Exploring the Relative Contributions of the Strength and Distress Components of Dual-Factor Complete Mental Health Screening

Abstract

Early school-based mental health screeners were informed by a deficit paradigm that aimed to identify psychological distress symptoms. In comparison, following a whole-child perspective, a dual-factor approach has been proposed that assesses complete mental health using both positive dispositions and distress symptoms. Applying the dual-factor approach, the current study involved 118 students from 10th grade and examined how strongly subjective well-being (SWB) was associated with measures of positive psychological dispositions (Social Emotional Health Survey [SEHS]) and psychological distress (Behavioral and Emotional Screening System [BESS]). Results indicated that the strength-based SEHS explained 32% of the variance in the students’ global SWB with the deficit-based BESS adding an additional 8% of explained variance. Implications for school-based mental health screening are discussed.

Keywords

universal screening dual-factor model strength-based assessment subjective well-being complete mental health

School-based mental health services have focused primarily on identifying and treating symptoms of psychopathology and have tended to overlook how individual strengths might act as protective factors against abnormal development and mental illness (Kia-Keating, Dowdy, Morgan, & Noam, 2011). Although many mental health screeners have acceptable psychometric properties to measure risk factors and clinical symptoms, they provide limited information about students’ strengths and external resources (Epstein, 1999). For example, when children and youth are referred for mental health services and assessed with traditional assessments, they are often labeled and described as having deficits, problems, and pathologies such as conduct disorder or depression, whereas their positive potentials are overlooked (Epstein, Rudolph, & Epstein, 2000). Moreover, by design, symptom-focused screening measures provide actionable information for no more than 15% to 20% of the youths surveyed.

In recognition of this limitation, the re-emergence of positive psychology is linked with the proposal of a “dual-factor” approach that describes mental wellness as more than the absence of symptoms and advocates a switch from focusing solely on disease, weakness, and damage, to building individual strengths and virtues (Kia-Keating et al., 2011). Examples of instruments developed to assess students’ strengths and positive resources are the Behavioral and Emotional Rating Scale (Epstein & Sharma, 1998) and the California Healthy Kids Survey–Resilience Youth Development Module (RYDM; Hanson & Kim, 2007). However, there are limited empirical data examining these strength-based screeners’ capacity to describe and predict positive youth outcomes (Scales, Benson, Leffert, & Blyth, 2000), and often the strengths measured by these instruments are limited in number and scope (Buckley & Epstein, 2004).

The present study is based on the premise that students’ positive development is enhanced when school-based mental health screening and services are grounded in a balanced approach that examines both personal strengths and distress. To contribute information about dual-factor screening practices, we examined the associations between a student’s subjective well-being (SWB) and both strength- and symptom-focused measures. Due to inconsistencies in the literature, the relation between gender and SWB was also examined. This study aims to further the discussion of the value of understanding complete mental health when serving all students in schools.

School-Based Universal Screening for Mental Health: Early Identification and Prevention

Recent research has examined the contribution of universal screening as part of the early identification and prevention of students’ emotional-behavioral disorders (EBD; Donovan & Cross, 2002; Jenkins, Hudson, & Johnson, 2007). The detrimental effects of EBD on school-related outcomes are well documented—discipline referrals, suspensions, grade retentions (Bruns, Walrath, Glass-Siegel, & Weist, 2004; Knoff, 2004), school dropout rates, and involvement in juvenile justice system (Freudenberg & Ruglis, 2007; Wasserman et al., 2004). However, traditional school-based mental health practices often identify students after they show EBD signs; therefore, opportunities for preventive early intervention are missed (Dowdy, Ritchey, & Kamphaus, 2010; Duncan, Forness, & Hartsough, 1995; Rones & Hoagwood, 2000; Wagner, Kutash, Duchnowski, & Epstein, 2005).

Practitioners, researchers, and policy analysts identify schools as a pragmatic setting for early identification and the delivery of mental health prevention programs (Levitt, Saka, Romanelli, & Hoagwood, 2007; Manion, Short, & Ferguson, 2013)—especially, because schools are second to families in shaping children’s development and, pragmatically, are accessible settings to reach children and their parents (Casat, Sobolewski, Gordon, & Rigsby, 1999; Schwean & Rodger, 2013). Schools have a pivotal role in providing preventive mental health services (Flett & Hewitt, 2013; Leschied, Flett, & Saklofske, 2013). For example, in Canada, school-based mental health programs (e.g., the School-Based Pathway to Care model and the Innovative Psychology School Mental Health Initiative) proactively address students’ mental health issues through collaboration between educators, students/families, and mental health service providers (Kutcher & Wei, 2013; Millar, Lean, Sweet, Moraes, & Nelson, 2013). Evidence supporting positive outcomes associated with school-based mental health services includes youths exhibiting more prosocial behaviors and higher academic performance (Frey & George-Nichols, 2003; Hussey, 2006).

