Predicting and Preventing Problem Behaviors in High School Classrooms
Mental Health Screening and School Behavior: Predicting and Preventing Problem Behaviors in High School Classrooms
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Mental health difficulties have become a common problem in primary and secondary schools throughout the United States. Meanwhile, the research literature is beginning to draw connections between a student’s poor mental health and his or her likelihood to engage in disruptive classroom behaviors. In response, schools are beginning to implement universal mental health screening to identify and refer students who would benefit from either in-school or community-based mental health services.
The present study draws on research to seek connections between negative classroom behaviors and scores on universal mental health screening measures at one Massachusetts high school. These preliminary findings show that students, who frequently engage in negative classroom behaviors, are less likely to participate in mental health screening and are more likely to show evidence of mental health difficulties than their peers. This study uses data from a school-wide behavioral support program and a school-wide mental health screening program.
Keywords: mental health, universal screening, classroom behavior, positive behavior intervention and supports, non-office disciplinary referrals,
Disclaimer
Before we get into it, please consider a few quick DISCLAIMERS.
- This is an academic paper written by Lauren McDonagh-Pereira as a final project in a graduate school counseling program.
- This article does NOT represent medical advice.
- This article has NOT been formally reviewed.
- All theories, opinions, and research in this article are the personal opinions of Lauren McDonagh-Pereira. They do not represent the opinions of any organization, school, or professional body.
- This paper was written in 2017,
- This article has been published to create a conversation about the importance of addressing mental health in schools.
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Scope of the Problem
Mental health problems are a serious and growing concern within American schools. According to the Centers for Disease Control (CDC) thirteen (13%) to twenty percent (20%) of American children experience a mental disorder each year (2013). Further, as many as twenty percent (20%) of children between the ages of eight and fifteen years-old will experience a serious mental health disorder during their school years (Powers, Swick, Wegmann, & Watkins, 2016). Across the lifespan, half of all adult cases of mental illness began during childhood or adolescence (Humphrey & Wigelsworth, 2016).
The CDC (2013) defines mental health in children as being characterized by “the achievement of developmental and emotional milestones, healthy social development, and effective coping skills, such that mentally healthy children have a positive quality of life and can function well at home, in school, and in their communities” (p. 3). Within this context, mental health disorders are “serious deviations from expected cognitive, social, and emotional development” (CDC, 2013, p. 3).
The most common mental health problems among school-aged children in the United States are attention-deficit/hyperactivity disorder, behavioral and conduct disorders, anxiety, major depressive disorder, autism spectrum disorders, and Tourette syndrome (CDC, 2013). Each year, in the United States, behavioral disorders such as ADHD and conduct disorder impact 10.3% of school-aged children, while mood disorders and anxiety disorders affect 5.2% of children (CDC, 2013). Children and adolescents who experience mental health disorders are at an increased risk of experiencing a myriad of problems at school, at home, and in future workplaces.
Multiple studies have found significant correlations between mental health disorders, such as depression and anxiety, and externalized behavioral problems, such as school rule-violation and conduct disorders (CDC, 2013). Students with mental health problems often have difficulties with attention, school-adjustment, aggressive behaviors, opposition to authority, and hostility to teachers, parents and peers (CDC, 2013). Students who report symptoms of depression face a greater risk of academic failure, substance use and abuse, delinquency behaviors, and interpersonal difficulties (Kofler et al., 2011). Adolescents who experience mental health problems tend to have lower levels of academic performance, an increased risk of exhibiting severe behavior problems, and have poorer social relationships than their mentally healthy peers (Von der Embse, Pendergast, Kilgus, & Eklund, 2015).
Other studies have found that when mental health problems result in a decrease in a student’s emotional or behavioral engagement at school, the student is at a significantly greater risk of dropping out of school (Want, & Fredricks, 2014). In fact, “approximately one-half of failures to complete secondary education are attributable to psychiatric disorders” (Husky, Kaplan, McGuire, Chrostowski, & Olfson, 2011, p. 505).
In light of this information, it is imperative that schools learn to recognize mental health illness in students, and are able to provide appropriate in-school support, counseling, and referrals to mental health treatment within the larger community. The CDC (2013) states that, in regards to mental health, “surveillance efforts are critical for documenting the impact of mental disorders, and for informing policy, prevention, and resource allocation” (p. 2). One way in which schools can keep an appropriately watchful eye on student mental health and well-being is through universal mental health screening.
Connections to Negative Behavior
A strong connection between poor mental health and negative behaviors is a well-documented phenomenon in educational and psychological research literature (Blain-Arcaro & Vaillancourt, 2016). For example, a study by Brooks, Harris, Thrall, and Woods (2007) surveyed mental health and risk behavior in high school students. For female students, the researchers found significant correlations between self-reported symptoms of anxiety or depression and tobacco use, early sexual activity, and not using birth control during sexual activity. For male students, the researchers found correlations between depression or anxiety symptoms and involvement in physical fights, not using contraceptives during sexual intercourse, tobacco use, and absences from school.
Similarly, Fletcher et al. (2014) found correlations between mental health self-report data and cyberbullying. The researchers surveyed students between the ages of 11 and 16 about cyberbullying, mental health symptoms, and other demographic data. Among the findings, there were significant correlations between being the perpetrator of cyberbullying and lower scores on measures of emotional and psychosocial well-being. There were also significant correlations between perpetrating cyberbulling and self-reporting symptoms of Attention-Deficit Hyperactivity Disorder (ADHD) and Conduct Disorder.
Studies focusing specifically on behaviors occurring within school settings have found that symptoms of mental illness are closely tied to disruptive classroom behaviors, poor academic performance, lower teachers ratings of social and behavioral development, decreased levels of school engagement, increased physical and relational aggression, delinquent behaviors, poor school attendance, and rates of being suspended from school (Ballard, Sander, & Klimes-Dougan, 2014; Blain-Arcaro & Vaillancourt, 2016; Connors-Burrow et al., 2012; Guzman et al., (2010); Kofler et al., 2011; Powers et al., 2016; Want & Fredricks, 2014).
