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Mental health consequences of structural racism on Latine youth: moderating role of racial-ethnic socialization

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Mental health consequences of structural racism on Latine youth: moderating role of racial-ethnic socialization

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Content Mental Health Consequences of Structural Racism on Latine Youth:
Moderating Role of Racial-Ethnic Socialization
By
Adrelys Mateo Santana
A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS, AND SCIENCES
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF ARTS
(PSYCHOLOGY)
December 2024
Copyright 2024 Adrelys Mateo Santana

ii
TABLE OF CONTENTS
List of Tables……………………………………………………………………………………..iii
List of Figures…………………………………………………………………………………….iv
Abstract…………………………………………………………………………………………...v
Chapter One: Introduction………………………………………………………………………...1
Chapter Two: Method……………………………………………………………………………..9
Chapter Three: Results…………………………………………………………………………...18
Chapter Four: Discussion………………………………………………………………………...28
Tables…………………………………………………………………………………………….43
Figures……………………………………………………………………………………………45
References………………………………………………………………………………………..51
Appendices………………………………………………………………………………….……57
Appendix A: Example of Demographic Questionnaire Completed by Adolescents…….57
Appendix B: Example of Demographic Questionnaire Completed by Caregivers……...62
Appendix C: Information about the Quality of the Geocoding Results…………..……...74
Appendix D: Information about the Indicators of Structural Racism……........................75
Appendix E: Information about the Quality of the Geocoding Results…………..……...77

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LIST OF TABLES
Table 1. Correlations and Descriptives for All Variables………………………………………..43
Table 2. Fit Statistics Across Three Moderation Models ……………………………………….44
Table C1. Quality of Geocoded Addresses ……………………………………………………...74
Table D1. Description of the Indicators of Structural Racism and Year of Data Collection……75
Table E1. Main Effects and Moderation Effects of Structural Racism with Outliers and with
Outliers Winsorized……………………………………………………………………………...77
Table E2. Main Effects and Moderation Effects of Education with Outliers and with Outliers
Winsorized……………………………………………………………………………………….78
Table E3. Main Effects and Moderation Effects of Healthcare with Outliers and with Outliers
Winsorized……………………………………………………………………………………….79
Table E4. Main Effects and Moderation Effects of Food Insecurity with Outliers and with
Outliers Winsorized……………………………………………………………………………...80
Table E5. Main Effects and Moderation Effects of Housing with Outliers and with Outliers
Winsorized……………………………………………………………………………………….81
Table E6. Main Effects and Moderation Effects of Employment and Income with Outliers and
with Outliers Winsorized………………………………………………………………………...82
Table E7. Main Effects and Moderation Effects of Policing with Outliers and with Outliers
Winsorized……………………………………………………………………………………….83

iv
LIST OF FIGURES
Figure 1. Conceptual Model of Estimated Regression and Moderation Paths…………………..45
Figure 2. Distribution of Families Across Los Angeles County Neighborhoods………………..46
Figure 3. Interaction of Healthcare Index of Structural Racism and Preparation for Bias with
Outliers…………………………………………………………………………………………...47
Figure 4. Interaction of Healthcare Index of Structural Racism and Preparation for Bias with
Outliers Winsorized……………………………………………………………………………...48
Figure 5. Interaction of Policing Index of Structural Racism and Cultural Socialization with
Outliers…………………………………………………………………………………………...49
Figure 6. Interaction of Policing Index of Structural Racism and Preparation for Bias with
Outliers Winsorized……………………………………………………………………………...50

v
Abstract
Structural racism heightens racial-ethnic disparities in mental health, increasing the risk
for poor psychological outcomes among racial-ethnic minoritized youth. Racial-ethnic
socialization (RES)–messages about race and racism–simultaneously buffer against the impact of
racism. However, extant work overlooks the influence of structural racism on mental health and
how RES might moderate these effects. This study explored the link between structural racism
and Latine youths’ externalizing and internalizing symptoms and examined the moderating effect
of caregiver RES messages (e.g., preparation for bias, cultural socialization, minimization of
racism). Participants included forty-four Latine caregiver-adolescent (10-17 years old) dyads.
Youth reported on their externalizing and internalizing symptoms, while caregivers reported on
their RES practices. Geospatial modeling was leveraged to develop indices of structural racism.
Path models were estimated to test the main effects of structural racism on youths’
symptomatology and the moderating effect of caregiver RES practices. Structural racism was not
associated with youths’ externalizing or internalizing symptoms. However, preparation for bias
moderated the impact of the healthcare index of structural racism, and cultural socialization
moderated the impact of the policing index of structural racism on youths’ externalizing and
internalizing outcomes. This pattern of results did not emerge for minimization of racism
messages nor other indices of structural racism. Overall, findings suggest that indicators of
structural racism differentially impact mental health outcomes, and thus robust, differentlyweighted structural racism indices are warranted. Findings also underscore the benefits of RES
practices and suggest that its moderating role should be considered in the context of other racist
experiences.