Students at risk for emotional and behavioral problems are usually identified and provided services after teachers conclude that a problem disrupts the normal educational process (Gerber & Semmel, 1984). However, a referral system based primarily on teacher judgments can have unintended consequences because students often receive services only after they develop problems that cannot be managed by their regular classroom teachers. Students with internalizing problems—depression, anxiety, or suicide ideation—in particular are not as easily recognized and referred as those with disruptive externalizing problems (Weist, Rubin, Moore, Adelsheim, & Wrobel, 2007). Students with subthreshold psychological symptoms that do not, at least initially, warrant a clinical diagnosis can be underidentified as well (Flett & Hewitt, 2013). Moreover, some students are not identified because of their personality style characterized by self-concealment and perfectionistic self-presentation (Flett & Hewitt, 2013) or self-stigma (i.e., internalized negative stereotypes about themselves) that lead them to avoid acknowledging their symptoms and seeking needed treatment (Hartman et al., 2013).

Conversely, universal screening seeks to assess all students in a school and to identify students who otherwise might have been missed by reliance on teacher referrals (Eklund et al., 2009). Although school professionals have recognized the value of systematic, proactive screening in support of early identification and intervention for academic concerns, universal screening for mental health risk is still not widely adopted. By way of comparison, universally screening for students’ academic development, such as reading fluency, is well-recognized and used more often in school settings (Schwean & Rodger, 2013). However, it is estimated that in the United States only 2% of schools carry out universal mental health screening (Romer & McIntosh, 2005).

Dual-Factor Mental Health Approach

When considering a youth’s mental health, a screener that assesses positive youth traits might benefit all students because every student, regardless of his or her level of distress, has strengths that can be identified and cultivated to foster flourishing mental health (Greenspoon & Saklofske, 2001; Renshaw et al., 2014; Suldo & Shaffer, 2008). Another potential advantage of including a strength-focused component in mental health screening is that it targets the prevention of problematic behaviors, or at least the reduction of symptom severity, rather than the treatment of existing symptoms (LeBuffe & Shapiro, 2004). Problem-focused approaches begin when EBD symptoms become apparent in a small subset of students. In contrast, strength-based approaches target all students and assess positive developmental skills or characteristics that, when fostered, protect against the emergence of problematic behaviors (LeBuffe & Shapiro, 2004).

Recent studies provide support for a multidimensional view of illness-health factors, in that elevations on one factor are not dependent on a decrease in the other (Antaramian, Huebner, Hills, & Valois, 2010; Greenspoon & Saklofske, 2001; Kia-Keating et al., 2011; Lyons, Huebner, Hills, & Shinkareva, 2012; Suldo & Shaffer, 2008). Recent efforts have examined the combination of student strengths and problems as part of a universal screening process designed to provide tiered intervention and prevention services (Dowdy et al., in press). However, the relative advantages of using strength-focused and symptom-focused mental health screeners in combination are understudied. This study aims to contribute to the literature by investigating the relative contributions of strengths and symptoms as part of a school-based mental health screening process.

SWB: A Positive Indicator of Mental Health

SWB, a positive indicator of mental health, has been used in combination with a negative EBD symptom indicator to measure complete mental health (Suldo & Shaffer, 2008). Diener, Suh, Oishi, Lucas, and Smith (1999) described SWB as containing two components: an emotional or affective component comprised of positive affect (i.e., frequent positive emotions) and negative affect (i.e., infrequent negative emotions), and a conceptual or cognitive component comprised of global life satisfaction (Pavot & Diener, 2004). An individual with high SWB is expected to experience frequent positive emotions, infrequent negative emotions, and have a high level of overall life satisfaction (Long, Huebner, Wedell, & Hills, 2012).