Connors-Burrow et al. (2012) investigated the links between mental health consultations for preschool teachers and the rate of disruptive behaviors experienced in a preschool classroom. The researchers created a three year-teacher training program, in which preschool teachers met weekly with a mental health service provider who taught them how to better care for the mental health needs of their pre-school aged students. Teachers were periodically surveyed about classroom climate and the rates of disruptive behaviors occurring in their classrooms. A control group of preschool teachers who were not receiving any mental health consultation were also surveyed. At the end of the three year mental-health training, teachers in the consultation group reported significantly fewer disruptive behaviors occurring in their classrooms than did the teachers who did not receive any mental health training. Conners-Burrow et al. (2012) postulated that the reductions in students’ disruptive behaviors were due to their teachers improved abilities to recognize and offer supports for students’ mental health symptoms.
Guzman et al. (2011) researched the effect of mental health illness on students’ academic performances. In their study, 7,903 elementary school students in Chile were given a mental health screener in first grade. The results of this screen were then compared to the students’ standardized math, science, and language test scores in fourth grade. The researchers found a significant correlation between indices of poor mental health in first grade, and low standardized test scores in fourth grade. Based on these data, the researchers concluded that poor mental health is predictive of later poor academic performance.
The link between mental health needs and teacher ratings of social and behavioral progress was researched by Powers et al. (2016). The researchers compared data from urban elementary schools in the United States. Of the 323 students in their sample, the researchers found that the students with the most frequent referrals to in-school mental-health support services, were also the students with the lowest average teacher ratings of social and behavioral development on their report cards. Powers et al. (2016) interpreted this finding to mean that students who exhibit greater mental health needs also tend to exhibit greater negative classroom behaviors and signs of inferior social development.
Want and Fredricks (2014) surveyed 7th through 11th graders attending American high schools to determine whether their level of emotional engagement with school had an effect on their behavior. The researchers defined “emotional engagement” as “identification with school, which includes belonging, enjoyment of school learning, and valuing or appreciation of success in school related outcomes” (Want & Fredricks, 2014, p. 722). They found that the students who reported lower levels of emotional engagement with school were significantly more likely to report delinquent behavior and drug use, and were significantly more likely to drop out of school. Want and Fredricks (2014) suggest that identifying students with decreased levels of emotional engagement in school, and helping them to increase their levels of engagement could have a positive effect on their behavior both in and outside of the classroom.
A 2016 study by Blain-Arcaro and Vaillancourt surveyed the correlation between depression and aggression in children and adolescents. Six hundred ninety-eight (698) students were surveyed yearly between the ages of ten and seventeen. Both parents, and children completed a phone interview in which they reported symptoms of depression and symptoms of both physical and relational aggression. The researchers found statistically significant correlations between depression and physical aggression, and between depression and relational aggression in any given year. They also found statistically significant correlations between depression and both physical and relational aggression across time. This research suggests that identifying students who exhibit symptoms of depression may be an effective method of identifying and treating students who are likely to commit acts of aggression, and vice versa.
Depression and delinquency behaviors were compared in a 2011 study by Kofler et al. A nationwide sample of 3,604 adolescents between the ages of 12 and 17 were surveyed. Self-report and parent interviews were completed for each participant at initial contact, and then at one- and two-year follow-ups. The researchers found that the levels of depression reported during the initial interviews significantly predicted increases in delinquent behavior reports during the follow-up interviews. This finding provides further evidence for the idea that identifying and treating adolescent mental illness may prevent the occurrence of future negative behaviors.
Ballard et al. (2014) researched the effect of providing in-school mental health services on the school attendance and school suspension rates for high-risk high school students. One hundred fifty-nine (159) students, determined by their academic history as being at a risk of poor school attendance or frequent suspensions, were offered in-school mental health treatment by mental health clinicians. They were compared to 148 high-risk students who did not receive such services. By the end of the school year, students who had received mental health services were significantly less likely to be suspended from school, and had significantly better school attendance than students who did not receive in-school mental health services. In this study, student outcomes were significantly improved by a school-based mental health intervention.
At the extreme end of the behavior spectrum are students who attend alternative high schools because they have been expelled from their local schools for delinquent behaviors. Johnson and Taliaferro (2011) found that students attending alternative high schools, due to previous behavioral challenges, were at a high risk for poor mental health both during high school and during early adulthood after high school. These students were also at a high risk for substance abuse, sexual risk-taking, and violence.
Schools must work to improve student behavior to improve each individual student’s ability to find success in the classroom. Roffey (2016) notes, “defiance, disengagement and disruption are issues that undermine academic excellence across a whole school and need to be addressed for the benefit of all” (p. 30). In previous surveys, classroom teachers have expressed particular concern about student classroom behavior. A 2011 study asked teachers to rank their most pressing concerns regarding student mental health (Reinke, Stormont, Herman, Puri, and Goel). The number one mental health concern cited by the classroom teachers who participated in this survey was “Behavior problems, including disruptive, defiant, aggressive, and conduct problems” (Reinke et al., 2011, p. 6). In order to address these concerns, school counselors, teachers, and administrators must be prepared to use all of the tools at their disposals to improve students’ emotional, behavioral, and academic outcomes.
Previous research has established that student mental health is significantly correlated with behaviors that are detrimental to both an individual’s academic success and the school’s overall behavioral climate (Blain-Arcaro & Vaillancourt, 2016; Connors-Burrow et al., 2012). Research has also established the positive outcomes of engaging in school-based universal mental health screening (Humphrey & Wigelsworth, 2016). However, there is a paucity of research exploring the connection between adolescent scores on mental health screeners and minor disruptive behaviors that are fairly routine in high school classrooms. The present study seeks to find a correlation between student scores on universal mental health screening measures and student classroom behavior.
