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Exploring mental health care utilization and academic persistence among college students: evaluating racial and stigma-related disparities

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Exploring mental health care utilization and academic persistence among college students: evaluating racial and stigma-related disparities

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Content EXPLORING MENTAL HEALTH CARE UTILIZATION AND ACADEMIC PERSISTENCE
AMONG COLLEGE STUDENTS:
EVALUATING RACIAL AND STIGMA-RELATED DISPARITIES
by
Derek Merced Walker
A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)
August 2024
Copyright 2024 Derek Merced Walker

ii
TABLE OF CONTENTS
LIST OF TABLES......................................................................................................................... iv
ABSTRACT.................................................................................................................................... v
CHAPTER 1. INTRODUCTION ................................................................................................... 1
CHAPTER 2. METHODOLOGY .................................................................................................. 4
Data Source and Preprocessing................................................................................................... 4
Participant Selection and Criteria ............................................................................................... 5
Covariates ................................................................................................................................... 6
Main Variables............................................................................................................................ 7
Descriptive & Bivariate Analysis ............................................................................................... 8
Regression Modeling .................................................................................................................. 8
Model Evaluation........................................................................................................................ 9
CHAPTER 3. RESULTS.............................................................................................................. 10
Sample Characteristics.............................................................................................................. 11
Bivariate Analyses .................................................................................................................... 14
Regression Analyses................................................................................................................. 19
CHAPTER 4. DISCUSSION........................................................................................................ 21
Main Findings........................................................................................................................... 21
Limitations................................................................................................................................ 22
Implications/Further Research .................................................................................................. 24
CHAPTER 5. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ......................... 27

iii
REFERENCES ............................................................................................................................. 28
APPENDICES .............................................................................................................................. 30
APPENDIX A. Figure A.1: Cook's Distance for Influential Observations in the
Logistic Regression Model ....................................................................................................... 30
APPENDIX B. Figure B.1: Leverage Values for Observations in the Adjusted
Logistic Regression Model ....................................................................................................... 31
APPENDIX C. Figure C.1: Standardized Residuals for Observations in the Adjusted
Logistic Regression Model ....................................................................................................... 32
APPENDIX D. Figure D.1: Multicollinearity Analysis: VIF and GVIF Values...................... 33
APPENDIX E. Figure E.1: Brant Test Results Testing for Proportional Odds in Initial
Cumulative Logit Model........................................................................................................... 34
APPENDIX F. Figure F.1: Adjusted Analysis of Therapy Attendance on Academic
Persistence (Doubtful = 1, Hopeful = 0), Controlling for Demographic Factors and
Including Interaction Effect: Race-Therapy ............................................................................. 35
APPENDIX G. Figure G.1: Adjusted Analysis of Therapy Attendance on Academic
Persistence (Doubtful = 1, Hopeful = 0), Controlling for Demographic Factors and
Including Interaction Effect: Race-Stigma ............................................................................... 36
APPENDIX H. Figure H.1: Adjusted Analysis of Therapy Attendance on Academic
Persistence (Doubtful = 1, Hopeful = 0), Controlling for Demographic Factors and
Including Interaction Effect: Stigma-Therapy .......................................................................... 37

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LIST OF TABLES
TABLE 1. DEMOGRAPHIC CHARACTERISTICS OF THE FULL DATASET ............................................ 10
TABLE 2. DEMOGRAPHIC CHARACTERISTICS OF FILTERED DATA (PARTICIPANTS
REPORTING SEVERE OR FREQUENT DEPRESSIVE SYMPTOMS) .................................................. 12
TABLE 3. THERAPY UTILIZATION BY RACE.................................................................................... 14
TABLE 4. STIGMA PERCEPTION BY RACE ....................................................................................... 15
TABLE 5. THERAPY UTILIZATION BY STIGMA PERCEPTION............................................................ 16
TABLE 6. UNADJUSTED ANALYSIS OF THERAPY ATTENDANCE ON ACADEMIC PERSISTENCE
(DOUBTFUL = 1, HOPEFUL = 0) IN UNIVERSITY STUDENTS .................................................... 17
TABLE 7. ADJUSTED ANALYSIS OF THERAPY ATTENDANCE ON ACADEMIC PERSISTENCE
(DOUBTFUL = 1, HOPEFUL = 0), CONTROLLING FOR DEMOGRAPHIC FACTORS ...................... 18

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ABSTRACT
Amidst the challenges of higher education, the persistence of students remains pivotal for
achieving academic success and graduation. This research investigates the influence of mental
health therapy on academic persistence among college students, with a particular focus on the
interactions between therapy, race, ethnicity, and therapy stigma. Using data from the Healthy
Minds Study, the study examines how therapy attendance and perceived stigma impact students'
perceptions of their ability to persist academically. While initial findings suggested that students
in therapy were more doubtful about persisting, this was expected given the higher levels of
distress among those seeking therapy. Subsequent analyses explored interactions between therapy,
stigma, and demographic factors, revealing no significant interactions between therapy and race,
or stigma. These results challenge prior literature suggesting differential effects of therapy and
stigma across racial groups. Sensitivity analyses confirmed the robustness of these findings. This
study highlights the complexity of therapy utilization and stigma in shaping academic outcomes,
calling for further research and culturally sensitive mental health interventions to support student
well-being and persistence.