1
Chapter One: Introduction
Racism, or the social system through which dominant racial-ethnic groups use their
power to devalue and disempower minoritized racial-ethnic groups, is a pervasive and significant
stressor facing Latine youths in the United States (U.S.; Jones, 1997; Lopez et al., 2010; Pasco et
al., 2022). Experiences of racism have been a long-standing reality for Latine youth, often
stemming from various contexts, such as anti-immigration laws and documentation assumptions
(Pasco et al., 2022). For instance, a recent study found that the most common forms of
discrimination reported by U.S.-born Latine youth were related to assumed (undocumented)
immigration status, unintelligence, criminality, and inferiority (Pasco et al., 2022). These
experiences of racism compromise Latine youths’ mental health, conferring increased risk for
internalizing (Benner et al., 2018; Romero et al., 2007; Smokowski & Bacallao, 2007; Zeiders et
al., 2013) and externalizing symptomatology (Benner et al., 2018; Okamoto et al., 2009; Romero
et al., 2007). Thus, there is an urgent need to better understand the ramifications of racism on
Latine youths’ well-being and identify protective factors that may buffer the detrimental mental
health effects of racism.
Racism and Mental Health
Extant work on the mental health consequences of racism has primarily focused on
interpersonal racism, defined as the differential treatment and assumptions about a group of
people based on their racial-ethnic background (Jones, 2000). However, there is growing
recognition that structural racism, or the numerous practices that promote racial-ethnic
discrimination and inequity across multiple systems (e.g., housing, education, healthcare,
policing, nutrition, and employment), is also a key driver of mental health disparities among

2
racial-ethnic minoritized youth (Alvarez et al., 2022; Bailey et al., 2017; Dennis et al., 2021).
Theoretically, researchers have defined structural racism as a system of institutions, laws,
policies, and cultural practices grounded on historical events that promote the unequal
distribution of resources and opportunities, actively disadvantaging racial-ethnic minoritized
groups (Bailey et al., 2017; Jones, 2002). According to Jones (2002), structural racism is often
legalized (e.g., redlining, voting suppression), and the resulting disadvantages are often inherited
through generations. The transgenerational effect of structural racism further perpetuates
discriminatory thoughts and views, which, in turn, further promotes racism and racist practices
(e.g., the continued unequal distribution of resources; Bailey et al., 2017).
Structural racism is of particular concern to psychologists and other mental health
professionals because it fosters the unequal allocation of social determinants of health,
promoting health disparities and poor mental health outcomes (Alvarez et al., 2022; Bailey et al.,
2017; Dennis et al., 2021; Hardeman et al., 2022). More specifically, researchers theorize that
structural racism impacts the environment where people reside and the opportunities available to
them, which may impact their access to healthcare and may result in poor healthcare quality
(Jones, 2002). Although most of the extant work on the health consequences of structural racism
has been grounded in public health theories and primarily focused on physical health, structural
racism likely has a similar domino effect on mental health. Therefore, research is warranted to
further an understanding of the mental health consequences of structural racism among racialethnic minoritized individuals, such as Latine youth.
Measuring Structural Racism
Researchers have employed diverse approaches to quantifying structural racism. For
instance, some researchers have captured structural racism by assessing the availability of

3
individual domains, such as housing, education, employment, or healthcare, among others, for
Black versus White families (Bailey et al., 2017; Groos et al., 2018). Others have further
operationalized structural racism by comparing the proportion of Black and White individuals
impacted by discriminatory laws, policies, and practices, such as redlining and voting
suppression (Groos et al., 2018; Needham et al., 2022). Some have focused on specific
neighborhood disadvantages (e.g., socioeconomic disadvantage; Singh, 2003). More recently, a
few researchers have suggested that structural racism may also be measured by tying specific
indicators of structural racism (e.g., increased police surveillance in predominantly racial-ethnic
minoritized neighborhoods) to past and current policies (Neblett & Neal, 2022; Needham et al.,
2022). The numerous forms in which researchers have tried to measure structural racism
highlight the complexity of capturing such an interconnected systematic issue.
A significant gap in the efforts to capture structural racism is the lack of studies assessing
the differential utility and predictive validity of distinct approaches (e.g., theory-driven vs. datadriven) for measuring structural racism. As evidenced by the numerous approaches researchers
use to capture structural racism, this is a complex system to measure and disentangle. Therefore,
there is not one clear approach researchers may use. Instead, the measurement of structural
racism may be informed by the goal of the study, the conceptualization of structural racism, and
the historical and geographical contexts of the populations we study, as these may inform how
structural racism presents and what indicators may be particularly salient (Acevedo-Garcia et al.,
2023; Hardeman et al., 2022). Further, the predictive validity of structural racism indices may
vary based on the approach used (Acevedo-Garcia et al., 2023; Trinidad et al., 2022). Thus, it is
critical that researchers explore the impacts of structural racism using various methodologies
(e.g., theory-driven, data-driven approaches) that consider the historical and geographical