Universal Mental Health Screening
Schools throughout the United States, and other developed nations, are slowly beginning to adopt universal mental health screening programs (Humphrey & Wigelsworth, 2016). In these programs, every student in a particular grade, a set of grades, or even an entire school district is asked to either complete a mental-health self-assessment tool, or is screened based on a parent- or teacher-rated assessment tool, depending on the student’s age and intellectual ability (Humphrey & Wigelsworth, 2016). Proponents of universal mental health screening argue that it is superior to the typical method of waiting for a child’s mental health concerns to become obvious and severe enough to warrant parent or teacher referral to mental health services (Humphrey & Wigelsworth, 2016).
Humphrey and Wigelsworth (2016) outlined many of the benefits of universal health screening in schools. First, because attending school is compulsory in the United States, school screening methods are more likely to identify children who are at risk of developing mental health disorders than any other method of screening. Second, by screening to identify children who are in the early stages of mental health disorders, rather than only identifying students currently experiencing severe disorders, schools are able to offer, or refer out to, appropriate early interventions which have been shown to be both more effective and less expensive. Third, universal screening reduces the risk of some children with mental illness being overlooked because their symptom presentations seem less severe than others. Fourth, universal screening creates detailed baseline data to evaluate the effectiveness of data-driven interventions. Fifth, by screening everyone in a school, or a grade, the risk of unintentionally identifying students with mental health needs to their peers is greatly reduced.
Humphrey and Wigelsworth (2016) went on to state, “it has been argued that a critical prerequisite to providing effective school-based prevention and intervention services is the adoption of a population-based approach embodied by a universal screening model” (p. 24). As of 2016, only 2% of American school systems regularly use a universal mental health screening program (Humphrey & Wigelsworth, 2016). However, within schools that do use universal screening methods, 85% of the teachers and administrators believe that they effectively identify students with mental health needs, who then benefit from follow-up treatment (Humphrey & Wigelsworth, 2016).
A 2011 study by Huskey et al. explored the effectiveness of a universal mental-health screen on ninth (9th) grade students. The researchers conducted their study based upon previous findings stating that only one-third (⅓) of students who require mental health services are ever identified by school staff. This is especially problematic in light of other research showing that a majority of children and adolescents who receive mental health services, receive those services in school, either from a school counselor, school psychologist, school social worker, adjustment counselor, or a community mental health clinician working on-site. School provides many students with their primary access to mental health services. It is therefore, imperative that schools are able to properly identify and refer students in need.
Huskey et al. (2011) compared 9th grade students at two high schools that were demographically similar. At one school, 365 students were asked to complete a universal mental health screener. At the other school, 291 students were simply made aware of the mental health support services available within the school. The researchers found that students in the universal screening condition were significantly more likely to get a referral for either in-school mental health support, or community mental health counseling than were students in the control group. Huskey et al. concluded that brief, universal screening is a more effective method of identifying and offering treatment to students with mental health needs than waiting for teachers to identify students in need, or waiting for students to refer themselves.
Universal mental health screens offer benefits to individual students in need, and to teachers and the larger school community, because mental health disorders in children and adolescents has been empirically linked to negative classroom behaviors (Von der Embse et al., 2015). Inappropriate classroom behaviors then have direct, negative impacts on a school’s culture and climate. By utilizing universal mental health screening, schools may be able to identify students who are at risk for engaging in disruptive classroom behaviors, and enroll them in appropriate treatments to improve their mental health status and mitigate the behavioral effects of poor mental health.
Connections to Negative Behavior
A strong connection between poor mental health and negative behaviors is a well-documented phenomenon in educational and psychological research literature (Blain-Arcaro & Vaillancourt, 2016). For example, a study by Brooks, Harris, Thrall, and Woods (2007) surveyed mental health and risk behavior in high school students. For female students, the researchers found significant correlations between self-reported symptoms of anxiety or depression and tobacco use, early sexual activity, and not using birth control during sexual activity. For male students, the researchers found correlations between depression or anxiety symptoms and involvement in physical fights, not using contraceptives during sexual intercourse, tobacco use, and absences from school.
Similarly, Fletcher et al. (2014) found correlations between mental health self-report data and cyberbullying. The researchers surveyed students between the ages of 11 and 16 about cyberbullying, mental health symptoms, and other demographic data. Among the findings, there were significant correlations between being the perpetrator of cyberbullying and lower scores on measures of emotional and psychosocial well-being. There were also significant correlations between perpetrating cyberbulling and self-reporting symptoms of Attention-Deficit Hyperactivity Disorder (ADHD) and Conduct Disorder.
Studies focusing specifically on behaviors occurring within school settings have found that symptoms of mental illness are closely tied to disruptive classroom behaviors, poor academic performance, lower teachers ratings of social and behavioral development, decreased levels of school engagement, increased physical and relational aggression, delinquent behaviors, poor school attendance, and rates of being suspended from school (Ballard, Sander, & Klimes-Dougan, 2014; Blain-Arcaro & Vaillancourt, 2016; Connors-Burrow et al., 2012; Guzman et al., (2010); Kofler et al., 2011; Want & Fredricks, 2014).
Connors-Burrow et al. (2012) investigated the links between mental health consultations for preschool teachers and the rate of disruptive behaviors experienced in a preschool classroom. The researchers created a three year-teacher training program, in which preschool teachers met weekly with a mental health service provider who taught them how to better care for the mental health needs of their pre-school aged students. Teachers were periodically surveyed about classroom climate and the rates of disruptive behaviors occurring in their classrooms. A control group of preschool teachers who were not receiving any mental health consultation were also surveyed. At the end of the three year mental-health training, teachers in the consultation group reported significantly fewer disruptive behaviors occurring in their classrooms than did the teachers who did not receive any mental health training. Conners-Burrow et al. (2012) postulated that the reductions in students’ disruptive behaviors were due to their teachers improved abilities to recognize and offer supports for students’ mental health symptoms.