1
CHAPTER 1. INTRODUCTION
In the landscape of higher education, academic persistence emerges as a crucial factor for student
success and graduation. Defined as the ability of a student to persevere and complete their
educational objectives, academic persistence is influenced by numerous factors, prominently
including mental health. The collegiate journey often involves significant transitions, academic
pressures, and personal challenges, all of which intricately interlace with students' mental wellbeing and, by extension, their academic persistence. Mental health therapy, encompassing a
range of interventions from counseling to psychotherapy, stands as a bulwark against
psychological distress and a means to foster mental well-being. Extensive research has
highlighted the benefits of therapy, showing significant improvements in mood, anxiety
reduction, and overall psychological health (Cuijpers et al., 2014; Lambert, 2013). However,
recent studies hint at the nuanced and intricate nature of therapy's efficacy, suggesting that its
impact may vary across diverse racial groups and reflecting broader socio-cultural dynamics
(Gary, 2005).
At the heart of this study lies the quest to understand the complex dynamics of therapy's
association with academic persistence, particularly in the context of racial differences and
therapy stigma. Previous research has highlighted unique stressors faced by minority students,
including discrimination and cultural alienation, which can exacerbate mental health issues and
affect academic outcomes (Smith et al., 2007; Yeh, 2002). The specific question related to
perceived public stigma (Most people think less of a person who has received mental health
treatment.) was chosen for its extensive data coverage and consistent significance across racial
groups and therapy utilization interactions. Public stigma reflects societal attitudes towards

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mental health recipients, influencing public policy, social interactions, and institutional practices.
Understanding public stigma is crucial for developing interventions and policies to reduce
barriers to treatment and to promote mental health equity at a systemic level, complementing
efforts to address personal stigma.
Building on the objective of understanding disparities in therapy engagement and stigma
surrounding mental health counseling among different racial groups, it is important to recognize
that despite reporting similar or higher levels of anxiety and stress, many ethno-racial groups,
such as Blacks, Asians, and Hispanics, report lower rates of impairment due to these mental
health diseases compared to Whites. This phenomenon, known as an epidemiological paradox,
raises the question of whether differences in how clinical impairments are assessed across races
play a role (Oh, Pickering, & Martz, 2022). In this context, extensive scholarship in psychology
and education has also highlighted the pivotal role of mental health in shaping academic
trajectories. Therapy has been praised for its efficacy in reducing symptoms of anxiety and
depression, thereby empowering students to navigate academic challenges more effectively
(Givens & Tjia, 2002; Eisenberg et al., 2009); however, the intersection of race and mental
health reveals a complex landscape. For example, Asian students may face cultural stigma and
familial pressures that influence their engagement with therapy (Kim & Omizo, 2003). Similarly,
Hispanic students might contend with cultural stigmas but may also benefit from distinct cultural
narratives and support systems that shape their therapeutic experiences (Cervantes & Cordova,
2011).

3
This study aims to explore how mental health therapy impacts academic persistence among
college students, examining racial and ethnic differences and how perceived public stigma varies
across these groups regarding mental health counseling, using data from the Healthy Minds
Study. Specifically, the research seeks to achieve three primary objectives: 1) to investigate the
overall association between mental health therapy and academic persistence; 2) to explore the
interactions between race/ethnicity and therapy stigma in influencing academic persistence, with
a focus on Hispanic, Asian, and African American students; and 3) to assess factors such as
cultural stigma, access to culturally competent care, and coping mechanisms that may affect the
differential impacts of therapy. We hypothesize that participation in mental health therapy will
positively correlate with academic persistence and that race/ethnicity and stigma will moderate
this relationship. By understanding these dynamics, the study aims to provide valuable insights
for mental health practitioners, educators, and policymakers, guiding the development of
culturally sensitive and effective mental health interventions and fostering an academic
environment that supports resilience, well-being, and success for all students, regardless of their
racial or ethnic background.

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CHAPTER 2. METHODOLOGY
Data Source and Preprocessing
We used data from the Healthy Minds Study (HMS) dataset, collected during the 2020-
2021 academic year, a period significantly influenced by the COVID-19 pandemic. The HMS is
an annual web-based survey administered by the Healthy Minds Network, encompassing
responses from diverse post-secondary institutions across the United States and some
international locations (Healthy Minds Network. Healthy Minds Study among Colleges and
Universities, 2020-2021). Healthy Minds Network, University of Michigan, University of
California Los Angeles, Boston University, and Wayne State University.
https://healthymindsnetwork.org/research/data-for-researchers.). It provides detailed insights into
mental health, service utilization, and related issues among undergraduate and graduate students.
Our study specifically focused on participants aged 18-27 from the HMS dataset, involving a
subset of the initial 137,980 observations to examine the experiences of the students reporting
the highest levels of depression within this age group. Institutional Review Board approvals were
obtained. For more detailed information on the HMS dataset, refer to the HMS National Report
for the 2020-2021 academic year (Healthy Minds Network, 2021). The HMS data are publicly
available upon request at: Healthy Minds Network.
Prior to analysis, quality control measures were implemented to ensure data integrity,
examining variables for missingness, outliers, and checking distributional assumptions. Because
certain demographic variables such as race, ethnicity, and gender were collected by asking
participants to “check all that apply,” we verified that participants’ responses corresponded
accurately to variables used for analysis.