4
contexts of the population of interest. This multi-method approach may help ensure that
researchers use the most robust measurement of structural racism, which may better inform
future work and policies.
Another gap in the literature is that indicators of structural racism are often assessed in
isolation rather than concurrently. For instance, studies have linked unidimensional indicators of
structural racism, such as police killings of Black people and Black-White neighborhood
segregation, to a myriad of poor mental health outcomes, such as anxiety and depression (Das et
al., 2021; Do et al., 2019). Though this work is an important first step toward understanding the
mental health consequences of structural racism, there is also a need to explore the mental health
consequences of the various interconnected systems of structural racism. This is of particular
importance because while individual domains of structural racism may have unique effects on
mental health outcomes, many indicators of structural racism are associated with one another and
may have interactive and additive effects on mental health (Hardeman et al., 2022). For instance,
lack of access to quality education may impact educational attainment, which may further impact
employment attainment, which may affect income, and this chain of events may then be linked to
chronic stress, anxiety, and depression (Dougherty et al., 2020; Hardeman et al., 2022; Needham
et al., 2022). Thus, simultaneously assessing multiple dimensions of structural racism and their
impact on mental health is critical; doing so may help identify more comprehensive and
empirically supported approaches and policies to dismantle structural drivers of racial-ethnic
disparities in mental health outcomes and care.
Geocoding, or transforming addresses into latitude and longitude coordinates, is a
specialized approach that allows researchers to examine the distribution of participants in space
and link their experiences or exposures to various social and environmental determinants of

5
health they experience in their residential neighborhood based on where they live (if at a
residence) or spend time in (if measuring residence, school, work, play areas, etc.; Krieger et al.,
2005). More specifically, geocoding allows the conversion of addresses into geographic
coordinates that can be linked to administrative boundaries (e.g., census tracts, counties, cities)
or other data layers based on spatial relationships (U.S. Census Bureau, 2021). Once home
addresses are geocoded, they can be spatially displayed to visualize youths’ residential locations’
immediate neighborhoods. These data can then be linked to indicators of structural racism, such
as inequitable distribution of resources (e.g., access to quality education) and exposure to risks
(e.g., over-policing) at various levels (e.g., census tracts, counties, cities, neighborhoods). The
level of granularity afforded by the individual data layers allows researchers to capture indicators
of structural racism that impact specific communities (e.g., the Latine community). Such rich
data could be analyzed and combined unidimensionally or multidimensionally to capture aspects
of structural racism relevant for exposure assessment with Latine youth.
Cultural Protective Factors of Structural Racism
As we continue to advance our understanding of how racism (e.g., interpersonal or
structural) affects the mental health of Latine youth, it is imperative that we not only improve our
measurement of this construct but also identify strength-based approaches to protecting Latine
youth from its pernicious effects. One strength we can leverage in these efforts is racial-ethnic
socialization (RES), or parents’ transmission of messages about race and racism. RES is a
culturally-specific parenting practice utilized often by parents of color to help youth navigate
racialized contexts by promoting racial coping and positive racial-ethnic identity development in
their children.

6
Racial-ethnic socialization can be categorized based on the content of the messages
parents provide to their children (Hughes et al., 2006). For instance, preparation for bias, defined
as how parents socialize their children to navigate experiences of racism, is one RES strategy
that has received considerable attention in prior research (Hughes et al., 2006; Umaña-Taylor &
Hill, 2020). Through preparation for bias messages, parents increase youths’ awareness of racism
and equip them with the necessary skills to effectively cope with these encounters, which has
been found to buffer the deleterious effects of racism among racial-ethically minoritized youth
(Ayón, 2018; Hughes et al., 2006; Umaña-Taylor & Hill, 2020; Wang et al., 2020). Cultural
socialization, or the explicit or implicit process through which parents of color foster cultural and
racial-ethnic pride in their children by teaching them about their heritage and engaging in
cultural customs and traditions, is another RES strategy that has received considerable attention
(Hughes et al., 2006; Umaña-Taylor & Hill, 2020). Importantly, studies have found associations
between parents’ cultural socialization practices and fewer internalizing and externalizing
problems in racial-ethnically minoritized youth (Hughes et al., 2006).
Another RES strategy that researchers have identified is minimization of racism (Hughes
et al., 2006; Umaña-Taylor & Hill, 2020). Minimization of racism messages tend to emphasize
racial-ethnic equality and the importance of treating everyone equally irrespective of their racialethnic background (Galán et al., 2022; Hughes et al., 2006; Juang et al., 2016). While parents
who provide these messages may be well-intentioned, minimization of racism messages can
often result in color-blind statements (e.g., “racism doesn’t exist” or “everyone has an equal
chance to succeed”) that minimize racial-ethnic differences and youths’ experiences of racism
(Galán et al., 2022; Juang et al., 2016; Neville et al., 2013). The minimization of racism has been
linked to the endorsement of racial-ethnic prejudices and has been hypothesized to perpetuate