Guzman et al. (2011) researched the effect of mental health illness on students’ academic performances. In their study, 7,903 elementary school students in Chile were given a mental health screener in first grade. The results of this screen were then compared to the students’ standardized math, science, and language test scores in fourth grade. The researchers found a significant correlation between indices of poor mental health in first grade, and low standardized test scores in fourth grade. Based on these data, the researchers concluded that poor mental health is predictive of later poor academic performance.
The link between mental health needs and teacher ratings of social and behavioral progress was researched by Powers et al. (2016). The researchers compared data from urban elementary schools in the United States. Of the 323 students in their sample, the researchers found that the students with the most frequent referrals to in-school mental-health support services, were also the students with the lowest average teacher ratings of social and behavioral development on their report cards. Powers et al. (2016) interpreted this finding to mean that students who exhibit greater mental health needs also tend to exhibit greater negative classroom behaviors and signs of inferior social development.
Want and Fredricks (2014) surveyed 7th through 11th graders attending American high schools to determine whether their level of emotional engagement with school had an effect on their behavior. The researchers defined “emotional engagement” as “identification with school, which includes belonging, enjoyment of school learning, and valuing or appreciation of success in school related outcomes” (Want & Fredricks, 2014, p. 722). They found that the students who reported lower levels of emotional engagement with school were significantly more likely to report delinquent behavior and drug use, and were significantly more likely to drop out of school. Want and Fredricks (2014) suggest that identifying students with decreased levels of emotional engagement in school, and helping them to increase their levels of engagement could have a positive effect on their behavior both in and outside of the classroom.
A 2016 study by Blain-Arcaro and Vaillancourt surveyed the correlation between depression and aggression in children and adolescents. Six hundred ninety-eight (698) students were surveyed yearly between the ages of ten and seventeen. Both parents, and children completed a phone interview in which they reported symptoms of depression and symptoms of both physical and relational aggression. The researchers found statistically significant correlations between depression and physical aggression, and between depression and relational aggression in any given year. They also found statistically significant correlations between depression and both physical and relational aggression across time. This research suggests that identifying students who exhibit symptoms of depression may be an effective method of identifying and treating students who are likely to commit acts of aggression, and vice versa.
Depression and delinquency behaviors were compared in a 2011 study by Kofler et al. A nationwide sample of 3,604 adolescents between the ages of 12 and 17 were surveyed. Self-report and parent interviews were completed for each participant at initial contact, and then at one- and two-year follow-ups. The researchers found that the levels of depression reported during the initial interviews significantly predicted increases in delinquent behavior reports during the follow-up interviews. This finding provides further evidence for the idea that identifying and treating adolescent mental illness may prevent the occurrence of future negative behaviors.
Ballard et al. (2014) researched the effect of providing in-school mental health services on the school attendance and school suspension rates for high-risk high school students. One hundred fifty-nine (159) students, determined by their academic history as being at a risk of poor school attendance or frequent suspensions, were offered in-school mental health treatment by mental health clinicians. They were compared to 148 high-risk students who did not receive such services. By the end of the school year, students who had received mental health services were significantly less likely to be suspended from school, and had significantly better school attendance than students who did not receive in-school mental health services. In this study, student outcomes were significantly improved by a school-based mental health intervention.
At the extreme end of the behavior spectrum are students who attend alternative high schools because they have been expelled from their local schools for delinquent behaviors. Johnson and Taliaferro (2011) found that students attending alternative high schools, due to previous behavioral challenges, were at a high risk for poor mental health both during high school and during early adulthood after high school. These students were also at a high risk for substance abuse, sexual risk-taking, and violence.
Schools must work to improve student behavior to improve each individual student’s ability to find success in the classroom. Roffey (2016) notes, “defiance, disengagement and disruption are issues that undermine academic excellence across a whole school and need to be addressed for the benefit of all” (p. 30). In previous surveys, classroom teachers have expressed particular concern about student classroom behavior. A 2011 study asked teachers to rank their most pressing concerns regarding student mental health (Reinke, Stormont, Herman, Puri, and Goel). The number one mental health concern cited by the classroom teachers who participated in this survey was “Behavior problems, including disruptive, defiant, aggressive, and conduct problems” (Reinke et al., 2011, p. 6). In order to address these concerns, school counselors, teachers, and administrators must be prepared to use all of the tools at their disposals to improve students’ emotional, behavioral, and academic outcomes.
Previous research has established that student mental health is significantly correlated with behaviors that are detrimental to both an individual’s academic success and the school’s overall behavioral climate (Blain-Arcaro & Vaillancourt, 2016; Connors-Burrow et al., 2012). Research has also established the positive outcomes of engaging in school-based universal mental health screening (Humphrey & Wigelsworth, 2016). However, there is a paucity of research exploring the connection between adolescent scores on mental health screeners and minor disruptive behaviors that are fairly routine in high school classrooms. The present study seeks to find a correlation between student scores on universal mental health screening measures and student classroom behavior.
Method
Participants
Participants were 1,311 high school students attending a large Massachusetts High School. Participants ranged in age from 14 years old to 19 years old, with 25.8% of the sample coming from the freshman class, 27.22% coming from the sophomore class, 25.14% coming from the junior class, and 21.79% coming from the senior class. Among the participants, 48.25% identify as female, and 53.02% identify as male. Sixty-two per cent (62%) of students were White, 30% of students were Hispanic, 5% of students were Asian, and 3% of students were Black.
Measures
Three measures were used to compare the mental health symptoms of the two groups of students; the Strengths and Difficulties Questionnaire (SDQ), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder 7-Item Scale (GAD-7) (Goodman, Ford, Simmons, Gatward, & Meltzer, 2003; Kroenke & Spitzer, 2002; Spitzer, Kroenke, Williams, & Lowe, 2006). All three measures are peer-reviewed, self-report surveys. They have been statistically validated for measuring symptoms of mental illness in adolescents.