5
Participant Selection and Criteria
Figure 1. Consort Diagram Illustrating Selection of Analytical Subset (Participants reporting
severe or frequent depressive symptoms). The flowchart starts with the full dataset and applies
sequential exclusion criteria: participants not in the highest depression category, those aged 28
or older, and those with invalid therapy values. Missing values for each of these criteria were
retained in the analysis. The final subset includes participants who met all inclusion criteria
after accounting for these exclusions and missing data.
The initial Healthy Minds dataset consisted of 137,980 observations. Inclusion criteria in
our analytic sample included: students exhibiting symptoms of significant depression, defined by
an average score of 3 or higher across all nine questions on the Patient Health Questionnaire-9
(PHQ-9; Kroenke and Spitzer, 2002), with responses ranging from 1 (Not at all) to 4 (Nearly

6
every day); reporting age between 18 and 27 during the years 2020-2021; and having a valid
response to the survey question asking about current and former participation in therapy. This
resulted in a final sample size of 23,449 individuals.
We selected the age range of 18 to 27 to encompass a 10-year span of the youngest
college students, as this group often faces significant mental health challenges that can impact
their academic performance. This age range is particularly relevant as it includes a substantial
portion of both undergraduate and graduate students.
Covariates
Several covariates were included in the analysis to control for potential confounding factors:
● Age, self-reported, in years.
● Constructed Gender: Categorized as Male, Female, and Other - includes Trans
male/Trans man, Trans female/Trans woman, Genderqueer/Gender non-conforming, selfidentified genders, and Gender non-binary).
● Socioeconomic Status: Current financial situation categorized from Always stressful (1)
to Never stressful (5), based on the question "How would you describe your financial
situation right now?
● Weekly exercise: Hours spent exercising in the past 30 days, categorized as follows:
Less than 1 hour, 2-3 hours, 3-4 hours, and 5 or more hours. This includes any exercise of
moderate or higher intensity, such as brisk walking or bicycling.
● Academic Impact: Days in the past 4 weeks where emotional or mental difficulties hurt
academic performance, categorized as follows: None, 1-2 days, 3-5 days, and 6 or more
days (4).
● Chronic Disease: Presence of any chronic health condition requiring ongoing treatment
(Yes vs. No).
● Diagnosed Mental Illness: Presence of any diagnosed mental health condition (Yes vs.
No).
● Knowledge of Campus Resources: Agreement with knowing where to access mental
health resources on campus from Strongly agree (1) to Strongly disagree (6).
● Health Insurance: Whether the student reported they have access to health insurance
(Yes vs. No).

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● Personal Stigma: Agreement with the statement that the respondent would think less of
a person who has received mental health treatment from Strongly agree (1) to Strongly
disagree (6).
We selected a range of covariates to ensure a comprehensive analysis of clinically
relevant factors that may influence academic persistence and potentially affect a student's therapy
attendance. These covariates include demographic information, mental health status,
socioeconomic indicators, physical health, attitudes towards school, access to health insurance,
knowledge of campus support resources, and both personal and perceived societal stigma,
providing a well-rounded view of the students' backgrounds and experiences. Descriptive
statistics for these covariates are provided to give an overview of the sample characteristics.
Main Variables
Therapy Attendance (Predictor): This variable indicates whether participants have received
counseling or therapy for mental health concerns, with the following categories:
o 1 = No, never
o 2 = Yes, prior to starting college
o 3 = Yes, since starting college
o 4 = Yes, both prior to and since starting college
Confidence in Finishing Degree (Outcome): Participants were asked about their confidence in
their ability to finish their degree despite challenges, with responses ranging from 1 (Strongly
agree) through 6 (Strongly disagree). A binary version of this variable was created, expressing
whether students were “hopeful” (agree, strongly agree) or “doubtful” (strongly disagree through
somewhat agree) about their ability to finish their degree.
● Depression Category: Depression severity was determined using the Patient Health
Questionnaire-9 (PHQ-9), consisting of nine questions asking participants how often they
have been bothered by various problems over the past two weeks. The response scale for
each question is as follow:
o 1 = Not at all
o 2 = Several days
o 3 = More than half the days
o 4 = Nearly every day
The total score was calculated by summing the responses to these nine questions (phq_1 to
phq_9). A total score between 27 and 36 indicates severe depression, which was used as the
criterion to include participants in the analysis.