7
oppressive systems (Galán et al., 2022; Yi et al., 2022). For instance, minimization of racism
messages may promote ideologies, such as meritocracy, that blame racial-ethnic minoritized
youth for the inequalities they face (Neville et al., 2013). Moreover, when parents of color
minimize racism, they may inadvertently lead their children to internalize negative racial-ethnic
stereotypes and minimize the importance of race and ethnicity when it comes to the inequalities
they face (Neville et al., 2013). This is troubling because the aforementioned is a form of
internalized racism, which studies have found to be associated with anxiety, depression, and poor
psychological well-being (David et al., 2019; Kline et al., 2021; Mouzon & McLean, 2017). This
suggests that messages that minimize racism may exacerbate youths’ mental health problems
associated with experiences of racism. Indeed, although there is limited work assessing the
impact of minimization of racism on youths’ mental health outcomes, a few studies have linked
these messages to poorer mental health outcomes and increased socioemotional distress (Wang et
al., 2020). Notwithstanding, other forms of RES messages, particularly preparation for bias and
cultural socialization, have been shown to buffer the effects of racism on Latine youths’ mental
health and behavioral outcomes (Hughes et al., 2006; Huguley et al., 2019; Umaña-Taylor &
Hill, 2020).
Despite extensive research on parental RES messages, there has been a disproportionate
focus on RES messages as a protective factor against interpersonal racism, often overlooking
whether RES messages buffer other forms of racism that may also impact youths’ well-being,
such as structural racism. Exploring whether the provision of RES messages moderates the
impact of structural racism on Latine youths’ mental health is essential, as it would advance our
understanding of whether RES messages protect against various forms of racism or if it is only a
protective factor for more overt interpersonal experiences of racism. This is a crucial step toward

8
furthering the RES literature and informing the development of more comprehensive prevention
programs and policies. Thus, more work is warranted to assess Latine caregivers’ provision of
RES messages and their impact on Latine youths’ mental well-being associated with structural
racism.
Present Study
Based on the identified gaps in the literature, the present study sought to test four
exploratory aims in a sample of Latine youth and their primary caregivers: (1) Develop three
variations of a structural racism index, including (a) an equally-weighted multidimensional
index; (b) a data-driven approach informed by correlation patterns, and (c) a theory-driven
approach that considers each domain of structural racism independently (hereafter referred to as
the domain-specific approach). (2) Examine the association between structural racism and youth
externalizing and internalizing symptoms, (3) Examine the association between caregiver RES
and youth externalizing and internalizing symptoms, and (4) Examine whether caregiver RES
moderates the impact of structural racism on youths’ externalizing and internalizing symptoms
(see Figure 1). Given the novelty and exploratory nature of the present study, no hypotheses
were established. To our knowledge, this is the first study to explore the moderating role of RES
on the effects of structural racism.

9
Chapter Two: Method
Participants
Latine adolescent-caregiver dyads (n = 44) were recruited from Los Angeles County.
Adolescents were between 10 and 17 years old (Mage = 13.98, SDage = 1.90), and exactly half of
the sample identified their biological sex as female (n = 22, 50%). In regard to gender identity,
47.7% identified as cisgender male, 38.6% as cisgender female, 6.82% as non-binary, 4.55% as
genderqueer, and 2.27% as questioning or unsure. The majority of adolescents identified as
monoracial (88.6% Latine), with only four adolescents identifying as biracial (6.8% Latine and
Black; 2.3% Latine and White), and one identifying as multiracial (2.3% Latine, Black, and
American Indian/Alaska Native). When asked about their ethnicity, 79.5% of adolescents
identified as Mexican, 15.9% identified as Salvadorean, 11.4% identified as Guatemalan, 6.82%
identified as Black, 2.27% identified as Colombian, 2.27% identified as Honduran, and 18.2%
reported two or more ethnicities. The majority of adolescents reported that they were born in the
United States (n = 42, 95.5%) and that they speak Spanish at home (n = 40, 90.9%).
Among caregivers, the mean age was 43.73 years old (SDage = 7.02), and the majority
identified as cisgender female (n = 39, 88.6%). The majority of caregivers (93.2% Latine) were
monoracial. Two caregivers identified as biracial (2.3% Latine and Black; 2.3% Latine and
American Indian/Alaska Native), and one as multiracial (2.3% Latine, Black, and Pacific
Islander). In regard to their ethnicity, 61.4% identified as Mexican/Mexican American, 13.7%
identified as Salvadorean, 13.7% identified as Guatemalan, 2.3% identified as Colombian, 2.3%
identified as African American, 2.3% identified as Belizean, and 2.3% identified as
Mexican/Salvadorean. Most caregivers immigrated to the United States (n = 31, 70.5%), and
about half completed the survey in Spanish (n = 23, 52.3%). Approximately 41% (n = 18) of

10
caregivers did not receive formal schooling or obtained less than a high school diploma, 22.7%
(n = 10) received a high school diploma or GED, and 36.4% (n = 16) completed some college or
obtained a bachelor’s degree. The median household income was $36,307.71 (SD = $28,682.45).
Procedure
Recruitment. The present sample was drawn from a larger study assessing the RES
networks of Latine youth across the family, school, and community contexts. Participants were
recruited via community outreach (e.g., emails to youth-serving agencies and community groups)
and the distribution of flyers at grocery stores, places of worship, restaurants, and other local
businesses. Recruitment also occurred through a partnership with the USC Leslie and William
McMorrow Neighborhood Initiative (NAI), an established college access and success program
for 6th – 12th grade students who attend schools in the South and East Los Angeles areas.
Interested families completed a brief phone screener to determine eligibility. Inclusionary criteria
included (1) adolescents between 10 and 17 years of age, (2) adolescents who identified as
Latine, and (3) a parent or legal caregiver who was willing to participate. Given that the study
protocol required sustained attention, comprehension, and processing, families with intellectual
disabilities were excluded. The presence of active, untreated schizophrenia in the primary
caregiver was also an exclusionary criterion. All study procedures received Institutional Review
Board approval.
Study Visit. Participation involved a one-time, 3-hour laboratory-based assessment at the
University of Southern California (USC). After providing consent (youths ages 14-17 and
caregivers) or assent (youths ages 10-13), adolescents and caregivers independently completed a
battery of surveys assessing demographic characteristics, parental RES, racism exposure, and
mental health outcomes (e.g., internalizing and externalizing symptoms). All measures were