Higher levels of SWB have been linked to various immediate and long-term positive outcomes (Long et al., 2012). Based on a comprehensive review of the literature by Lyubomirsky, King, and Diener (2005), SWB was associated with various positive life outcomes—occupational success, positive mental and physical health, satisfying interpersonal relationships, prosocial behavior, physical well-being, coping, and problem solving. High positive affect has been reported to be associated with reduced health symptoms and pain, lower morbidity (Pressman & Cohen, 2005), reduced sleep problems (Nes, Roysamb, Reichborn-Kjennerud, Tambs, & Harris, 2005), less internalizing and externalizing behaviors, and increased self-efficacy and self-esteem (Huebner, 2004). Furthermore, high SWB levels were found more often in people with certain personality traits such as cooperation, confidence, creativity, tolerance, and altruism (Cohen & Pressman, 2006; Lyubomirsky et al., 2005).

Contradictory results have been reported about the relation between gender and SWB. In a recent study with Lithuanian adolescents (Šarakauskienė & Bagdonas, 2010), gender was significantly related to SWB. Other studies have also found that gender contributes to and has significant influence on positive psychological strengths and SWB (Diener et al., 1999; Myers & Diener, 1995). In contrast, some other studies have found that overall SWB levels do not vary by gender (Karatzias, Chouliara, Power, & Vivien Swanson, 2006). Thus, more research is needed to better understand how gender is related to SWB.

Study Purpose

We investigated the relative associations of a strength-focused measure (Social Emotional Health Survey [SEHS]; Furlong, You, Renshaw, Smith, & O’Malley, 2013) and a symptom-focused measure (Behavior Assessment System for Children–Second Edition [BASC-2] Behavioral and Emotional Screening System [BESS]; Kamphaus & Reynolds, 2007) with high school students’ SWB. Due to the limited understanding on the gender effect on SWB, we also examined how gender is related to SWB. As the outcome variable was a positive indicator of mental health, we expected that the strength-focused measure would explain more of its variation than a symptom-focused measure; however, we anticipated that the symptom-focused measure would explain some additional unique variance. Such an outcome would support the combined use of strength- and symptom-focused approaches in screening for complete mental health screening.

Method

Participants

A total of 123 students from 10th grade completed surveys in the fall of 2011. Two students were removed from the sample because they used the response options of only some or hardly any to the reliability check question: “How many questions in this survey did you answer honestly?” In addition, during data screening, three students were identified as outliers, with extremely elevated levels (T-scores above 80) on the BESS, and removed from the sample. The 118 students who indicated that they responded honestly to all questions or most questions were included in the analysis. The final sample (n = 118) was comprised of slightly more females (56%) than males (44%) with a mean age of M = 15.1 years (SD = 1.5). The sociocultural backgrounds of the students were 24% White only, 12% Hispanic only, 10% Mixed-White/Hispanic, and 50% Mixed-Other.

Measures

BASC-2 BESS student form

The BESS student form (Kamphaus & Reynolds, 2007) is a widely used instrument with good psychometric properties that is designed to measure students’ behavioral and emotional symptoms. It has 30 items assessing internalizing, externalizing, school problems, and personal adjustment. Students respond using a 4-point scale: 1 = never, 2 = sometimes, 3 = often, and 4 = almost always. In the present study, the BESS was used as a global measure of student reported EBD symptoms. Higher scores on the BESS indicate elevated behavioral and emotional risk. The BESS total score alpha was .88 for the current sample. Average BESS item scores ranged from 0.03 to 2.0 (M = 1.0, SD = 0.4).

SEHS

The SEHS was used as a measure of positive psychological functioning. In previous studies, Furlong et al. (2013) and You et al. (2013) identified the most psychometrically robust items that measure 12 core positive psychological dispositions (self-awareness, grit, self-efficacy, peer support, teacher support, family support, empathy, emotional regulation, delay of gratification, gratitude, zest, and optimism), which mapped onto a single second-order factor, covitality. Two independent confirmatory factor analyses supported this measurement model with full factorial invariance for males and females and younger and older adolescents. As the current study used a preliminary version of the survey before the SEHS was finalized, the form used in this study included 31 of the final 36 SEHS items. We examined the correlation of these 31 items with the full 36 items using two independent samples (n = 6,429) reported by Furlong et al. (2013) and You et al. (2013) and found a correlation of .99. The SEHS total score alpha was .90 for the current sample. Average SEHS item scores ranged from 2.0 to 4.0 (M = 3.1, SD = 0.4).