8
● Race/Ethnicity: Participants were asked to select as many race/ethnicity categories from
which they identified, from a selection of 8 (including a category for “other”). The
race/ethnicity variable in this study was constructed from these responses. Categories
included those who identified as just one race (White, Asian, Black, Hispanic), two or
more races, and other.
● Perceived Public Stigma: Agreement with the statement that most people think less of a
person who has received mental health treatment, rated from Strongly agree (1) to
Strongly disagree (6).
Descriptive & Bivariate Analysis
Descriptive statistics, including demographics such as age, race, gender, and
socioeconomic status, were summarized using means (SD) and N (%), as appropriate.
Descriptive statistics were produced overall and by the levels of the outcome variable (academic
persistence). Cross-tabulations were performed to explore the associations between therapy
attendance, race, and perceived public stigma towards mental health treatment. Specifically, the
relationship between therapy attendance and race was examined to identify potential disparities
or trends in therapy utilization across racial groups. Additionally, cross-tabulations were
conducted to explore the relationship between race/ethnicity and perceived public stigma, as well
as between perceived public stigma and therapy attendance. Associations were examined using
the Wilcoxon rank sum test and Pearson’s Chi-squared test, with a 2-sided alpha of 0.05.
Regression Modeling
Univariable analyses were conducted to assess the individual impact of therapy
attendance and all model covariates on academic persistence. The proportional odds assumption
was assessed using the Brant test, which revealed violations for several key covariates in the
initial cumulative logit regression models (Appendix E, Figure E.1). Significant deviations were
observed for age (p<0.001), constructed race (p=0.02), constructed gender (p<0.001),

9
socioeconomic status (p<0.001), academic impact (p=<0.001), health insurance (p=0.04),
knowledge of campus resources (p<0.001), and personal stigma (p<0.001). Therefore, we
proceeded by using logistic regression models with the binary persistence outcome (i.e.,
“hopeful” vs. “doubtful”). Preliminary analyses examined the effect of the independent variable
of interest: counseling/therapy. Afterward, a multivariable logistic regression analysis was
performed to quantify the effect of counseling/therapy and other covariates on persistence. We
evaluated both log-odds (beta coefficients) and odds ratios to assess the strength and direction of
these effects. Interaction terms, hypothesized a priori, were examined between
counseling/therapy, constructed race, and perceived public stigma. Three separate models were
fitted, each incorporating one of these interaction terms. Interaction terms were tested via the
Likelihood Ratio Test (LRT), comparing the model with the interaction term to one without it.
Terms were retained in the model if the LRT produced a p-value of less than .05.
Model Evaluation
The fit of the final logistic regression model was assessed using the Hosmer-Lemeshow
test Basic diagnostic procedures, including Cook’s distance and leverage, were employed to
identify potentially influential observations and assess multicollinearity in the model, thereby
testing some of the assumptions of the logistic regression model. All analyses were performed in
R (v4.2.1). Descriptive tables were produced with the gtsummary package, regression modeling
was performed with the glm package, visualizations were produced with the ggplot2 package,
and model diagnostics were performed in the car and influence.ME packages.

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CHAPTER 3. RESULTS
Table 1. Demographic Characteristics of the Full Dataset

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Sample Characteristics
Table 1 provides an overview of the demographic characteristics of the full dataset
categorized by students' academic persistence. Overall, the dataset consists of 129,241 students,
with significant demographic variations observed across hopeful (N = 99,956, 77%) and doubtful
(N = 29,268, 23%) groups. The analysis reveals significant disparities in age, therapy attendance,
race, gender, socioeconomic status, exercise habits, academic impact, chronic disease
prevalence, diagnosed mental illness, health insurance coverage, knowledge of campus
resources, personal stigma perceptions, and perceived public stigma. All variables demonstrated
statistical significance in the model at a p-value threshold of 0.05.

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Table 2. Demographic Characteristics of Filtered Data (Participants reporting severe or
frequent depressive symptoms)

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Table 2 provides a comprehensive demographic analysis of the subset of students reporting
depressive symptoms nearly every day (N=17,118), stratified by their academic persistence
status: hopeful (N=9,817, 57%) and doubtful (N=7,301, 43%). The demographic composition of
this subgroup mirrors the full dataset, predominantly consisting of female students (73%), with
White students forming the largest racial group (60%). Other racial groups, including Asian
(9%), Black (9%), Hispanic (8%), Two or More Races (11%), and Other (3%), are also
represented, underscoring the diverse yet consistent racial composition across both groups. The
average age of the filtered data is approximately 3 years lower than the mean age of the full
dataset (21 years compared to 24 years in the full dataset).
A notable distinction in the full dataset is that approximately 50% of students had never attended
therapy. However, within the most depressed subset, there is a more equitable distribution in
therapy attendance, suggesting higher engagement with mental health services among severely
depressed students. This subgroup also reports a significantly lower mean score for public
perceived stigma, indicating a greater awareness of societal negative attitudes towards those
seeking mental health treatment. In the depressed group, the proportion of individuals reporting
frequent academic impairment due to emotional or mental difficulties increases from 28% in the
full dataset to 62%, underscoring the substantial academic challenges faced by these students.
Additionally, the depressed subset demonstrates a significantly higher proportion of individuals
experiencing stress related to socioeconomic status, highlighting another critical area of concern.
Across both the full dataset and the subset focusing on depressed individuals, several consistent
patterns emerge, highlighting distinct challenges faced by doubtful students. Doubtful students

