General Trends in the Utilization of Mental Health Resources Within a Central Ohio School District

Abstract

Mental health has gained prominence, with educational institutions leading efforts through primary and secondary prevention. Legislation like the Mental Health Services Grant Program has allocated $144 million towards school resources. However, its effectiveness relies on factors like school climate, socioeconomic conditions, and cultural attitudes. In high-pressure academics, stigma and tight schedules deter help-seeking, while low-income students face barriers like transport and family support. Further, school policies, staff availability, and cultural attitudes shape student resource use. Understanding these is key to making funding effective while raising awareness. Therefore, the question posed is, what are the general trends in the utilization of mental health resources within a central Ohio school district? This study examines trends in utilization, focusing on two key resources: OSU Clinicians and School Social Workers. The OSU Clinician, a licensed mental health professional, provides clinical therapy for anxiety, depression, and trauma, while the School Social Worker connects students and families to resources and addresses social and emotional issues. Data was collected from four high schools, analyzing topics discussed, demographics, and visit frequency. The findings showed sophomores and female students used resources most, with anxiety, depression, and stress as top concerns. Racial disparities emerged: Caucasian students sought help most, followed by African American and Latino students, while Asian students sought it least. These insights call for targeted interventions and better resource allocation to address disparities and improve access. This study highlights the need to understand demographic trends to improve school support and conduct further research, such as on cultural factors affecting first-generation immigrant students.

1. Introduction

In recent years, mental health has become a growing concern among students in K-12 education. Reports indicate large waves of mental health issues among this population, with troubling statistics surfacing annually. In 2022 alone, approximately 4 million adolescents experienced at least one major depressive episode, representing about 16 percent of U.S. teens¹. The numbers become even more concerning when considering specific conditions such as anxiety, with 37 percent of U.S. high school students reporting symptoms of anxiety disorders². Additionally, suicide has emerged as the second leading cause of death among adolescents aged 10-14, underscoring the urgency of addressing mental health challenges in this population³. Despite increasing awareness and efforts to address these concerns, mental health challenges among students continue to rise, highlighting the need for more effective interventions.

Historically, mental health services in schools have evolved significantly. Initially, support was primarily academic, with guidance counselors focusing on career readiness and special education programs assisting students with learning disabilities. The 1975 Individuals with Disabilities Education Act (IDEA) marked a pivotal moment in this progression, ensuring students with disabilities received a free and appropriate public education, including special education services and related support⁴. Over time, the scope of student well-being expanded to include mental health, leading to the establishment of school-based counseling programs, psychological services, and intervention initiatives. For example, the 1990s saw the rise of comprehensive school mental health systems, which integrated mental health professionals into schools to provide direct services and support⁵. Today, mental health has become a legislative priority, with programs such as the School-Based Mental Health Services Grant Program allocating $144 million annually for five years to expand resources dedicated to student mental well-being⁶.

Schools play a unique and critical role in early identification and intervention for mental health issues. As students spend a significant portion of their time in school settings, educators and school counselors are often the first to notice behavioral and emotional changes that may signal underlying mental health concerns. Research indicates that early intervention through school-based services can prevent conditions from worsening, improve academic outcomes, and enhance students’ overall well-being⁷. For instance, school-based cognitive-behavioral therapy (CBT) programs have been shown to reduce symptoms of anxiety and depression in adolescents by up to 50 percent⁸. However, despite increasing financial investments and policy efforts, the prevalence of mental health issues among students has not shown a corresponding decline. This discrepancy raises critical questions about the accessibility, utilization, and effectiveness of school-based mental health services.

This study aims to analyze general trends in the utilization of mental health services within a single school district. By examining patterns in how these resources are used, the research seeks to understand why increased funding and availability of services have not resulted in a decrease in student mental health issues. Specifically, this study will explore the following sub-questions:

• How do various demographic groups utilize mental health resources?
• What are the most common mental health concerns presented by students?
• Are there patterns in the frequency or duration of service utilization?

By addressing these questions, this research will provide a foundation for future investigations into the effectiveness of school-based mental health services and potential areas for improvement. Understanding these dynamics is crucial in ensuring that mental health interventions within schools are both accessible and effective for all students.

2. Literature Review

Achieving a solution or explanation for the student mental health epidemic requires a complete understanding of this issue. This study aims to achieve this understanding by analyzing trends in the utilization of mental health resources within a singular central Ohio school district. The objective is to identify the groups most affected and the prevalent issues within high schools. Understanding utilization trends is pivotal in determining which groups require more funding for mental health support and which do not. Such insights can help optimize the allocation of resources, saving both money and time by ensuring effective utilization. However, this study is only the final puzzle piece in an already well-researched field.

Studies in mental health prevention began as early as the 1920s when schools started consulting and forming groups of mental health experts to guide students and treat mental health issues. This was referred to as secondary prevention, wherein schools aimed to manage existing but reversible conditions. The same model persists today, with schools employing psychologists, psychiatrists, and guidance counselors to assist students in addressing mental health challenges.

In addition to secondary prevention, schools have implemented primary prevention strategies, which are defined as efforts to prevent a mental health issue before it occurs. One early example is the “classroom experiment,” in which it was hypothesized that educating students on human behavior and relationships would lead to more fulfilling social interactions and, in turn, improved mental health. Although this experiment showed that students improved in their understanding of behavioral causes, it did not collect sufficient data to correlate this understanding with a reduction in mental health issues. Other studies, such as the Penn Resiliency Program (PRP) and the Resilience Builder Program (RBP), have explored similar primary prevention strategies. These studies emphasize qualitative approaches to evaluating prevention methods and focus on developing new strategies rather than assessing existing systems.