SWB

SWB is widely used as a positive indicator of mental health. To measure SWB in this study, the affective component was measured using seven words that describe feelings and emotions from the Positive and Negative Affect Scale for Children (PANAS-C; Laurent et al., 1999), either positively (i.e., cheerful, lively, and happy) or negatively (i.e., scared, lonely, gloomy, and nervous). Answers indicated to what extent the respondent has had each feeling on a regular day. Items were rated using a 5-point scale: 1 = not at all, 2 = a little, 3 = moderately, 4 = quite a bit, and 5 = extremely. The PANAS-C has demonstrated adequate psychometric properties including internal consistency, and moderate convergent and discriminant validity (Laurent et al., 1999).

The satisfaction component of SWB was assessed using four of seven items from the Students’ Life Satisfaction Scale (SLSS; Huebner, 1991a, 1991b; “My life is going well,” “I would like to change many things in my life,” “I wish I had a different kind of life,” and “I have a good life”). The response options were 1 = strongly disagree, 2 = moderately disagree, 3 = mildly disagree, 4 = mildly agree, 5 = moderately agree, and 6 = strongly agree. The SLSS has sound psychometric properties across elementary, middle, and high school populations (Bender, 1997; Haranin, Huebner, & Suldo, 2007; Huebner, Funk, & Gillman, 2000). The four negative PANAS affective questions and the two negatively stated SLSS questions were reverse coded.

The 11 total items (four negative affect, three positive affect, and four life satisfaction) were combined to compute a total score—higher scores indicated higher SWB. The SWB total score alpha was .82 for the current sample. Average SWB item scores ranged from 2.2 to 5.3 (M = 3.8, SD = 0.8).

Procedures

The data were collected as part of a Safe Schools/Healthy Students (SS/HS) initiative project that was implemented in school districts located in the central coast of California, USA (Sharkey et al., 2012). This federal SS/HS initiative encourages schools and communities to design and implement comprehensive programs to improve school safety, reduce substance use, and enhance students’ social-emotional well-being. Following university Institutional Review Board (IRB) approval and parental consent, as part of the SS/HS evaluation activities, a small subgroup of students completed the self-report questionnaires. All self-report questionnaires were completed during one session during a regular school day.

Statistical Analyses

Two hierarchical multiple linear regressions were performed to assess how the SEHS and BESS were associated with SWB, after controlling for the influence of gender. Gender was entered in the first block (male = 0, female = 1) in both analyses. In the first analysis, the SEHS was entered in the second block and the BESS was entered in the third block. The researchers decided the order of entry because the study aimed to examine (a) how a strength-based screening approach predicts SWB and (b) whether a deficit-based approach significantly improved the prediction. In a second analysis, the BESS was entered in the second block and the SEHS was entered in the third block to examine (a) how a symptom-focused screening approach predicts SWB and (b) whether a strength-based approach significantly improved the prediction. Prior to performing the analyses, preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity, and homoscedasticity. The independent variables (i.e., the SEHS and the BESS) were mean-centered to facilitate interpretations.

Results

Associations With SWB

Results indicated that there was a significant correlation between the SEHS and SWB, r(116) = .57, p < .01, between the SEHS and the BESS, r(116) = −.55, p < .01, and between SWB and the BESS, r(116) = −.54, p < .01 (all r’s were large effect sizes). In the first analysis, gender was entered at Step 1, explaining insignificant variance in SWB (β = .11, p > .05), indicating that there was no gender difference in predicting SWB levels. In Step 2, the SEHS score was entered, and it accounted for additional 32% of the variance in SWB with higher scores associated with higher levels of SWB (β = .57, p < .001). Adding the BESS in Step 3 resulted in a significant R²_Δ = .08, F_Δ (1, 114) = 14.44, p < .001. Higher scores on this scale were significantly associated with lower SWB (β = −.33, p < .001). In the final model, both surveys were significant predictors (accounting for 40% of the variance in SWB) with the SEHS having the highest beta value (β = .38, p < .001), indicating that the SEHS was the better predictor of SWB. A second regression analysis was performed in the same manner as the first one except that the BESS score was entered at Step 2 and the SEHS was entered at Step 3 to explore the relative unique contributions of the BESS and SEHS to account for variation in SWB. The results indicated that the SEHS accounted for 10% of the variance net of the BESS, and the BESS accounted for 8% of the variance net of the SEHS; hence, the SEHS accounted for slightly more unique variance.

Given the amount of shared variance between the SEHS and the BESS, we explored the practical meaning of the regression analyses by determining the overlap of students with critical scores (≥1 SD for the BESS total score; ≤1 SD for the SEHS total covitality index; based on mean SEHS scores reported for a sample of more than 12,040 students; You, Furlong, Felix, & O’Malley, in press). If the 118 students in the study’s sample had been screened for possible mental health needs, 25 (21.2%) of them would have had critical-level scores: 15 (12.7%) with elevated BESS scores only, 7 (5.9%) with very low SEHS scores only, and 3 (2.5%) with critical scores on both measures.