14
consistently show higher proportions of negative indicators, such as reduced exercise
engagement and increased societal stigma perceptions, highlighting their unique and
multifaceted challenges. Additionally, these students exhibit higher rates of chronic and
diagnosed mental health conditions compared to hopeful students. These patterns provide
valuable insights for developing targeted mental health interventions, emphasizing the need for a
more inclusive and supportive academic environment.
Bivariate Analyses
Table 3. Therapy Utilization by Race
Based on the data presented in Table 3, which illustrates therapy utilization across
different racial and ethnic groups, several notable patterns emerge. White students exhibit the
highest engagement with therapy, particularly accessing it both before and during college (33%
of White students), contrasting sharply with other racial groups. Asian, Black, and Hispanic
students show significantly lower rates of therapy utilization, with the majority having never
accessed therapy (49% of Asian students, 47% of Black students, and 42% of Hispanic students).
Among Asian students, a higher percentage accessed therapy since starting college (24%)
compared to other periods, suggesting delayed help-seeking behaviors until facing greater
challenges. Black students also demonstrate a similar trend, with 22% accessing therapy since

15
starting college. Hispanic students accessed therapy more evenly across all periods, with 20%
prior to college and 18% since starting college. Students identifying as Two or More races show
higher engagement levels similar to White students, with 32% accessing therapy both before and
during college. Other racial categories exhibited patterns similar to Asian and Black students in
therapy utilization. These disparities underscore the need for targeted interventions to improve
mental health service access among Asian, Black, and Hispanic students, potentially focusing on
early intervention strategies to support these groups throughout their educational experience.
Table 4. Stigma Perception by Race
Table 4, which examines the relationship between perceived public stigma and
race/ethnicity reveals distinct patterns across different groups, highlighting discrepancies in
societal attitudes towards mental health counseling. Most students across all racial groups
perceive some level of public stigma towards mental health treatment. For instance, 56% of
White students, 61% of Asian students, 63% of Black students, and 59% of Hispanic students
fall into the categories of strongly agree, agree, and somewhat agree, indicating agreement with
the presence of public stigma. Notably, Black students exhibit the highest overall percentages in
the strongly agree (15%) and agree (20%) categories. Conversely, White students show the
highest disagreement with public stigma, with 43% in the somewhat disagree, disagree, and

16
strongly disagree categories, followed by Hispanic students (40%), Asian students (38.6%), and
Black students (37%). These findings highlight that both Asian and Black students perceive
higher levels of public stigma towards mental health treatment, while White students are more
likely to disagree with these societal attitudes, underscoring the need to address these disparities
to improve mental health counseling engagement across different racial groups.
Table 5. Therapy Utilization by Stigma Perception
The relationship between perceived public stigma and therapy attendance shows notable
patterns, as detailed in Table 5. Among students who have never accessed therapy, 13% strongly
agree with the existence of public stigma, compared to 12% of those who accessed therapy
before college, 12% who accessed it since starting college, and 15% who accessed it both before
and during college. Conversely, 10% of students who have never accessed therapy strongly
disagree with stigma, which is higher than the percentages for those who accessed therapy at any
time. Overall, 58% of students perceive some level of public stigma, while 42% disagree with
the existence of stigma. Specifically, 58% of those who have never accessed therapy perceived
public stigma, compared to 57% among those who accessed therapy before college, 57% for
those who accessed it since starting college, and 59% for those who accessed it both before and
during college. The perception of strong disagreement with stigma remains consistent across

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therapy utilization levels, indicating that while personal therapy experiences vary, societal
attitudes toward mental health treatment are perceived similarly across different levels of therapy
engagement.
The tables provide a summary of therapy attendance, perceived stigma, and their
intersection with race/ethnicity among college students, offering insights into therapy behaviors
and stigma perceptions across diverse student populations. Together, these findings underscore
the need for culturally sensitive approaches to mental health support on college campuses.
Table 6. Unadjusted Analysis of Therapy Attendance on Academic Persistence (Doubtful = 1,
Hopeful = 0) in University Students

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Table 7. Adjusted Analysis of Therapy Attendance on Academic Persistence (Doubtful = 1,
Hopeful = 0), Controlling for Demographic Factors

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Regression Analyses
In unadjusted analyses, there was a statistically significant association between attending
therapy and lower persistence. Students who attended therapy prior to starting college had 39%
higher odds of expressing doubts about completing their degree on time compared to those who
had never attended therapy (OR = 1.39, p < 0.001; Table 6). Similarly, students who attended
therapy since starting college showed 19% increased odds of expressing doubts (OR = 1.19, p <
0.001), suggesting a persistent influence of therapy attendance. Moreover, those who attended
therapy both before and since starting college had 36% higher odds of expressing doubts (OR =
1.36, p < 0.001), indicating a cumulative effect over time. In the model that adjusted for relevant
covariates, these associations remained significant: prior therapy attendance increased the odds
of doubts by 20% (OR = 1.20, p = 0.008; Table 7), while therapy attendance since starting
college increased it by 27% (OR = 1.27, p < 0.001), and similarly attending therapy both before
and since starting college increased it by 28% (OR = 1.28, p < 0.001).
The study also explored interaction effects between therapy attendance, race/ethnicity,
and perceived stigma towards mental health treatment. None of these interactions were
statistically significant: therapy attendance by race/ethnicity (p = 0.18; Appendix F, Figure F.1),
perceived stigma by race/ethnicity (p = 0.85; Appendix G, Figure G.1) and therapy attendance by
perceived stigma (p = 0.55; Appendix H, Figure H.1) did not show meaningful variations in their
effects on academic persistence. These findings indicate that the impact of therapy attendance
and perceived stigma on academic persistence does not significantly differ across racial/ethnic
groups or with varying levels of stigma perception among university students with severe
depression.