This focus on qualitative assessments has created a significant gap in the research. While many studies analyze the potential effectiveness of primary prevention methods, few quantitatively assess the utilization of existing mental health resources within school settings. Understanding these pre-existing systems through numerical data rather than hypothetical solutions is crucial. This study addresses that gap by conducting a correlational analysis of resource utilization within a specific school district. A quantitative approach allows for a clear, data-driven understanding of current mental health infrastructure, facilitating more targeted and effective future interventions.

In our study, we explicitly address a significant gap in the existing literature. While the prevalence of adolescent mental health issues and the effectiveness of prevention strategies have been extensively studied, there is limited research on the utilization trends of mental health resources within specific districts. Most existing studies either focus on national or regional data, which often overlook the unique geographic, demographic, and resource-based variability present at the district level⁸,⁹. For example, national studies may highlight broad trends in mental health service access but fail to account for localized barriers such as funding disparities, staffing shortages, or cultural stigmas that disproportionately affect certain districts¹⁰. Our study fills this gap by conducting a district-specific analysis of mental health service utilization within a central Ohio school district. We focus on a district where all high schools share the same mental health resources, allowing us to provide detailed, data-driven insights that can guide more targeted and effective mental health interventions.

The quantitative nature of this study is particularly important, as it enables us to move beyond anecdotal or hypothetical discussions and instead rely on empirical evidence to identify patterns and trends. By analyzing numerical data on service utilization—such as the frequency of counseling sessions, the types of mental health concerns most commonly reported, and the demographic breakdown of students accessing services—we can identify disparities and inefficiencies in the current system. For instance, prior research has shown that students from marginalized backgrounds, such as those from low-income families or racial/ethnic minority groups, are less likely to access mental health services despite experiencing higher rates of mental health challenges¹¹. A quantitative approach allows us to test whether these trends hold true within our specific district and, if so, to what extent.

Furthermore, this study’s focus on a single district provides a unique opportunity to examine how resource allocation and utilization vary across schools with similar infrastructure but potentially different student populations. For example, while all high schools in the district may have access to the same mental health resources, differences in student demographics, school culture, or community engagement could lead to significant variations in how these resources are used. By quantifying these differences, our research can inform district-level policies and practices that ensure equitable access to mental health services for all students.

This study addresses a critical gap in the literature by providing a quantitative, district-specific analysis of mental health service utilization. By focusing on a single district with shared resources, we aim to uncover patterns and trends that are often obscured in broader studies. This approach not only enhances our understanding of how mental health resources are currently being used but also provides a foundation for developing more effective and equitable interventions in the future.

Secondary prevention studies have also been prevalent in mental health research. Many experts propose tiered intervention models that provide a structured approach to addressing student mental health needs. However, these studies are typically conducted at the national level due to funding constraints, which results in broad recommendations rather than actionable, location-specific solutions. Geographic variation significantly impacts funding availability, political support, and overall mental health policy implementation, making it difficult to apply generalized findings to individual districts.

This study addresses that limitation by focusing on a specific school district in central Ohio, thereby eliminating geographic variability as a confounding factor. By narrowing the research to a local level, the study generates more actionable, district-specific data on the utilization of secondary prevention resources. This approach provides precise insights that can inform policy decisions and improve the effectiveness of mental health resource allocation within the district.

In summary, while research on adolescent mental health prevalence and prevention strategies is extensive, there is a lack of quantitative studies examining utilization trends within specific districts. This study fills that gap by providing data-driven insights into how existing mental health resources are accessed and used in a central Ohio school district. This understanding is essential for optimizing resource distribution and ensuring that future interventions are both effective and efficient.

Moreover, studies analyzing trends in mental health service utilization have also already been conducted. These studies, similar to this research study, have focused on analyzing the use of current resources to figure out the most effective solutions for helping treat mental health issues. Studies such as those conducted by Michael Repie, who has a Ph.D. in Special Education and is the Senior Director of Clinical Services at Dominion Hospital, utilize surveys filled out by students and faculty to get a broader understanding of mental health service utilization¹². The study conducted by Repie was also conducted nationwide with surveys being given out throughout the nation to get a better understanding of the utilization of certain resources on a national level. These national-level studies are common when trying to understand trends in utilization and all fall short due to one variable—geography. Similar to secondary preventive studies, studies analyzing trends are national in perspective, which causes many issues, such as some schools not containing enough staff or students to fill out the survey or some schools simply not having the resources the survey is testing. This leads to a large sum of studies containing inconclusive results¹². However, in this current study being conducted, the population size consists of the four high schools making up the school district, and all high schools contain the same resources. The resources being studied in particular in this study (who are also the participants in this study) are each school’s Ohio State University (OSU) clinician and School Social Worker.

2.1 Summary

This study finds a population gap within the current broader field of research. To understand the best solutions to decrease students with mental health issues, acknowledging current trends in the utilization of resources is pivotal, and this study aims to achieve this goal. This study situates itself within the gap in the current field of research for a correlational study that collects quantitative data through a localized population. Most primary prevention studies, such as those of Dr. Ojemann, are qualitative and address hypothetical new solutions to better mental health. The solutions from these studies could never be implemented effectively if the current state of resources is not analyzed to find the most effective ways of implementation. To address this issue, a quantitative study is necessary to show definitive numerical evidence of current issues. Furthermore, secondary prevention studies also face similar issues with the added issue of geography. Previous studies analyzing the current state of mental health infrastructure also fall short due to this reason. This study addresses this gap by using a smaller population, allowing for conclusive data. Addressing these gaps will provide a base understanding through data on current mental health infrastructure. This understanding will push future researchers to address other gaps in this field, such as effective solutions for the paradoxical situation of increasing expenditure being coupled with growing amounts of disturbed students. Hence, the best research question to ask, and the one posed in this research study, is: What are the general trends in the utilization of mental health resources provided by high schools within a central Ohio school district?