11
completed on iPads via REDCap. Caregivers and youth were compensated $75 and $50,
respectively. Families also received travel reimbursement (e.g., mileage or door-door shared ride
service).
Measures
Demographics
Adolescents and caregivers completed a study-developed demographics questionnaire in
which they reported age, biological sex, gender identity, race, ethnicity, languages spoken at
home, immigration status, and the racial-ethnic composition of their neighborhood and school.
Caregivers also reported on their own educational status and household income (see Appendix A
for the demographic questionnaire completed by the adolescents and Appendix B for the
demographic questionnaire completed by the caregivers).
Independent Variable: Structural Racism
Geocoding. Caregivers provided their residential address at the time of screening. These
addresses were first parsed and standardized and then geocoded using the Texas A&M (TAMU)
GeoServices, a secured software that processes, geocodes, and visualizes addresses in batches
(Goldberg, 2022). Geocoding via TAMU Geoservices involved uploading, validating, and
processing the data. Once processed, we observed the spatial distribution of our families (e.g.,
where and how close or spread out they lived to each other) by visualizing addresses on the map.
TAMU also allowed us to examine the geocoding match quality and manually correct addresses
with poor match quality based on the best available knowledge. The aforementioned was
accomplished by moving locations on a map and documenting geocode manual correction
decisions. Though it should be noted that match quality was generally good, with an average
match score of 98.63 and with majority “Building Centroid” and “Parcel” match types (see

12
Appendix C). The final output contained each address’s latitude, longitude, and geographic
identifier (Geo ID), as well as metadata on the geocoding process and quality of the outputs. This
information was then linked to indicators of structural racism collected at the neighborhood level
across Los Angeles County using an adapted version of The Los Angeles Times neighborhood
boundaries (Sol Price Center for Social Innovation, 2023). Informed by the distribution of our
families across neighborhoods and our interest in understanding the impact of structural racism
as a multidimensional issue, the neighborhood layer was selected as our granularity level (see
Figure 2).
Indicators of Structural Racism. Indicators of structural racism (e.g., education,
healthcare, food insecurity, housing, employment and income, and policing) were extracted from
the Neighborhood Data for Social Change (NDSC; Sol Price Center for Social Innovation, 2023)
platform, a publicly available resource developed by the USC Sol Price Center for Social
Innovation. Specifically, education indicators included chronic school absenteeism, educational
attainment (e.g., high school, associate, and bachelor’s degree), and college enrollment.
Healthcare indicators consisted of the number of hospitals available and the rates of uninsured
individuals. Food insecurity indicators included access to grocery stores, free and reduced-price
lunch, and acceptance of the Supplemental Nutrition Assistance Program (SNAP). Housing
indicators comprised housing stability, rent burden, homeownership, and overcrowding.
Employment and income indicators included unemployment, poverty level (e.g., below 100
poverty line), medium household income, youth opportunity, and labor force. Policing indicators
included police stops, Latine-specific police stops, arrests, Latine-specific arrests, violent crimes,
and property crimes. Indicators were collected as rates, numbers, and percentages. The most

13
recently collected data available for each indicator was extracted, which ranged from 2019-2022
(see Appendix D).
Dependent Variables: Mental Health Outcomes
Externalizing Outcomes. Youth reported on their externalizing symptoms via the Youth
Self-Report (YSR; Achenbach & Rescorla, 2001), a widely used measure of youth problem
behaviors. The 32-item externalizing broadband scale, indexed by the narrowband subscales of
aggressive (e.g., “I physically attack people”) and rule-breaking (e.g., “I steal from places other
than home”) behaviors, was utilized for the present study. Youth rated the extent to which each
behavior described them within the past six months using a 3-point Likert scale (e.g., 0 = not
true, 1 = somewhat or sometimes true, and 2 = very true or often true). The externalizing
subscale demonstrated good internal consistency in the present study (⍺ = 0.83).
Internalizing Outcomes. Youth reported on their internalizing symptoms via the
Revised Children’s Anxiety and Depression Scale (RCADS; Chorpita et al., 2000). The RCADS
is a widely used self-reported (8-18 years old) measure of anxiety and depression symptoms. The
current study assessed the Generalized Anxiety Disorder (GAD; e.g., “I worry about things”) and
the Major Depressive Disorder (MDD; e.g., “I feel sad or empty”) subscales separately. Youth
rated how often they experience each symptom on a 4-point Likert scale (e.g., 0 = never, 1 =
sometimes, 2 = often, and 3 = always). The GAD (⍺ = 0.81) and MDD (⍺ = 0.90) subscales
demonstrated good internal consistency in this study.
Moderators: Racial-Ethnic Socialization
Preparation for Bias. Caregivers reported on their provision of preparation for bias
messages via the Hughes Parental Ethnic Socialization Scale (HPESS; Hughes & Chen, 1997).
The HPESS is a 13-item scale subdivided into three subscales. The present study used only the