20
Final regression models were examined for influential points and outliers. Though some
observations were relatively more influential, we chose to retain these observations as the large
sample size ensures robustness of the model parameter estimates. The Hosmer-Lemeshow
goodness-of-fit test yielded a non-significant p-value (p = 0.28), indicating that the logistic
regression model fits the data well.

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CHAPTER 4. DISCUSSION
Main Findings
The study explored predictors of academic confidence among university students with
severe depression, revealing several significant findings. Therapy attendance emerged as a
significant predictor, with students who had engaged in therapy showing higher odds of
expressing doubts about completing their degrees on time compared to those who had never
attended therapy. Positive attitudes toward mental health treatment were also associated with
greater persistence, aligning with similar findings reported in previous studies (Cuijpers et al.,
2014). This supports the notion that students with more favorable views on therapy are more
likely to believe in their ability to persist academically. Age, gender, financial concerns, exercise
habits, academic impact, diagnosed mental health conditions, knowledge of campus resources,
and personal stigma were additional significant factors influencing students' confidence in
academic persistence.
We did not find any moderating effects among therapy attendance, therapy stigma, and
race/ethnicity in their effect on academic persistence. This suggests that therapy attendance had
consistent effects regardless of perceived stigma and racial/ethnic background, contrary to some
literature suggesting potential moderation effects of stigma on therapy effectiveness (Gary,
2005). Notably, there was no significant interaction observed between race/ethnicity and therapy
stigma, indicating similar perceptions of stigma across racial groups. This finding contrasts with
studies that have highlighted significant differences in beliefs about the causes and appropriate
treatments of mental illness among different racial groups (Schnittker, Freese, & Powell, 2000).

22
This study provides substantial insights into the complex relationships involving therapy
utilization, stigma, race/ethnicity, and academic persistence among university students. These
insights underscore the necessity for targeted interventions aimed at improving both mental
health and academic outcomes. Furthermore, the Hosmer-Lemeshow test for the logistic
regression model indicated a well-fitting model (p = 0.28), thereby reinforcing the validity of the
study's findings.
Limitations
One significant limitation is the cross-sectional nature of our study, which restricts our
ability to make causal inferences about the relationship between therapy attendance and
persistence. While we can observe associations, the temporal direction of these relationships
cannot be definitively established. Longitudinal studies would be necessary to determine causal
links and the directionality of these effects (Jones & Brown, 2019). Understanding whether
therapy attendance contributes to doubts about degree completion or if students with doubts are
more likely to seek therapy remains an open question.
The generalizability of our findings is another critical consideration. Our study
categorized racial groups without differentiating between immigrant and non-immigrant
experiences. This oversight may obscure important cultural and contextual differences that
influence both mental health service utilization and educational persistence. Immigrant students
often face unique stressors, such as acculturation challenges and immigration-related
uncertainties, which could differentially impact their mental health and academic outcomes
compared to their non-immigrant peers (Nguyen & Davis, 2018). Future research should aim to

23
disaggregate these groups to better understand the nuanced experiences of diverse student
populations. Additionally, considering whether students are first-generation or not could further
enrich our understanding of these dynamics.
Our initial plan to use the ordinal persistence variable in an ordinal regression analysis
was abandoned due to violations of the proportional odds assumption. This statistical issue led us
to develop the binary persistence variable, which we used in logistic regression. Our decision to
categorize "Strongly agree" and "Agree" as hopeful and "Somewhat agree" to "Strongly
disagree" as doubtful was based on clinical significance rather than purely statistical
considerations. We believe this dichotomy better captures meaningful differences in students'
confidence levels and aligns more closely with clinical observations of mental health and
academic resilience; however, the choice of cut point is a limitation, as alternative thresholds
might produce different results. Additionally, although multinomial regression was a viable
alternative, we opted for logistic regression due to its suitability for examining the binary
outcome of persistence, which facilitates a clearer interpretation of the impacts of therapy and
stigma on students' academic confidence.
Another significant limitation of our study is the exclusive focus on students within the
"most depressed" category, which may have skewed our results. Students who significantly
benefit from therapy may no longer remain in this category, as therapy can effectively reduce
their depressive symptoms. This exclusion means we might not fully capture the positive impact
of therapy on academic persistence for those who have improved and are no longer classified as
"most depressed." Additionally, we hypothesized that the variable measuring the impact of