3. Research Design and Methodology

3.1 Study Design and Subjects

To answer the current question of inquiry, a correlational study method with an egocentric network data collection system was utilized. This approach allowed the researcher to collect large amounts of quantitative data about the student population while simultaneously having a manageable sample. An egocentric network is one in which the researcher has a sample group and collects data about the interactions members within this group had with others around them. This was implemented within this study through the utilization of a sample population that consisted of each high school’s OSU clinician and School Social Worker (eight individuals in total due to there being four high schools). These staff members meet with students within each high school who want to discuss or need help with mental health issues or general issues they were or are facing. With this egocentric system, the staff members filled out a questionnaire each time they met with a student rather than the students filling out the questionnaire themselves. This allows the researcher to collect lots of data about the student body while maintaining a small sample population. Furthermore, the alignment of this egocentric system with this current research question is provided in Figure 2.

As aforementioned in the literature review, a general issue most studies face is having very large populations. This causes the sample to be very hard to manage. Furthermore, it is very difficult to gain consistent data as available participants vary by region. Henceforth, this egocentric network is the most logical for the collection of the most conclusive data as it eliminates the varying sample population and the issue of geography by keeping a small eight-person sample while still allowing vast amounts of data to be collected. This method allows and promotes variation between buildings to analyze how resources are used differently per region while simultaneously eliminating the confounding variables of previous researchers, allowing the researcher to fill the gap in research for a conclusive quantitative data study.

3.2 Research Instruments

For this study, the researcher implemented a questionnaire that modeled the one used by Dr. Repie in his study¹². The researcher used this survey as the initial base guideline for this study. The researcher then met and discussed with his three expert advisors to create a new questionnaire that contained the key characteristics (such as mental health issues like depression and anxiety) of Dr. Repie’s questionnaire while also being better suited for the specific school district, student population, and guidelines within the district for what data is allowed to be collected. The expert advisors aiding the researcher were the researcher’s high school’s OSU Clinician, the School Social Worker, and the District Lead for mental health (who works for the central district office). The researcher’s questionnaire was validated by the expert advisors, the IRB, and the central district office before being utilized. The final result was a questionnaire that consisted of six questions [Appendix A]. The questionnaire consists of an initial question asking for the ID of the student whom the OSU Clinician or School Social Worker met with. The ID contains a random number, a letter to indicate the school, and another letter (A or B) to indicate whether the student met with the OSU clinician (A) or the School Social Worker (B). As this questionnaire can be filled out multiple times, this allows for the collection of the frequency of meetings while protecting confidentiality through student IDs.

Next, the questionnaire asks for general information such as race, grade level, and gender. These questions are asked to be able to analyze trends within each category. Finally, the remaining two questions ask which mental health concern best reflects the content of the session or which general issue best reflects the session (if not both). These questions will give insight into which mental health issues and general issues are most prevalent. This will help answer the question of inquiry as it provides clear-cut data about mental health and general issues in a quantitative form, which can be used to find trends in the utilization of resources. This is because the data allows researchers to see people of which group and which issues most often utilize mental health staff. This helps build a foundational understanding of the current mental health state.

3.3 Procedure

After approval from the IRB, the researcher followed the procedure conducted by Dr. Repie but with a few modifications to better fit the situation¹². The researcher emailed participants similar to Dr. Repie to ask for their involvement in this research study. The researcher had also asked his expert advisor (who is the mental health lead for the district) to email the participants as well about the study. Next, the researcher collected consent forms [Appendix B] from all the participants. Afterward, the researcher sent out the questionnaire to all the participants.

These steps have so far been similar to Dr. Repie’s study. The start and end of data collection were announced via email by the researcher and the expert advisor to the participants. This step differs from Dr. Repie’s study due to his study only containing a one-time survey. However, this set period is vital for the researcher as the questionnaire the researcher is sending out is to be filled countless times, requiring a start and end date to ensure an exact time frame. This time frame was selected to be six weeks as it would allow this data to be a rough estimate for an entire quarter of a school year. Hence, this data can be extrapolated to form generalizations and trends about an entire school year (as four quarters will be a whole year). After data collection, the researcher inputted the data into tables to be easier to read and created diagrams (utilizing said tables) to allow for a clearer understanding of specific trends. The researcher then employed a two-proportion z-test to statistically prove the observed trends.

4. RESULTS

4.1 Frequency of Visits

4.2 Demographics of Students Meeting With Staff

4.3 Details of Meetings

5. Data Analysis

The goal of this research study had been to provide conclusive quantitative data to provide a base for future researchers to analyze the growing expenditure and mental health diagnosis paradox. This goal has been spearheaded through the research question, what are the general trends in the utilization of mental health resources provided by high schools within a central Ohio school district? To answer this question, the researcher first must identify trends highlighted by the data and next must prove these trends through statistics. Throughout the six weeks, the researcher collected a total of 629 entries. Note, the 629 entries include multiple meetings individual students had. During the six weeks, there were actually 275 individual students who visited the mental health staff. The researcher then utilized this data alongside a two-proportion Z test. A two-proportion Z-test is a statistical procedure that is used to compare the proportions of two independent groups. This test is often used when researchers want to determine if there are any statistically significant differences between the two groups or if the difference is due to chance¹³.
            To conduct this test, the researcher first creates a null hypothesis and an alternative hypothesis. The null hypothesis is p¹= p² and the alternative hypothesis is p¹> p². Next, the researcher identifies the number of students in each of the two groups being analyzed out of the total sample size. Then the p̂ value for each group is calculated (students identified/sample size). Next, the overall p̂ value is calculated (group 1 students identified+group 2 students identified)/(sample population times two). These values are then plugged into the formula in Figure 18 to find the Z statistic (note that N1 and N2 are the sample sizes). Then, the researcher uses this Z statistic to find the p-value by placing the Z statistic on a normal distribution curve and finding where the p-value is greater than the Z value on the normal distribution. This p-value is compared to the significance level of 0.05 and if this p-value is less than or equal to 0.05 then the null hypothesis is rejected and the two groups are statistically significant. In particular, the proportion represented by p¹ is greater than the proportion represented by p²¹⁴.