The Strengths and Difficulties Questionnaire (SDQ) – The SDQ (see Appendix A) is a 25-item scale to measure externalizing and internalizing problems in children between the ages of 4 and 17 years old. (Goodman et al., 2003). It can be completed by a parent, a teacher, or an 11-17 year-old child. This study used the child-report version of the survey.
The survey is made up of five scales assessing emotional problems, conduct problems, hyperactivity, peer problems, and prosocial behavior (Youth in Mind, 2012). Each scale is scored by tallying the participants’ endorsements of five related statements. For example, a question under the emotional problems scale asks the child to determine how true the statement “I am often unhappy” is for the child (0 = not true, 1 = somewhat true, 2 = certainly true). Some questions are framed in reverse, such as the question “I finish the work I am doing” from the hyperactivity scale. In these instances, the score values are reversed with 2 = not true, 1 = somewhat true, and 0 = certainly true (for a detailed explanation of each item’s scoring, see Appendix B).
Each of the 5 scales results in a score between 0 and 10. The emotional problems scale, the conduct problems scale, the hyperactivity scale, and the peer problems scale are then added together to create a total difficulties score (0 to 40). These scores can be used as continuous variables, or organized into one of four categories: close to average, slightly raised/slightly lowered, high/low, or very high/very low (see Appendix C). In studies of community samples of children in the UK, 80% of children score as close to average, 10% score as slightly raised, 5% score as low, and 5% score as very low (Youth in Mind, 2012).
The Patient Health Questionnaire-9 (PHQ-9) – The PHQ-9 (see Appendix D) is a brief 9-item self-report survey to assess depression (Kroenke & Spitzer, 2002).
It is an adaptation from the longer patient health questionnaire (PHQ). The nine questions on the PHQ-9 represent the nine criteria that made up the DSM-IV’s diagnosis of major depressive disorder. Participants are asked how often they have been bothered by problems such as “Little interest or pleasure in doing things” over the last two weeks (0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day). The participant’s score is then tallied for a total score between 1 and 27, with 1-4 indicating no depression, 5-9 indicating mild depression, 10-14 indicating moderate depression, 15-19 indicating moderately severe depression, and 20-27 indicating severe depression (see Appendix E).
The Generalized Anxiety Disorder 7-Item Scale (GAD-7) – The GAD-7 (see Appendix F) is a 7-item measure for assessing generalized anxiety disorder (Spitzer et al., 2006). Participants are asked to answer how often, over the last two weeks they have been bothered by problems such as “Feeling nervous, anxious or on edge” (0 = not at all sure, 1 = several days, 2 = over half the days, 3 = nearly every day). A total score of 10 or greater is indicative of generalized anxiety disorder. Scores of 0-4 indicate no concern, 5-9 indicate mild anxiety, 10-14 indicate moderate anxiety and 15-21 indicate severe anxiety (Spitzer et al., 2006).
Apparatus
The SDQ, PHQ-9, and GAD-7 questions were typed into a GoogleForms document. All participants used school supplied IPADs to complete the online version of the survey. The answers that participants plugged into GoogleForms generated a GoogleSheets spreadsheet for each of the three surveys.
Procedure
This Massachusetts high school runs a bi-weekly social-emotional development course for all students. Twice a month, a class block is added into the schedule. During this time, all students go to an assigned classroom for small group discussions, lectures, and activities related to topics about social and emotional development as prescribed by the program’s curriculum. During three of these class meetings throughout the 2016/17 school-year, students were asked to complete the SDQ, the PHQ-9, and the GAD-7.
All parents in the school were sent letters, at the beginning of the school year, explaining the school’s universal mental health screening program and seeking informed consent for their child’s participation. The students who were not given consent were allowed to use their IPAD during the time of the survey in lieu of participation.
When students arrived in their social-emotional development class, they were instructed to take out their IPADs and click on a GoogleForms link that had been e-mailed to the entire student body. The classroom teachers explained that participation in the surveys were voluntary, and that any student who wanted to opt out, could simply quietly use their IPAD instead.
The link brought students to a GoogleForms document which asked them to identify their student ID number, their grade, and their assigned guidance counselor. This was followed by a typed version of the questions that appear on each of the three surveys. Students clicked to indicate their responses to each question. When all students in the class finished the survey, a follow-up lesson was conducted.
The guidance department sorted the incoming data to identify students who had indicated high or moderate levels of symptomatology as determined by the cut scores of the SDQ, PHQ-9, and the GAD-7. Any student who scored at the highest level of concern for one of the three surveys, met with his or her guidance counselor for a follow-up meeting within 24 hours of the survey. Any student who scored at a moderate level of concern, had a follow-up meeting with their guidance counselor within seven days of the survey administration. During these follow-up meetings students were screened, and if necessary parents were contacted to offer either community referrals, or brief in-school therapy.
Independent variable – The independent variable in this study is the students’ involvements in the school’s non-office disciplinary referral program. Students were either deemed as “Students without non-ODRs” or “non-ODR students”.
This high school is engaged in a Positive Behavioral Interventions & Supports (PBIS) program (OSEP, 2017). Within this program, the school seeks to define, teach, and support appropriate student behaviors. One facet of this program is referred to as “non-ODR referrals” or non-office discipline referrals. These are behavioral offenses that are disruptive to the school environment, but are not serious enough to warrant disciplinary action from the school’s administration. Examples of behaviors that would result in a non-ODR include cutting class for the first time, using a personal electronic device in class, or confrontationally arguing with a teacher or peer. Staff are asked to log all incidences of non-ODR behaviors. When a student’s name appears three times on the non-ODR tracking document, they are sent to meet with a positive behavioral support team. This team consists of an administrator, usually an associate principal, and a student support personnel, usually a school counselor. This meeting is not disciplinary in nature, and is instead meant to offer the student an opportunity to understand why his or her behaviors are disruptive, and to allow the student to discuss issues that led to the repeated disruptive behaviors.