14
preparation for bias (e.g., “Told your child that people may try to limit him or her because of
their race/ethnicity”) subscale. Caregivers rated how often they had talked to their children about
experiences of racism and discrimination within the past year using a 5-point Likert scale (e.g., 1
= Never, 2 = Once, 3 = 2 or 3 Times, 4 = 4 or 5 Times, 5 = Six or More Times). The HPESS
preparation for the bias subscale demonstrated good internal consistency (⍺ = 0.89).
Cultural Socialization. Caregivers reported on their provision of cultural socialization
messages via the Familial Ethnic Socialization Measure (FESM; Umaña-Taylor et al., 2004).
The FESM is a 12-item measure of overt (e.g., “I teach my child about his/her ethnic/cultural
background) and covert (e.g., “I celebrate holidays that are specific to our ethnic/cultural
background”) familial ethnic socialization. The present study combined the overt and covert
subscales into a single factor for analysis. Caregivers rated the extent to which they agreed with
each statement using a 5-point Likert scale (e.g., 1 = Not at all, 2, 3, 4, 5 = Very Much). The
internal consistency of the total FESM familial ethnic socialization scale was excellent (⍺ =
0.93).
Minimization of Racism. Caregivers completed the 17-item Juang Parental RacialEthnic Socialization Scale (J-PRESS; Juang et al., 2016), through which they rated the frequency
with which they communicated specific RES messages or engaged in certain activities with their
child using a 5-point Likert scale (e.g., 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Very
Often). The present study only used the minimization of racism subscale (e.g., “Told your child
that racism doesn’t exist”). The internal consistency of the J-PRESS minimization of racism
subscale was reasonable (⍺ = 0.67). However, it should be noted that the minimization of racism
subscale only includes three items, which can impact Cronbach’s alpha (Pallant, 2011).

15
Therefore, we also assessed the inter-item correlation (r = 0.41), which fell within the
recommended range (e.g., 0.15-0.50; Clark & Watson, 1995).
Data Analysis
Aim 1. Develop three variations of a structural racism index. Geocoding techniques were
used to link participants’ addresses with various indicators of structural racism at the
neighborhood level. After the structural racism indicators at the neighborhood layer were linked
to the participants’ addresses, indicators of structural racism were z-scored, and multiple
structural racism indices were developed. First, an equally-weighted multidimensional index of
structural racism was created by aggregating various indicators of structural inequity, including
education, healthcare, food insecurity, housing, employment and income, and policing, into a
single composite. The internal consistency of the multidimensional index was acceptable (⍺ =
0.77-0.83), with outliers not modified and outliers winsorized, respectively. Second, bivariate
correlations between the individual items included in the multidimensional index and youths’
externalizing and internalizing symptomatology were conducted to elucidate relevant correlation
patterns and inform a data-driven, differentially weighted multidimensional index of structural
racism. Lastly, a theory-driven approach was adopted to create domain-specific indices of
structural racism by equally aggregating items within each identified domain (e.g., education,
healthcare, food insecurity, housing, employment and income, and policing) of structural racism.
The domain-specific indices were also created by simple averaging the z-scored items for each
indicator. All indices were developed using SPSS Statistics Syntax (28.0).
Aim 2. Examine the association between structural racism and youth externalizing and
internalizing symptoms. Path models were estimated to test whether structural racism predicts
youth-reported internalizing and externalizing symptoms. Specifically, the different indices of

16
structural racism were separately modeled to explore their direct effects on youths’ externalizing,
anxiety, and depression symptoms. These models accounted for the effect of the moderating
variables (i.e., caregiver-reported preparation for bias, cultural socialization, and minimization of
racism).
Aim 3. Examine the association between caregiver RES and youth externalizing and
internalizing symptoms. Path models were estimated to explore whether different RES messaging
predict externalizing and internalizing symptoms among Latine youths. More specifically,
preparation for bias, cultural socialization, and minimization of racism were modeled to assess
their direct effects on youths’ externalizing, anxiety, and depression symptoms. The model also
accounted for the direct effects of the predictor (i.e., structural racism).
Aim 4. Examine whether caregiver RES moderates the impact of structural racism on
youths’ externalizing and internalizing symptoms. Path models were estimated to explore
whether different types of RES messages moderate the effect of structural racism on youths’
internalizing and externalizing outcomes. Specifically, we estimated the direct effects of
preparation for bias, cultural socialization, and minimization of racism on the relationship
between structural racism and youths’ externalizing, anxiety, and depression symptoms to
examine if it buffered or further strengthened the association. The model also accounted for the
direct effects of the predictor (i.e., structural racism), the moderators (i.e., caregiver-reported
preparation for bias, cultural socialization, and minimization of racism), and the interaction terms
(e.g., preparation for bias by structural racism).
All path models testing aims 2 through 4 were estimated using Mplus 8.0 (Muthén &
Muthén, 1998-2017). Youths’ age and biological sex were included in all the models as
covariates. Full Information Maximum Likelihood (FIML) was used to address missingness.