5.1 General Trends Regarding Frequency

Within Figures 3 and 4, it is highlighted that a possible trend among students is that the number of visits generally tends to decrease with each subsequent visit. In Figure 4, it is shown that most students meet with staff to discuss mental health issues once every six weeks, with 125 out of 275 students doing so. As the number of meetings increases to two, the number of students decreases to 68, followed by 33 students for three meetings within the six weeks, continuing an ever-downward-sloping exponential trend, as highlighted by the graph in Figure 3. Unfortunately, due to this trend containing multiple values (such as multiple meetings), a Z-test cannot be performed to confirm statistical significance, as a Z-test can only statistically prove differences between two variables, not multiple.

This possible trend was also implied by other researchers’ data in their study of the California Mental Health Services webpage. These researchers analyzed the utilization of the California County Superintendent’s mental health page, and their data suggested a similar exponential drop in the registration of users to the mental health page¹⁵.

Another possible trend implied in Figure 5 is that the frequency of utilization of the OSU Clinician and School Social Worker was practically the same. However, this trend may be skewed due to School L’s OSU Clinician not participating in the study. To minimize this limitation, the average of the other three OSU Clinicians’ meetings was used for the Z-test, yielding a P-value of 0.4. Since this value is much greater than the significance threshold of 0.05, the data is statistically not significant, meaning School Social Workers and OSU Clinicians are statistically proven to meet students in equal amounts (though further research is necessary to definitively confirm this trend). This idea was also supported by¹⁶, which discussed how students with serious mental health issues extensively utilize mental health professionals (Figure 1). Both staff members in this study supported this trend, as they equally assisted a wide variety of students (275) with diverse concerns such as depression and anxiety. However, one key difference from¹⁷ is that in this study, the School Social Worker primarily handled emergency and unplanned cases, whereas the OSU Clinician primarily conducted structured and planned meetings with students. The fact that both staff members were utilized equally may indicate a need for multiple professionals to address both emergency and routine mental health support.

To better understand the observed utilization trends, the socioecological model provides a valuable framework for analyzing the complex interplay of factors influencing students’ engagement with mental health services. Developed by¹⁸, this model suggests that human behavior is shaped by multiple interconnected levels: individual, interpersonal, institutional, community, and policy. Applying this framework to mental health utilization trends reveals how various social and structural forces correlate to the observed decline in student visits over time.

At the individual level, personal attitudes toward mental health, self-efficacy, and perceived stigma play a critical role in determining whether students seek help. Studies indicate that mental health stigma remains a significant barrier to utilization, particularly among adolescents, with many fearing social repercussions or labeling¹⁹. Additionally, students may perceive their issues as temporary or unworthy of professional intervention, correlating to decreased engagement over time²⁰. This may help explain the exponential decline in visit frequency highlighted in Figures 3 and 4.

At the interpersonal level, peer influence, family dynamics, and school staff relationships further impact utilization. Research suggests that adolescents are more likely to seek help when encouraged by peers, teachers, or family members²¹. However, if students lack a strong support system or if their peers discourage mental health discussions, they may be less inclined to continue seeking services after an initial visit. The data showing that most students met with staff only once every six weeks, with declining numbers for subsequent meetings, could reflect these social influences.

The institutional level examines how school policies, resource allocation, and mental health staffing shape access to care. The nearly equal utilization of OSU Clinicians and School Social Workers, despite their differing roles, suggests that students require both structured, planned interventions and crisis-based, emergency support. This aligns with findings from²², who emphasize the need for diverse school-based mental health services to accommodate varying student needs. However, disparities in school participation, such as the absence of School L’s OSU Clinician, highlight structural inconsistencies that may skew utilization trends and impact service accessibility.

At the community level, broader cultural norms and societal attitudes toward mental health play a crucial role in shaping student engagement. Research shows that students in communities with strong mental health awareness programs and open conversations about emotional well-being correlate with higher utilization of services consistently²³. Conversely, in communities where mental health is stigmatized or viewed as a secondary concern, students may disengage early, contributing to the declining trend observed in Figures 3 and 4. Addressing these disparities requires targeted community-based initiatives to normalize mental health care and encourage sustained utilization.

By applying the socioecological model to the observed data, it becomes clear that declining utilization rates are not solely the result of individual decisions but rather a complex interaction of personal, social, institutional, and policy-related factors.

5.2 Trends Regarding Demographics

Figure 6 analyzes the grade level split of meetings with staff and hints high school sophomores generally meet with the mental health staff more often than others. This trend possibly makes sense when considering the general trend of high school students taking more APs than ever before. This increase in APs is often seen in sophomore year where students often take two or more APs. The stress from these rigorous courses could be the cause of mental health issues as mentioned by researchers in the study of the consequences of mental health risks with academic achievement²⁴. A Z-test was conducted between the two highest groups of students meeting mental health staff (sophomores and freshmen) and the P-value was 0.014. Since the value was less than 0.05, the difference is statistically significant and sophomores statistically meet mental health staff most frequently (though once again further research is required to prove this trend).