In this study, students who had received the threshold three or more non-ODR referrals were compared to students who had received two or fewer non-ODR referrals between September 2016 and February 2017. Students who did not receive the threshold number of non-ODRs (subsequently referred to as “Students without non-ODR”) made up 94.68% of the student population, and 95.1% of the sample population. Students who did receive three or more non-ODRs (referred to as “non-ODR students”) accounted for 5.32% of the school population, and 4.9% of the sample.
Dependent variables –The dependent variables in this study are the students’ levels of participation and scores on the SDQ, PHQ-9, and the GAD-7. The study sought to determine whether being a member of the students without non-ODRs group or a member of the non-ODR students group resulted in the students’ scores on the three measures appearing more or less frequently at the higher ends of each survey’s symptom spectrum, and if group membership had an effect on the likelihood that students would participate in voluntary mental health screening.
Data Analysis
In the present study, data analysis was conducted to answer two questions. First, do students in the two groups opt out of participating in universal mental health screening at different rates. Second, do students in the two groups appear in the more concerning categories of the three surveys with different frequencies.
To answer the first question, a list of school ID numbers of every enrolled student was compared to the student ID numbers of the students who had participated in each of the three surveys. The ID numbers were then separated into the independent variable groups and compared using percentages.
To answer the second question, each student’s scores on the SDQ, PHQ-9, and GAD-7 were organized into one of the cut-score categories for each survey. For the SDQ, student scores were identified as either close to average, slightly raised, high or very high. For the PHQ-9, scores were categorized as no concern, mild symptoms, dysthymia/major depression (mild), major depression (moderately severe), or major depression (severe). For the GAD-7, students’ scores were organized into the groups no concern, mild anxiety, moderate anxiety or severe anxiety. The frequency with which students from the two groups (students without non-ODR and non-ODR students) appeared in each category was tallied and converted into a percentage of the sample.
Results
Student Participation – Most of the students (80.69%) completed at least one of the questionnaires. Of the school’s 1973 enrolled students, 33.55% completed all three surveys, while 19.31% opted out of all three surveys (see Table 1).
|
Student Participation Rates |
Student Population |
% |
Students w/out non-ODR |
% |
Non-ODR Students |
% |
|
Total Population |
1973 |
100.00% |
1868 |
94.68% |
105 |
5.32% |
|
Completed All 3 Surveys |
662 |
33.55% |
649 |
34.74% |
13 |
12.38% |
|
Opted Out of All 3 Surveys |
381 |
19.31% |
354 |
18.95% |
27 |
25.71% |
|
Completed SDQ (Oct. 2016) |
1344 |
68.12% |
1284 |
68.74% |
60 |
57.14% |
|
Completed PHQ-9 (Nov. 2016) |
1135 |
57.53% |
1099 |
58.83% |
36 |
34.29% |
|
Completed GAD-7 (Jan. 2017) |
943 |
47.80% |
914 |
48.93% |
29 |
27.62% |
Between groups, 34.74% of the students without non-ODRs completed all three surveys, while only 12.38% of non-ODR students completed all three surveys (see Figure 1).
Among the students without non-ODRs, 18.95% opted out of all three surveys, while 25.71% of non-ODR students opted out of all three surveys. A greater percentage of students without non-ODRs completed each of the three surveys than non-ODR students (68.12% vs. 57.14% for the SDQ; 58.83% vs. 34.29% for the PHQ-9, 48.93% vs. 27.62% for the GAD-7).
Strengths and Difficulties Questionnaire – A total of 1344 students participated in the SDQ. The students without non-ODRs group was made up of 1278 students or 95.09% of the sample (see Table 2).
|
SDQ (October 2016) |
Student Population |
% |
Students w/out non-ODR |
% |
Non-ODR Students |
% |
|
|
Sample |
1344 |
100% |
1278 |
95.09% |
60 |
4.46% |
|
|
Total Difficulties |
TD – Close to Average |
1014 |
75.45% |
973 |
76.13% |
41 |
68.33% |
|
TD – Slightly Raised |
153 |
11.38% |
145 |
11.35% |
8 |
13.33% |
|
|
TD – High |
65 |
4.84% |
61 |
4.77% |
4 |
6.67% |
|
|
TD – Very High |
106 |
7.89% |
99 |
7.75% |
7 |
11.67% |
|
|
Emotional Problems |
EP – Close to Average |
952 |
70.83% |
900 |
70.42% |
52 |
86.67% |
|
EP – Slightly Raised |
135 |
10.04% |
133 |
10.41% |
2 |
3.33% |
|
|
EP – High |
82 |
6.10% |
81 |
6.34% |
1 |
1.67% |
|
|
EP – Very High |
169 |
12.57% |
164 |
12.83% |
5 |
8.33% |
|
|
Conduct Problems |
CP – Close to Average |
1196 |
88.99% |
1153 |
90.22% |
43 |
71.67% |
|
CP – Slightly Raised |
61 |
4.54% |
57 |
4.46% |
4 |
6.67% |
|
|
CP – High |
49 |
3.65% |
44 |
3.44% |
5 |
8.33% |
|
|
CP – Very High |
38 |
2.83% |
30 |
2.35% |
8 |
13.33% |
|
|
Hyperactivity |
H – Close to Average |
1083 |
80.58% |
1046 |
81.85% |
37 |
61.67% |
|
H – Slightly Raised |
100 |
7.44% |
94 |
7.36% |
6 |
10.00% |
|
|
H – High |
90 |
6.70% |
80 |
6.26% |
10 |
16.67% |
|
|
H – Very High |
71 |
5.28% |
64 |
5.01% |
7 |
11.67% |
|
|
Peer Problems |
PP – Close to Average |
836 |
62.20% |
805 |
62.99% |
31 |
51.67% |
|
PP – Slightly Raised |
233 |
17.34% |
220 |
17.21% |
13 |
21.67% |
|
|
PP – High |
154 |
11.46% |
144 |
11.27% |
10 |
16.67% |
|
|
PP – Very High |
121 |
9.00% |
115 |
9.00% |
6 |
10.00% |
|
|
Prosocial |
Pro – Close to Average |
978 |
72.77% |
950 |
74.33% |
28 |
46.67% |
|
Pro – Slightly Lowered |
176 |
13.10% |
161 |
12.60% |
15 |
25.00% |
|
|
Pro – Low |
107 |
7.96% |
97 |
7.59% |
10 |
16.67% |
|
|
Pro – Very Low |
83 |
6.18% |
78 |
6.10% |
5 |
8.33% |
The non-ODR students group was comprised of 60 students or 4.46% of the sample. A greater percentage of non-ODR students scored as slightly raised (13.33% vs 11.35%), high (6.67% vs 4.77%), or very high (11.67% vs 7.75%) on the total difficulties scale (see Figure 2). Greater percentages of non-ODR students were also found on the slightly raised, high, and very high categories of the conduct problems scale, the hyperactivity scale, and the peer problems scale than students without non-ODRs (see Table 2).