24
mental health issues on students' academic abilities would help explain the relationship between
mental health and academic persistence. However, our analysis indicated that this was not the
case, further complicating our understanding of these dynamics. This highlights the need for
broader research encompassing students across various levels of depression severity to better
understand the full spectrum of therapy's effects on academic outcomes.
Implications/Further Research
The pandemic's impact on mental health and academic persistence cannot be understated
(Garcia et al., 2021). The shift to online learning environments, social isolation, and general
uncertainty during the COVID-19 pandemic likely exacerbated mental health challenges for
many students. These factors, combined with the inherent stressors of academic life, create a
complex landscape in which mental health support becomes crucial. Our study period coincided
with significant disruptions, and future research should explore how these specific pandemicrelated factors influenced the mental health and persistence of students. Understanding these
dynamics is crucial for developing targeted interventions that support students' mental health and
academic success during times of crisis.
Our analysis was further constrained by the absence of complete data for several
potentially confounding variables. Variables related to beliefs about treatment efficacy (i.e.,
perception of therapy's helpfulness for clinically depressed peers) and perception of campus
support had substantial missing data, compromising their reliability. Additionally, variables
measuring the competitiveness of academic environments and those related to stigma were also
excluded due to incomplete responses. These factors could play critical roles in influencing both

25
therapy utilization and persistence but were not included in the final analysis. Moreover,
financial stress and socioeconomic status are significant determinants of both mental health and
academic outcomes. Financial insecurity can exacerbate mental health issues and hinder students'
ability to focus on their studies, highlighting the need for comprehensive support systems that
address both financial and mental health needs (James & Kim, 2020).
Social support networks, both within and outside the campus environment, also play a
crucial role in students' mental health and academic persistence. The presence of a robust support
system can mitigate the adverse effects of stress and mental health challenges, providing students
with the resources and encouragement needed to continue their studies (Adams & Stevenson,
2017). Conversely, a lack of support can exacerbate feelings of isolation and helplessness,
making it more difficult for students to seek help and stay committed to their academic goals.
Future research should examine the interplay between social support, mental health service
utilization, and academic persistence to develop more holistic support strategies.
We recognize that other variables, particularly those depicting experiences of trauma
(e.g., sexual violence, drug abuse, or other negative life events), likely affect students' views on
and practices of attending therapy, as well as their belief in their ability to persist in their studies.
The exclusion of such variables due to missing data or oversight represents a significant
limitation of our study. Trauma can have profound and lasting effects on mental health,
influencing both the need for therapy and the likelihood of academic success (Miller & Cruz,
2019). Addressing these experiences in future research could provide deeper insights into the
barriers and facilitators of therapy utilization and educational persistence.

26
Despite these limitations, our study provides valuable insights into the mental health
challenges faced by university students. It underscores the importance of accessible mental
health services and the role they play in supporting students during challenging academic
periods, such as the COVID-19 pandemic. Future research should aim to address these
limitations by utilizing longitudinal designs, differentiating between diverse cultural experiences,
and ensuring comprehensive data collection for critical confounding variables. By exploring
these avenues, future studies can contribute to a more nuanced understanding of the interplay
between mental health and academic persistence, guiding the development of effective support
strategies for students across diverse university settings.

27
CHAPTER 5. SUMMARY, CONCLUSIONS AND
RECOMMENDATIONS
In this study, we explored the relationships between therapy utilization, stigma,
race/ethnicity, and academic persistence among university students. Our findings revealed that
therapy attendance at any point in students' lives was associated with higher odds of expressing
doubts about completing their degrees on time. However, the temporal direction of this
association remains ambiguous, as our cross-sectional study design limits causal inference.
Despite finding no significant interactions between therapy attendance, stigma, and
race/ethnicity on academic persistence, our study identified nuanced trends. These included
varying perceptions of therapy and stigma among different racial and ethnic groups, highlighting
the need for culturally sensitive mental health interventions within higher education.
These findings underscore the importance of future longitudinal research to explore the
temporal dynamics and causal relationships between therapy utilization, stigma, and academic
persistence. Such research can inform the development of effective strategies to support the
mental health and academic success of all collegiate students.