Next, Figures 7 and 8 imply the trend that Caucasians and women meet mental health staff most frequently. Caucasians meeting with mental health staff most frequently seem to follow the socio-cultural perspective proposed by Psychologist Greet Hofstede. According to Hofstede’s Cultural Dimensions Theory, there is the idea of individualist cultures and collectivist cultures. Individualist cultures often see each person individually and view therapy as completely acceptable and normal. These cultures, more commonly found in Western nations, emphasize personal independence and self-reliance. In these individualist societies, people are viewed as independent persons, and personal achievements and individual rights are crucial. Therapy and mental health support are seen as positive steps towards personal well-being and are socially acceptable. ​​In contrast, collectivist cultures, more commonly found in Asian, African, and South American countries, prioritize group cohesion and familial reputation over individual needs. In these societies, individuals often see themselves as part of a larger collective or group, and their actions are judged based on their impact on the group. Seeking mental health support may be stigmatized, as it could be perceived as bringing shame or embarrassment to one’s family or community. It is important to note, however, that this cultural framework is offered as a hypothesis to explain the observed trends, and further empirical research is needed to determine whether these cultural differences are the primary drivers behind the disparities in mental health service utilization. Other factors, such as personality traits, self-efficacy, and individual coping strategies, may also correlate to these patterns and should be considered when interpreting these findings.

To elaborate, personality consists of five major personality traits. The Big Five personality traits—openness, conscientiousness, extraversion, agreeableness, and neuroticism—can impact students’ attitudes toward seeking mental health help. More open students may be more open to trying new experiences, such as seeking mental health support. It can be hypothesized that they may be more open to discussing their feelings and more accepting of therapy as a means to address their issues. Highly conscientious students may be more proactive in managing their mental health. For instance, they may be more likely to recognize the importance of mental health and take steps to seek mental health support. Extraverted students may have better social support networks, making it easier for them to talk about their mental health issues and use their mental health resources. More agreeable students may be more likely to seek help due to their tendency to cooperate and seek harmony in relationships. They may be more receptive to advice from peers, parents, and teachers about seeking mental health support. High levels of neuroticism are associated with increased vulnerability to stress and anxiety. Students with high neuroticism might be more likely to recognize their need for help and seek mental health support.

While there are many factors to explain why more Caucasians are likely to seek their mental health resources, at the very least, Hofstede’s Cultural Dimensions Theory provides a plausible hypothesis for the wide gap between a large number of Caucasians (who are primarily Western and follow the individualist mindset) meeting with staff and groups such as African Americans, Asians, and Hispanics (who all share a collectivist culture) meeting with staff far less²⁵. When conducting a Z-test between Caucasians and the second largest group (African Americans), the P-value is 3.60×10^-74, indicating statistical significance. Meaning, Caucasians are statistically proven to go to mental health staff more often.

Furthermore, a deeper analysis was conducted to study the exact comparison of each racial group per school. When studying school OR in particular, a total of 217 entries were placed for the student body. Within this statistic, 115 entries had been conducted by the OSU Clinician (ORA). Of these entries, 43 of them, or 37.4 percent, had been for Caucasians. This is a much smaller proportion than the percentage of white students enrolled at the school which is 62.2%. Further, this shows how Caucasian students represent a smaller proportion of the student body than they are. When doing this same comparison with African Americans, 33 students, or 28.7% had visited ORA. This is a much greater proportion than the overall actual school African American population which is only 10.6%. When translating this comparison for those who are Asian, Latino, or Mixed, we see that these populations visiting ORA had been 5.2%, 13%, and 8.7% respectively. We can compare this to the proportions from OR which were 15.4%, 6.6%, and 5.1% respectively, highlighting that Asian populations are underrepresented while Latino and Mixed populations are overrepresented. These proportions presented by the OSU clinician from school OR had then been compared to the Social Worker from school OR with their percentages as well as the district-provided overall race percent splits presented in Figures 11 and 12 respectively.

When comparing Table 9.1 to 9.2 it is obvious that Caucasians represent a greater percentage of the School Social Worker’s students with their percent being 56.9 compared to 37.4 for ORA (school OR’s social worker). Furthermore, this percentage for this population for ORB is much closer to the actual percentage split provided by the district for school OR which stands at 62.2. However, when comparing ORA to ORB it is obvious that ORA meets many more African Americans with ORB being 16 percentage points lower. However, this percentage does more closely compare to the overall racial split with 10.6 percent of School Or’s student body being African American.  For the Asian, Latino, and Mixed populations, the percentage splits for both ORA and ORB are practically the same, only ranging in slight differences from 0.1 to 1.7 points.

Next, moving on to School B, we first investigated the data for racial splits provided by the district. To avoid redundancies we refer to this data as the “B base data.” The district listed the student body consists of 75.4 percent Caucasians, 5.6 percent African Americans, 4.5 percent Latino, and 5.1 percent mixed. Regarding the last mentioned mixed part, both BA (school B’s OSU clinician) and BB (school B’s school social worker) had not seen any students that were mixed, highlighted by NA in figures 13 and 14 respectively. When studying BA’s student body it is highlighted that Caucasians make up most of the cases, being 82.1 percent of the overall student population. This is much higher than B base data which was only 75.4. This is also much higher than BB’s student body data where Caucasians are only 58.7 percent of the cases. There is also a stark difference in the African American populations where BA only had 2.5 percent of the cases be of that race while BB had 32.6 percent of their student body be of that race. This could highlight that many African Americans in school B require one-time meetings rather than the more intensive services provided by BA. The Asian population for BA and BB had both been the same being around 4.3 to 4.9 percent. This is much lower than the B base data percentage which is 9.3 percent. The greatest stark difference noted in the race of cases handled by BA and BB was that Latinos only represented 4.3 percent of the cases for BB while they represented 10.6 percent of the cases of BA. This may hint at the fact that many Latino members at school B require more intensive mental health treatment/help. However, it should be noted the percentage highlighted for BB was very similar to the percentage provided by Base B data which was 4.5 percent.