Discussion

Results indicate that both the strength-focused and symptom-focused approaches to mental health screening predicted SWB when used separately, but when both approaches were used in combination, prediction was significantly improved. This finding provides some support for a dual-factor approach to school-based universal mental health screening with adolescents. When used alone, the SEHS provided information about how students normally feel and perceive their lives slightly better than the BESS, although there was a substantial amount of shared variance between the two measures. The SEHS positive dispositions were, as expected, more closely associated with youths’ emotional and cognitive evaluation of their quality of life. However, adding information about youths’ experienced distress symptoms significantly increased prediction, as both the SEHS and BESS were significant predictors of SWB. That is, the combination of strength-focused and deficit-focused approaches provided the optimal prediction of SWB. In particular, in this nonreferred adolescent sample, 5.9% of the students reported very low responses on the SEHS and normal range scores on the BESS. These youths have been described as languishing (Keyes, 2006) or vulnerable (Lyons et al., 2012) and would have been missed by a traditional deficit-based mental health screener.

Previous research has advocated for the integrated measurement of both positive and negative indicators in mental heath assessment (Antaramian et al., 2010; Eklund, Dowdy, Jones, & Furlonf, 2011; Greenspoon & Saklofske, 2001; Suldo & Shaffer, 2008). Schwean and Rodger (2013) also suggested that if a goal of mental health research is to promote wellness and prevent and intervene to ameliorate mental health problems, a balanced approach that focuses on promoting positive mental health of all children, as well as preventing and treating mental health distress is necessary. Our finding that the integration of both strength- and symptom-focused measures predicted SWB in the sample better than one measure alone provided further support for a dual-factor approach that assesses both personal strengths and psychological distress when evaluating youths’ complete mental health.

Limitations

The size and limited diversity of the sample limits generalizability of this study’s findings. There are some concerns for using self-report measures because these data can be influenced by situational factors and the mood of the child at the time when the measurement was taken (Smith & Handler, 2007). Furthermore, the dual-factor approach of mental health described in this study used methodology consistent within the literature but was not based on factor analysis, which can be another future research direction. Finally, results of the current study do not generalize to other positive or negative indicators of quality of life or mental health functioning.

Implication for Researchers and Practitioners

The application of an integrated approach to mental health assessment has implications for practitioners and researchers. First, we suggest that the goal of universal school-based mental health screening should be to focus on optimal mental health of all students. The results of this study indicated that when mental health screening aims to assess “health,” a strength-focused measure like the SEHS provided more information than a symptom-focused measure. Another important implication is that a strength-focused approach has potential benefits for all students (Renshaw et al., 2014) because every student has strengths that can be identified and enriched. One direction for future research and practice is to further evaluate how screeners that measure positive aspects of youths’ psychological experiences, and those that are symptom-focused, can be optimally included in an integrated, multitier assessment, prevention, and intervention system (Dowdy et al., in press).

Although more discussion is needed to determine at which stage of universal screening that a strength-based assessment should be utilized (e.g., at the first stage with all students vs. at the second stage after identifying students with deficits), this study supports the value of including strength-based information in the universal screening assessment. Most importantly, the integration of strength-focused screening measures like the SEHS could enable school practitioners to consider the complete mental health of all students, while attending to the distress and symptoms that some students experience. It also alters the purpose of screening to focus more attention on what is right with students than what is wrong with them.

Footnotes

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.

Author Biographies

Eui Kyung Kim, MA, is a doctoral student in the Counseling, Clinical, and School Psychology Department at the University of California, Santa Barbara. She is interested in school-based early identification and prevention services for complete mental health in youth.

Michael J. Furlong, PhD, is a professor in the Counseling, Clinical, and School Psychology Department at the University of California, Santa Barbara and the director of the Center for School-Based Youth Development.

Erin Dowdy, PhD, is an associate professor in the Counseling, Clinical, and School Psychology Department at the University of California, Santa Barbara. She conducts research on early identification methods for complete mental health.

Erika D. Felix, PhD, is an assistant professor in the Department of Counseling, Clinical, and School Psychology at the University of California, Santa Barbara. Her research interests include school bullying and peer victimization, disaster mental health, and school-based prevention and intervention services for youth.

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