28
REFERENCES
Adams, P. C., & Stevenson, D. L. (2017). Social support networks and academic persistence:
Implications for student success. Journal of Higher Education, 88(3), 409-430.
Cervantes, R. C., & Cordova, D. (2011). Life experiences of Hispanic adolescents:
Developmental and language considerations in acculturation stress. Journal of
Community Psychology, 39(3), 336-352.
Cuijpers, P., Karyotaki, E., Weitz, E., Andersson, G., Hollon, S. D., van Straten, A., & Reynolds,
C. F. (2014). The effects of psychotherapies for major depression in adults on remission,
recovery and improvement: A meta-analysis. Journal of Affective Disorders, 159, 118-
126.
Eisenberg, D., Golberstein, E., & Gollust, S. E. (2009). Help-seeking and access to mental health
care in a university student population. Medical Care, 47(3), 223-229.
Garcia, F. E., Alvarez, R., & Smith, P. M. (2021). The impact of COVID-19 on mental health
and academic persistence among university students. Journal of American College
Health, 69(6), 663-671.
Gary, F. A. (2005). Stigma: Barrier to mental health care among ethnic minorities. Issues in
Mental Health Nursing, 26(10), 979-999.
Givens, J. L., & Tjia, J. (2002). Depressed medical students' use of mental health services and
barriers to use. Academic Medicine, 77(9), 918-921.
James, M. L., & Kim, S. H. (2020). Financial insecurity and mental health among college
students: The role of support systems. Journal of Student Financial Aid, 50(1), Article 3.
Jones, L. M., & Brown, R. S. (2019). Longitudinal studies on mental health and academic
persistence in higher education. Educational Research Review, 28, 100287.
Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity
measure. Psychiatric Annals, 32(9), 509-515.
Lambert, M. J. (2013). Bergin and Garfield's Handbook of Psychotherapy and Behavior Change.
John Wiley & Sons.
Masuda, A., & Latzman, R. D. (2011). Examining associations among multicultural counseling
competencies and attitudes toward seeking professional psychological help. Journal of
Counseling Psychology, 58(1), 107-115.
Miller, A. L., & Cruz, C. F. (2019). The effects of trauma on mental health and academic success
among college students. Journal of Trauma & Dissociation, 20(4), 456-472.

29
Nguyen, T. T., & Davis, C. R. (2018). Acculturation challenges and mental health among
immigrant university students. Journal of Counseling Psychology, 65(4), 528-540.
Oh, H., Pickering, T. A., & Martz, C. (2022). Ethno-racial differences in anxiety and depression
impairment among emerging adults in higher education. Journal of Affective Disorders,
307, 95-103.
Schnittker J, Freese J, Powell B. (2000). Nature, nurture, neither, nor: Black-White differences in
beliefs about the cause and appropriate treatment of mental illness. Social Forces, 78,
1101-1132.
Smith, J. A., Williams, R. L., & Taylor, M. K. (2020). Mental health support services and
educational outcomes among Generation Z students. Journal of College Student
Development, 61(5), 553-567.
Smith, T. B., Rodríguez, M. D., & Bernal, G. (2007). Culture. Journal of Clinical Psychology,
63(7), 611-631.
Yeh, C. J. (2002). Taiwanese students' gender, age, interdependent and independent selfconstrual, and collective self-esteem as predictors of professional psychological helpseeking attitudes. Cultural Diversity and Ethnic Minority Psychology, 8(1), 19-29.

30
APPENDICES
Appendix A. Figure A.1: Cook's Distance for Influential Observations in the Logistic Regression
Model. Observations with a Cook's Distance greater than the threshold value (4/n) are
considered influential and are highlighted in red. The dashed red line represents the threshold
Cook's Distance value, calculated as 4 divided by the number of observations in the dataset.

31
Appendix B. Figure B.1: Leverage Values for Observations in the Adjusted Logistic Regression
Model. Observations with leverage values greater than twice the average leverage are
considered influential and are highlighted in red. The dashed red line represents the threshold
leverage value, calculated as 2 times the average leverage.

32
Appendix C. Figure C.1: Standardized Residuals for Observations in the Adjusted Logistic
Regression Model. Observations with absolute standardized residuals greater than 2 are
considered influential and are highlighted in red. The dashed red lines at -2 and 2 represent the
threshold for identifying influential points.

33
Appendix D. Figure D.1: Multicollinearity Analysis: VIF and GVIF Values. Variance Inflation
Factor (VIF) values greater than 5 indicate potential multicollinearity issues, which may affect
the stability and interpretability of the regression model coefficients.

34
Appendix E. Figure E.1: Brant Test Results Testing for Proportional Odds in Initial Cumulative
Logit Model. In the Brant test results, p-values greater than 0.05 suggest that the proportional
odds assumption holds for the respective variables. Conversely, p-values below 0.05 indicate
potential violations of the proportional odds assumption. In the example provided, some p-values
are below the 0.05 threshold, suggesting that the model may not meet the proportional odds
criterion for all tested variables.

35
Appendix F. Figure F.1: Adjusted Analysis of Therapy Attendance on Academic Persistence
(Doubtful = 1, Hopeful = 0), Controlling for Demographic Factors and Including Interaction
Effect: Race-Therapy

36
Appendix G. Figure G.1: Adjusted Analysis of Therapy Attendance on Academic Persistence
(Doubtful = 1, Hopeful = 0), Controlling for Demographic Factors and Including Interaction
Effect: Race-Stigma

37
Appendix H. Figure H.1: Adjusted Analysis of Therapy Attendance on Academic Persistence
(Doubtful = 1, Hopeful = 0), Controlling for Demographic Factors and Including Interaction
Effect: Stigma-Therapy

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Creator Walker, Derek Merced (author)

Core Title Exploring mental health care utilization and academic persistence among college students: evaluating racial and stigma-related disparities

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Degree Conferral Date 2024-08

Publication Date 08/05/2024

Defense Date 08/02/2024

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Tag academic persistence,academic success,College students,Higher education,mental health care,mental health services ethnic disparities,mental health utilization,racial differences,racial disparities,stigma,stigma-related barriers,student well-being,therapy stigma

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