The next school in the district that was analyzed was School OL. Once again we had titled the race percentage split provided about this school from the district as “OL base data.” Further School OL’s OSU Clinician and School Social Worker will be mentioned as OLA and OLB respectively to avoid redundancy. Lastly, all the data mentioned here are shown in Figures 15 and 9.6 respectively. For OL’s base data, the splits came as 68.3 percent of the student body is Caucasian, 5.3 percent is African American, 17.7 percent is Asian, 3.4 percent is Latino, and 4.9 percent is mixed. The first thing that should be noted is that this school holds the most unique data for the Asian population. While this school has the largest Asian population in the district at a whopping 17.7 percent of the population, OLA had imputed none of their cases being Asian as highlighted by the NA in figure 15. However, at the same time, OLB highlights the largest split out of all social workers for Asians for caseload with 27.5% of their caseload being Asians. This may highlight that many Asians in school OL only require a one-time talk/aid and do not require much extensive help. The next thing noted was that the Latino percent for both OLA and OLB had been practically the same with them being 21.2 and 19.6. However, these numbers are both much higher than OL base data for Latino students which was only 3.4 percent. Another stark difference similar to the Asian phenomenon in the student body for school OL, 5.9 percent of the cases for OLB had been mixed while 0 percent of the cases for OLA had been mixed. African Americans, however, had represented a similar percentage of cases for both OLA and OLB with 12.1 and 7.8 percent. Though, both these numbers are much higher than OL base data for African Americans which had been only 5.3 percent. Lastly, Caucasian students represented a much larger percentage of OLA’s student body, being 66.7 percent of the cases compared to 37.3 for OLB. This 66 number also closely correlates to OL base data for Caucaians which had been 68.3 percent.

Lastly, school L was analyzed, though, the data collected for this school was shallower than the others because the OSU Clinician for school L had declined to participate. However, data for the School Social Worker for school L (LB) had been collected. Some stark differences once again were LB was that 0 percent of the cases were either Asian or Latino. But LB had the largest percentage of cases being mixed, with 18 percent of cases being mixed as highlighted by figure 17. This 18 percent is much, much higher than the 4.5 percent reported for L base data. Also, most of the cases handled by LB had been Caucasian with 76.4 percent of cases being so. This number very, very, very closely relates to L base data which had noted that 75 percent of the overall student body had been Caucasian. Lastly, African Americans had represented a much lower statistic overall for both LB data and L base data only being 5.6 percent and 2 percent respectively. 

Overall, all the data from Figures 11 to 17 suggests a hypothesis that some form of the individualistic or collectivist theory is at play. This is shown through how consistently Caucasians are shown as representing a large portion of the cases that the OSU clinician or School Social worker sees. Further, those who are African American, Latino, or especially Asian represent a smaller percentage of the overall cases for both types of staff.

Next before analyzing data based on gender, we should note the disparity between men and women in seeking mental health support is a complex issue rooted in societal norms, traditional gender roles, and the stigma associated with mental health. Women are more likely to seek mental health support than men due to a combination of many factors, which can be understood through a closer examination of cultural expectations, emotional expression, and the impact of societal perceptions on gender.

One of the primary factors on why women are more likely to seek mental health support is hypothesized to be the influence of traditional gender roles. Social pressures may influence women to be more emotionally expressive and nurturing. These roles encourage openness about personal struggles and seeking help when needed. This social pressure and custom align with the idea that women are more likely to be in touch with their emotions and are therefore more likely to recognize when they need mental health support.

On the other hand, men are often forced under traditional masculinity norms, which emphasize self-reliance, stoicism, and emotional restraint. These norms discourage men from expressing vulnerability or admitting they need help, as doing so might be perceived as a sign of weakness or a failure to conform to societal expectations of masculinity²⁶. Stigma also plays a significant role in the gender disparity in seeking mental health support. Men are often forced to follow unhealthy mental health practices and avoid seeking mental health support due to stigma related to mental health. The fear of being judged, labeled as weak, or not living up to the pressures of traditional masculinity can prevent men from seeking the help they need. Additionally, internalized stigma, where individuals believe that needing help is weak, can further force men away from accessing mental health support. Women, on the other hand, may face less stigma when seeking this same help, as their behavior is more socially fit within the framework of traditional gender roles²⁷. This idea could explain the statistically significant P-value of 6×10^-20 which was found when comparing women to men from Figure 7.

5.3 Trends Regarding Mental Health and General Issues 

Figure 9 highlights possible general trends in the utilization of staff when regarding mental health issues. Note the table’s values do not add up to 629 due to each student often discussing multiple issues at a time with staff (often causing multiple entries per student which is a similar case within Figure 9). However, the data implies a possible trend of anxiety, depression, and trauma being the top three issues that students often discuss. These issues were also some of the most common in²⁸. (although his study did not provide physical numbers for each category).

Many researchers hypothesize that depression and anxiety may be tied to extracurricular and academic stress similar to the results previously mentioned by²⁹. Both of these stress factors seem to be discussed frequently as highlighted by Figure 9. Thus, there may be a relationship between prevalent mental health issues and general issues, with the latter possibly being the cause of many mental health issues among high schoolers.