Non-ODR students were also more likely to be rated as slightly lowered, low, or very low on the prosocial scale. Students without non-ODRs appeared more often in the slightly raised, high, or very high categories for the emotional problems scale.
Patient Health Questionnaire-9 – A total of 1135 students completed the PHQ-9, 1099 (or 96.83%) came from the students without non-ODRs group, and 36 (or 3.17%) came from the non-ODR students group (see Table 3).
|
PHQ-9 (Nov. 2016) |
All Student Participants |
% |
Students w/out non-ODR |
% |
Non-ODR Students |
% |
|
Sample |
1135 |
100% |
1099 |
96.83% |
36 |
3.17% |
|
No Concern |
706 |
62.20% |
688 |
62.60% |
18 |
50.00% |
|
Mild Symptoms |
247 |
21.76% |
237 |
21.57% |
10 |
27.78% |
|
Dysthymia/Major Depression, mild |
91 |
8.02% |
88 |
8.01% |
3 |
8.33% |
|
Major Depression, moderately severe |
60 |
5.29% |
57 |
5.19% |
3 |
8.33% |
|
Major Depression, severe |
31 |
2.73% |
29 |
2.64% |
2 |
5.56% |
Non-ODR students less frequently scored in the no concern category than students without non-ODRs (50% vs. 62.6%). Non-ODR students more frequently scored in the categories of mild symptoms (27.78% vs. 21.57%), major depression, mild (8.33% vs. 8.01%), major depression, moderately severe (8.33% vs. 5.19%) and major depression, severe (5.56% vs. 2.64%) than did students in the students without non-ODRs group (see Figure 3).
Generalized Anxiety Disorder 7-Item Scale – Nine hundred forty-three (943) students completed the GAD-7. The students without non-ODRs group consisted of 914 students (96.92% of the sample), while the non-ODR students group consisted of 29 students (3.08% of the sample) (see Table 4).
|
GAD-7 (Jan. 2017) |
Student Population |
% |
Students w/out non-ODR |
% |
Non-ODR Students |
% |
|
Sample |
943 |
100.00% |
914 |
96.92% |
29 |
3.08% |
|
No Concern |
575 |
60.98% |
558 |
61.05% |
17 |
58.62% |
|
Mild Anxiety |
193 |
20.47% |
192 |
21.01% |
1 |
3.45% |
|
Moderate Anxiety |
107 |
11.35% |
102 |
11.16% |
5 |
17.24% |
|
Severe Anxiety |
68 |
7.21% |
62 |
6.78% |
6 |
20.69% |
Students without non-ODRs appeared more frequently in the no concern (61.05% vs. 58.62%) and mild anxiety (21.01% vs. 3.45%) categories than did non-ODR students (see Figure 4). Non-ODR students were more likely to score in the moderate anxiety (17.24% vs. 11.16%) and the severe anxiety (20.69% vs. 6.78%) categories than students without non-ODRs.
Discussion
This study found a marked difference between students who do not engage in frequent negative classroom behaviors and students who do engage in frequent negative classroom behaviors in regards to student participation in universal mental health screening, and students’ scores on mental health screening measures. Students who had been flagged for at least three non-office disciplinary referrals were more likely, than their peers, to opt for universal mental health screening, and were more likely to have concerning scores on measures of depression, anxiety, conduct problems, hyperactivity, peer problems, and prosocial behaviors.
There are clear limitations to these preliminary findings. First, it is unknown, at this time, if the data collected has statistical validity. The findings are based solely on the frequency percentages with which the scores of the two groups of students ended up in different diagnostic categories. The data will need to be run through statistical models to determine whether or not there are any significant findings.
Second, the data is the result of an unknown self-selection bias. While students with non-ODRs more often scored at higher ends of the survey results spectrums, they were also much less likely to participate in the surveys. Perhaps, only the non-ODR students who suspect that they might suffer with depression or anxiety took the surveys as an attempt to get support from the school’s administration. It is possible, that non-ODR students who would have scored in the typical ranges on the surveys were the ones who decided to opt out.
Third, the non-ODR sample is drastically smaller than the students without non-ODRs sample. The reported findings based on percentages are a bit distorted when, for example, 29 non-ODR GAD-7 scores are compared to 914 GAD-7 scores. The participant pool needs to be expanded to include other schools who use the PBIS non-ODR tracking and support model, so that the survey scores of a larger population of non-ODR students can be assessed.