17
Missing data was generally minimal (< 5%), with the exception of the multidimensional and the
policing indices (9.1% missingness; n = 4). The intraclass correlation (ICC) for most outcomes
did not meet the 0.1 threshold, except for anxiety symptoms (ICC = 0.12). However, the “TYPE
IS COMPLEX” function was used to account for the non-independence of the sample as a result
of some participants residing in the same neighborhood. This function uses Huber and White’s
sandwich estimator to correct for the standard errors (Huber, 1967; White, 1980). Model fit for
all models was assessed via chi-square (χ2), root mean square error of approximation (RMSEA),
comparative fit index (CFI), and standardized root mean square residual (SRMR). As per Kline’s
(2016) recommendations, adequate model fit meets the following thresholds: RMSEA ≤ 0.10;
CFI ≥ 0.90; SRMR ≤ 0.10. Lastly, as a form of sensitivity analysis to account for outliers (i.e.,
values that fell above the upper quartile or below the lower quartile when the interquartile range
[IQR] was multiplied by 1.5 or by 3), all models were conducted with the outliers not modified
and with the outliers winsorized to the nearest extreme value within the upper and lower
quartiles.

18
Chapter Three: Results
Descriptives
Bivariate Correlations
Table 1 shows the bivariate correlations between individual items of structural racism and
variables of interest, as well as the means and standard deviations. Overall, with a few
exceptions, the individual indicators of structural racism were not significantly correlated with
youths’ externalizing, anxiety, or depression symptoms. However, Latine-specific stops
(Externalizing and Anxiety: r = 0.36, p = 0.02) and arrests (Externalizing and Anxiety: r = 0.36,
p = 0.02) were positively associated with youth-reported externalizing and anxiety symptoms.
Additionally, the number of places that accept SNAP was positively associated with depressive
symptoms (r = 0.38, p = 0.01), and the percentage of youth opportunity (i.e., youth who are not
in school or working) was also positively associated with externalizing symptoms (r = - 0.33, p =
0.03). Nevertheless, as the majority of structural racism indicators were not significantly
correlated with youth outcomes, there was not a clear pattern of how indicators should be
differentially weighted. Thus, we were unable to create and test the effects of a differentially
weighted, data-driven index of structural racism. Instead, our models focused on the other two
variations of structural racism indices – an equally-weighted multidimensional index of
structural racism and domain-specific indices of structural racism.
Sensitivity Analyses
Sensitivity analyses suggested that results were generally the same when the outliers were
not modified and when the outliers were winsorized. However, there were a few results that
changed when outliers were winsorized, with some results becoming significant and others
becoming non-significant at the p ≤ 0.05 threshold. Subsequent results will be presented with the

19
outliers not modified; however, we indicate which results shifted when outliers were winsorized
(see Appendix E).
Path Models
Model 1. Equally-Weighted Multidimensional Structural Racism Index
Main Effects of Structural Racism Index and RES. When accounting for youths’ age
and biological sex, there was not a main effect of structural racism on externalizing (β = 0.12, SE
= 0.17, p = 0.45), anxiety (β = 0.11, SE = 0.15, p = 0.48), or depression (β = 0.07, SE = 0.10, p =
0.48) symptoms. Similarly, preparation for bias (externalizing: β = - 0.23, SE = 0.18, p = 0.20;
anxiety: β = - 0.20, SE = 0.17, p = 0.23; depression: β = - 0.13, SE = 0.11, p = 0.22) and
minimization of racism (externalizing: β = - 0.12, SE = 0.13, p = 0.33; anxiety: β = - 0.11, SE =
0.11, p = 0.32; depression: β = - 0.07, SE = 0.07, p = 0.31) were unrelated to youth outcomes.
However, cultural socialization was related to externalizing (β = 0.34, SE = 0.13, p = 0.01),
anxiety (β = 0.29, SE = 0.14, p = 0.03), and depression (β = 0.19, SE = 0.09, p = 0.03) symptoms.
Moderating Effect of RES on Structural Racism Index. There was not a moderating
effect of preparation for bias (externalizing: β = 0.06, SE = 0.19, p = 0.78; anxiety: β = 0.05, SE
= 0.16, p = 0.77; depression: β = 0.03, SE = 0.11, p = 0.78), cultural socialization (externalizing:
β = - 0.07, SE = 0.17, p = 0.69; anxiety: β = - 0.06, SE = 0.14, p = 0.67; depression: β = - 0.04,
SE = 0.09, p = 0.68), or minimization of racism (externalizing: β = 0.05, SE = 0.16, p = 0.75;
anxiety: β = 0.05, SE = 0.15, p = 0.75; depression: β = 0.03, SE = 0.10, p = 0.75) on the
relationship between structural racism and youths’ mental health outcomes. Model fit for the
present model was adequate (χ2 (14) = 17.71, p = 0.22; RMSEA = 0.08; CFI = 0.94; SRMR =
0.07).
Model 2. Domain-Specific Indices of Structural Racism