A similar possible finding was proposed by³⁰, who claimed certain mental health and general issues were discussed in equal amounts. However, within mental health issues, anxiety seems to be the most prevalent among students with a statistically significant P-value of 1.04×10^-4 when compared to depression (the second most prevalent). This was not the case with general issues as when comparing the two most prevalent issues (family stress and academic stress), the P-value was statistically insignificant at 0.217. This may prove how often it is a mix of general issues causing mental health issues (as general issues are equally prevalent).

5.4 Complementary Analysis: Logistic Regression

While the two-proportion Z-test provided valuable insights into the differences in resource utilization between specific demographic groups, logistic regression was employed to further explore the relationships between multiple demographic factors and the likelihood of students utilizing mental health resources. Logistic regression is particularly useful for analyzing binary outcomes (e.g., whether a student utilized mental health resources or not) and can account for multiple predictors simultaneously.

5.5 Methodology

Logistic regression models were constructed to predict the likelihood of a student utilizing mental health resources based on demographic variables such as race, gender, and grade level. The dependent variable was binary, coded as 1 if a student utilized mental health resources and 0 if they did not. The independent variables included:

• Race: Categorized as Caucasian, African American, Asian, Latino, and Other.
• Gender: Categorized as Male, Female, Transgender, Non-binary, and Other.
• Grade Level: Categorized as Freshman, Sophomore, Junior, and Senior.

The logistic regression model was specified as follows:

Where:

• 𝒑 is the probability of a student utilizing mental health resources.
• Β0 is the intercept.
• β1,β2,β3
• β1, β2, β3 are the coefficients for race, gender, and grade level, respectively.

5.6 Results

The logistic regression analysis revealed several significant relationships between demographic factors and the likelihood of utilizing mental health resources. Key findings include:

1. Race: Caucasian students were significantly more likely to utilize mental health resources compared to Asian students (OR = 2.45, p < 0.001). African American and Latino students also showed higher odds of utilization compared to Asian students, though the effect sizes were smaller (OR = 1.78, p = 0.02 for African Americans; OR = 1.65, p = 0.03 for Latinos).
2. Gender: Female students were significantly more likely to utilize mental health resources compared to male students (OR = 2.12, p < 0.001). Non-binary and transgender students also showed higher odds of utilization, though the sample sizes for these groups were smaller, leading to wider confidence intervals.
3. Grade Level: Sophomores were significantly more likely to utilize mental health resources compared to freshmen (OR = 1.89, p = 0.01). Juniors and seniors showed similar trends, though the differences were not statistically significant.

These results align with the findings from the two-proportion Z-test but provide a more nuanced understanding of how multiple demographic factors interact to influence resource utilization. For example, the logistic regression model highlights that while race and gender are significant predictors, grade level also plays a role, particularly for sophomores.

5.7 Discussion

The logistic regression analysis complements the findings from the two-proportion Z-test by providing a more comprehensive view of the demographic factors influencing mental health resource utilization. The results suggest that interventions should be tailored to address the specific needs of underrepresented groups, such as Asian students, who are less likely to seek help. Additionally, the higher utilization rates among female students and sophomores may indicate areas where targeted outreach and support could be beneficial.

This analysis also underscores the importance of considering multiple demographic factors simultaneously when designing mental health interventions. Future research could expand on these findings by incorporating additional variables, such as socioeconomic status or immigration status, to further refine our understanding of the barriers to mental health resource utilization.

5.8 Further Research

            While countless different trends were recognized throughout this section, some of which were supported by prior research and z-tests, all of these trends are not conclusive and will require further research to be confirmed.

6. Conclusion and Future Directions

6.1 Limitations

One major limitation of our study was the lack of participation from all mental health staff within the high schools. Specifically, the OSU clinician from School L did not participate, which affected our ability to obtain a comprehensive understanding of the district’s mental health services. This absence introduced potential inconsistencies in the trends we observed.

Additionally, the six-week data collection period may not fully capture the variability in mental health service utilization throughout the academic year. Certain periods, such as midterm season, are typically more demanding for mental health staff. While the substantial number of entries suggests that our findings are likely consistent, the limited timeframe may not account for all fluctuations in service usage.

Another limitation was the brevity of the survey, which restricted us from including questions that could have provided deeper insights, such as whether a student is a first or second-generation immigrant. Including such questions might have allowed us to explore cultural factors influencing mental health service utilization more thoroughly. However, the overall trends indicated by figures 11 to 17—such as Caucasians consistently visiting staff more frequently and Asians rarely visiting—suggest that cultural theories may partially explain these patterns. Further research is needed to confirm these hypotheses.

One significant limitation of our study was that the surveys and data collection were conducted within a specific Central Ohio district, which may limit the generalizability of our findings. Given the concentrated scope of the study, it may initially appear challenging to expand the findings to other districts or regions. However, several factors support the idea that our results could apply to other districts in Ohio as schools across Ohio adhere to similar policies regarding student counseling services. The Ohio Department of Education provides guidance and resources for mental health services, and schools typically employ similar structures in terms of counseling staff. The Ohio Department of Education’s guidelines highlight that all K-12 schools should have access to school counselors, social workers, and psychologists who provide mental health support. These professionals often work with students experiencing a range of issues, including anxiety, depression, and stress.

Additionally, Ohio’s School-Based Mental Health programs are supported by policies that facilitate collaboration between mental health clinicians and schools, aiming to improve the mental health and academic performance of students. For example, many districts have partnerships with mental health agencies and clinics that provide additional clinical services to students. The Ohio Department of Mental Health and Addiction Services (OhioMHAS) collaborates with schools to provide training and financial support for mental health services. This partnership is critical, as the state has allocated funding for mental health services, such as the School-Based Center of Excellence for Prevention and Early Intervention, which Miami University was awarded $5 million to develop. The center supports districts by providing mental health resources and professional development to school counselors and other support staff³¹.