Regardless of the limitations of this study, the preliminary findings are informative for both future school counseling practice and future research. School counselors, teachers, and administrators who are aware of the possible mental health needs of behaviorally challenging students can take a different, more supportive approach to dealing with those students and their behaviors. Knowing that the student who is frequently late or cutting class may suffer from generalized anxiety disorder, or that the student who has given up on school and is constantly caught listening to his headphones in class may be suffering from major depressive disorder will help schools make measured and more nuanced responses to those negative behaviors. Based on the findings of this study, it seems that sending students to speak with school counselors as the first intervention when they begin engaging in disruptive classroom behaviors may be a worthwhile approach to take.
The findings show an interesting degree of difference between the two groups that creates as many questions as it answers. Future research could consider why students who engage in negative classroom behaviors are less likely to participate in universal screening and how to encourage them to participate. Future research could also try to understand how anxiety, depression, and internalizing and externalizing problems affect classroom behavior, and how treatment of these symptoms may remediate some of the behavior problems that usually lead to disciplinary consequences.
These preliminary findings show that students who engage in negative classroom behaviors may do so as a result of struggling with one or more mental illness. Future researchers should attempt to replicate and validate this data with a larger, more representative student sample. If these preliminary findings are found to be generalizable across different high school populations, they have serious implications about the way that school counselors, administrators, and teachers deal with behaviorally disruptive students and mental health resources within schools.
References
Ballard, K. L., Sander, M. A., & Klimes-Dougan, B. (2014). School-related and social-emotional outcomes of providing mental health services in schools. Community Mental Health Journal, 50(2), 145–149. DOI:10.1007/s10597-013-9670-y
Brooks, T. L., Harris, S. K., Thrall, J. S. & Woods, E. R. (2007). Association of adolescent risk behaviors with mental health symptoms in high school students. Journal of Adolescent Health, 31, 240-246
Blain-Arcaro, C., & Vaillancourt, T. (2016). Longitudinal associations between depression and aggression in children and adolescents. Journal of Abnormal Child Psychology. DOI:10.1007/s10802-016-0204-2
Center for Disease Control and Prevention (CDC). (2013). Mental health surveillance among children: United States, 2005–2011. Morbidity and Mortality Weekly Report, 62(2), 1–17. Retrieved from http://www.cdc.gov/mmwr/pdf/other/su6202.pdf
Conners-Burrow, N. A., Whiteside-Mansell, L., McKelvey, L., Amini-Virmini, E., & Sockwell, L. (2012). Improved classroom quality and child behavior in an Arkansas early childhood mental health consultation pilot project. Infant Mental Health Journal 33(3), 256-264. DOI: 10.1002/imhj.21335
Goodman, R., Ford, T., Simmons, H., Gatward, R., & Meltzer, H. (2003). Using the strengths and difficulties questionnaire (SDQ) to screen for child psychiatric disorders in a community sample. International Review of Psychiatry 15, 166-172.
Fletcher, A., Fitzgerald-Yau, N., Jones, R., Allen, E., Viner, R., & Bonell, C. (2014). Brief report: Cyberbullying perpetration and its associations with socio-demographics, aggressive behavior at school, and mental health outcomes. Journal of Adolescence 37, 1393-1398. DOI:10.1016/j.adolescence.2014.10.005
Guzman, M. P., Jellinek, M., George, M., Hartley, M., Squicciarini, A. M., Canenguez, K. M., Kuhlthau, K. A., Yucci, R., White, G. W., Guzman, J., & Murphy, J. M. (2010). Mental health matters in elementary school: First-grade screening predicts fourth grade achievement test scores. Euro Child Adolescent Psychiatry, 20, 401-411. DOI: 10.1007/s00787-011-0191-3
Humphrey, N. & Wigelsworth, M. (2016). Making the case for universal school-based mental health screening. Emotional and Behavioral Difficulties 21(1), 22-42. DOI:10.1080/13632752.1120051
Husky, M. M., Kaplan, A., McGuire, L., Flynn, L., Chrostowski, C., & Olfson, M. (2011). Identifying adolescents at risk through voluntary school-based mental health screening. Journal of Adolescence 34, 505-511. DOI: 10.1016/j.adolescence.2010.05.018
Johnson, K.E., & Taliaferro, L.A. (2012). Health behaviors and mental health of students attending alternative high schools: A review of the research literature. Journal for Specialists in Pediatric Nursing 17, 79-97. DOI: 10.111/j.1744.6155.2011. 00311.x
Kofler, M. J., McCart, M. R., Zajac, K., Ruggiero, K. J., Saunders, B. E., & Kilpatrick, D. G. (2011). Depression and delinquency covariation in an accelerated longitudinal sample of adolescents. Journal of Consulting and Clinical Psychology 79(4), 458-469.
Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals 32(9), 509-515.
OSEP Technical Assistance Center on Positive Behavioral Interventions and Supports (2017). Positive Behavioral Interventions & Supports. Retrieved from www.pbis.org
Reinke, W.M., Stormont, M., Herman, K.C., & Puri. N. (2011). Supporting children’s mental health in schools: Teacher perceptions of needs, roles, and barriers. School Psychology Quarterly 26(1), 1-13. DOI: 10.1037/a0022714
Roffey, S. (2016). Building a case for whole-child, whole-school well-being in challenging contexts. Educational & Child Psychology 33(2), 30-42.
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Lowe, B. (2006). A brief measure for assessing generalized anxiety disorder. Archives of Internal Medicine 166, 1092-1097.
Von der Embse, N. P., Pendergast, L. L., Kilgus, S. P., & Eklund, K. (2015). Evaluating the applied use of a mental health screener: Structural validity of the social, academic, and emotional behavior risk screener. Psychological Assessment 28(10), 1265-1275. DOI:10.1037/pas0000253
Want, M. T., & Fredricks, J. A. (2014). The reciprocal links between school engagement, youth problem behaviors, and school dropout during adolescence. Child Development 85(2), 722-747. DOI: 10.1111/cdev.12138
Youth in Mind (2012). What is the SDQ? Retrieved from http://www.sdqinfo.com/a0.html
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