consider using a comprehensive psychological distress scale that measures diverse indicators of distress and has better internal consistencies. Finally, a systematic examination of a richer set of quality of life indicators could be a crucial next step to understanding and connecting the research findings to students’ needs.
Given the importance of student psychological functioning, the identification and understanding of complete psychological health is an important target for further attention in schools. The current study, using empirical classification methods, provides an alternative approach to understand the complete mental health subtypes of South Korean adolescents. One of the main advantages of this approach is to help school psychologists triage limited resources and determine level of care for students. Future research should examine how students with different profiles respond differently to interventions to enhance the efficacy of interventions. This would be a critical step toward further development of targeted mental health services for students at schools.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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Author biographies
Eui Kyung Kim, PhD, is an Assistant Professor in the Psychology Department at North Carolina State University. Her current research focuses on school-based complete mental health screening and related intervention and prevention services to promote social emotional well-being of students in schools. She is also interested in school readiness, school violence, and culturally appropriate services for children.
Erin Dowdy, PhD, NCSP, is an Associate Professor in the Counseling, Clinical, and School Psychology Department at the University of California, Santa Barbara. She is a licensed psychologist and conducts research on early identification methods for behavioral and emotional strengths and risk. She is affiliated with the International Center for School-Based Youth Development at UCSB.
Michael J. Furlong, PhD, is a Professor at the University of California Santa Barbara affiliated with the International Center for School-Based Youth Development. He is a Fellow of the American Psychological Association and the American Educational Research Association, and an elected a member of Society for the Study of School Psychology.
Sukkyung You, PhD, is a Professor in College of Education at Hankuk University of Foreign Studies, Seoul, Korea. Her scholarly publications and conference presentations have focused on prediction of antisocial behavior, gender and ethnic differences in emotional and behavioral problems, school violence, and positive school psychology.