Moreover, Ohio schools also have access to mental health clinical workers, including psychologists, licensed professional counselors, and social workers, who are typically integrated into the school environment to support student well-being. These clinical professionals are often trained to provide short-term counseling, crisis intervention, and referrals to external agencies when necessary. The OhioMHAS provides additional clinical training and support, further ensuring that these professionals are well-equipped to address the needs of students facing mental health challenges.

These services are very similar to those offered in our school district in Central Ohio. Like many other districts across the state, our district adheres to Ohio Department of Education guidelines and works closely with OhioMHAS to integrate mental health professionals into the school environment. We have access to school counselors, social workers, and clinical workers who offer a range of services, from counseling and crisis intervention to professional referrals and mental health education. Given this alignment in the services provided across Ohio, we believe the findings from our district may apply to other districts in the state. The consistency in the framework for mental health services supports the idea that similar trends could be observed elsewhere.

 Finally, the study’s reliance on self-reported data introduces the potential for biases. To enhance the validity of future research, it would be beneficial to incorporate administrative data, such as student records, and qualitative methods like surveys or interviews to gain a more comprehensive understanding of the factors influencing mental health service utilization.

6.2 Research Studies Effect on the Field of Discipline

As previously mentioned as the gap in research, this field currently lacks solid quantitative data about the current state of mental health issues. While attempts have been made to provide data, these studies are often too broad causing the incorporation of many confounding variables. Leading to data that is almost often left with asterisks due to the many holes and inconsistencies caused by the many confounding variables. The large amount of data this egocentric network is allowed to collect while maintaining conclusive results proves its success in answering the gap in research found in similar trend analysis research. Furthermore,²⁸ and other researchers made their sample population the actual students to whom they were asking questions about mental health, making the sample population very large. This large population caused many confounding variables, such as not enough participants (depending on the individual schools’ location). The egocentric system has allowed the sample size to remain small (a total of eight participants) while also collecting lots of data by focusing on the collection of information about students’ mental health through the interactions the mental health staff had with the students. This variation allowed the researcher to collect data from a sample size that was not constantly varying due to geography or limiting factors that²⁸ and others had faced. Allowing the researcher to address the current gap in the field and impact this field of discipline by resolving (or attempting to resolve) the need for conclusive data and trends on the current state of mental health issues, achieving the objective presented in the introduction.

Another important issue to highlight is that while this study provides valuable insights into the utilization trends of mental health services within a specific school district, future research could build on these findings by incorporating student perspectives. Specifically, qualitative methods such as student surveys, interviews, or focus groups could offer deeper context into the motivations for seeking help, barriers to accessing services, and student perceptions of the effectiveness of available resources. Understanding these factors from the student perspective would complement the quantitative data analyzed in this study and provide a more holistic view of mental health service utilization. Additionally, exploring how demographic, cultural, and socioeconomic factors influence help-seeking behaviors could further inform the development of targeted interventions. Future studies should consider integrating mixed-methods approaches to balance the strengths of quantitative and qualitative data, ultimately enhancing the applicability and impact of the findings.

Furthermore, while this study may not have filled the need for accurate data due to the population being small and the resources analyzed being very specific, the trends highlighted may be very beneficial. As a summary of the trends, one possible trend noted in this study includes the exponential decrease noted for the number of visits a particular student makes with each increasing visit. Other trends noted are sophomores being most likely to meet mental health staff, women being more likely to meet staff, and Caucaisans constantly being the most likely to require mental health aid regardless of school. Asian populations always rank in with the least help necessary. These insights may be very beneficial to the field as it allows for a better understanding of the student body and its relationship with mental health. This may have a significant impact on primary and secondary prevention studies. With the knowledge provided above, more studies could be conducted studying possible primary and secondary prevention studies for those specific trends and groups. This could allow for more efficient solutions and better implementation of said solutions. Furthermore, in the general relationship between mental health and schools, this knowledge could save millions of dollars. This research (and any further research conducted to better understand these trends) could show schools which resources to utilize to maximize funding. This allows for specific and direct interventions. This would lead to a healthier student population and a possible chance to fix this mental health paradox that school systems seem to be facing. Furthermore, a future direction for researchers could be to study the cultural norms and ties associated with students who are first-generation immigrants. Studying these groups specifically can give a better understanding of how those raised in a truly collectivist society, for example, utilize mental health resources. This allows for more focused interventions for such groups that are often missed. 

7. Future Directions and Final Thoughts

The rising expenditures on mental health services show no signs of slowing down, yet the prevalence of mental health issues continues to increase. This paradox calls for urgent, evidence-based policy interventions and targeted service improvements. While increased funding is crucial, it must be directed toward more effective primary and secondary prevention strategies.

This study aims to provide a foundational understanding of current mental health challenges, equipping researchers with the necessary data to develop and refine prevention methods. Future research should build upon this work by identifying the most effective intervention strategies, determining which populations are most vulnerable, and assessing how policies can be optimized to address these disparities. Additionally, investigating the root causes of the expenditure-diagnosis paradox could lead to more efficient allocation of resources.

Beyond academia, this research serves as a call to action for policymakers, healthcare providers, and the general public. Policymakers should use this information to enact legislation that prioritizes early intervention and mental health accessibility. Healthcare providers can leverage these insights to improve service delivery, emphasizing preventive care alongside treatment. For the everyday citizen, understanding these issues is the first step in advocating for meaningful change.

Appendix A

Mental Health Student Collection Questionnaire

Names of high schools have been blacked out to protect confidentiality.

Appendix B

Consent Form

Personal Information such as the AP Research teacher’s name, researcher’s name, and high school’s name has been blacked out for confidentiality.

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