20
Main Effects of Education Index of Structural Racism and RES. The education index
of structural racism was not significantly related to youth externalizing (β = 0.20, SE = 0.13, p =
0.13), anxiety (β = 0.18, SE = 0.12, p = 0.16), or depression (β = 0.12, SE = 0.09, p = 0.16)
symptoms. Minimization of racism was also unrelated to externalizing (β = - 0.05, SE = 0.12, p
= 0.70), anxiety (β = - 0.04, SE = 0.10, p = 0.70), and depression (β = - 0.03, SE = 0.07, p =
0.69) symptoms. While there was no main effect of preparation for bias on anxiety symptoms (β
= - 0.30, SE = 0.15, p = 0.051) when outliers were not modified, this association was significant
when outliers were winsorized (β = - 0.33, SE = 0.15, p = 0.03; see Table E2). Higher levels of
preparation for bias predicted lower levels of externalizing (β = - 0.34, SE = 0.16, p = 0.03) and
depression (β = - 0.20, SE = 0.10, p = 0.04) symptoms. There was also a positive main effect of
cultural socialization on externalizing (β = 0.43, SE = 0.12, p < 0.001), anxiety (β = 0.38, SE =
0.13, p = 0.004), and depression (β = 0.26, SE = 0.09, p = 0.003).
Moderating Effect of RES on Education Index of Structural Racism. Preparation for
bias (externalizing: β = 0.07, SE = 0.17, p = 0.68; anxiety: β = 0.06, SE = 0.15, p = 0.67;
depression: β = 0.04, SE = 0.10, p = 0.68), cultural socialization (externalizing: β = 0.01, SE =
0.16, p = 0.94; anxiety: β = 0.01, SE = 0.14, p = 0.94; depression: β = 0.01, SE = 0.09, p = 0.94),
and minimization of racism (externalizing: β = 0.11, SE = 0.19, p = 0.57; anxiety: β = 0.09, SE =
0.16, p = 0.57; depression: β = 0.06, SE = 0.11, p = 0.57) did not moderate the relationship
between the education index of structural racism and youths’ symptomatology. Model fit was
good (χ2
(14) = 11.19, p = 0.67; RMSEA < 0.001; CFI = 1.00; SRMR = 0.07).
Main Effects of Healthcare Index of Structural Racism and RES. There was not a
main effect of the healthcare index of structural racism on externalizing (β = 0.08, SE = 0.12, p =
0.53), anxiety (β = 0.06, SE = 0.10, p = 0.51), or depression (β = 0.05, SE = 0.07, p = 0.52)

21
symptoms. Preparation for bias was also unrelated to externalizing (β = - 0.28, SE = 0.15, p =
0.07), anxiety (β = - 0.23, SE = 0.14, p = 0.09), and depression (β = - 0.17, SE = 0.10, p = 0.08)
symptoms. Similarly, minimization of racism was also not significantly associated with
externalizing (β = - 0.13, SE = 0.13, p = 0.33), anxiety (β = - 0.10, SE = 0.11, p = 0.36), or
depression (β = - 0.08, SE = 0.08, p = 0.34) symptoms. There was a positive main effect of
cultural socialization on externalizing (β = 0.45, SE = 0.12, p < 0.001), anxiety (β = 0.37, SE =
0.13, p = 0.004), and depression (β = 0.27, SE = 0.09, p = 0.003) symptoms.
Moderating Effect of RES on Healthcare Index of Structural Racism. There was not
a moderating effect of preparation for bias (externalizing: β = 0.39, SE = 0.24, p = 0.10; anxiety:
β = 0.33, SE = 0.19, p = 0.09; depression: β = 0.23, SE = 0.14, p = 0.10), cultural socialization
(externalizing: β = - 0.24, SE = 0.17, p = 0.17; anxiety: β = - 0.20, SE = 0.15, p = 0.19;
depression: β = - 0.14, SE = 0.11, p = 0.18), or minimization of racism (externalizing: β = - 0.15,
SE = 0.17, p = 0.37; anxiety: β = - 0.13, SE = 0.15, p = 0.38; depression: β = - 0.09, SE = 0.10, p
= 0.37) on the relationship between the healthcare index of structural racism and youths’
externalizing, anxiety, and depression symptoms when outliers were not modified (see Figure 3).
Overall model fit was adequate (χ2
(13) = 16.51, p = 0.22; RMSEA = 0.08; CFI = 0.94; SRMR =
0.07). It should be noted, however, that when outliers were winsorized, there was a moderation
effect of preparation for bias (externalizing: β = 0.39, SE = 0.16, p = 0.01; anxiety: β = 0.33, SE
= 0.13, p = 0.01; depression: β = 0.22, SE = 0.09, p = 0.02) on the relationship between the
healthcare index of structural racism and youths’ symptomatology. Upon probing the interaction,
results indicated that the association between the healthcare index of structural racism and
externalizing, anxiety, and depression symptoms were significant at low levels (b = - 8.21, SE =
3.88, p = 0.03) of preparation for bias, but not at high (b = 6.89, SE = 4.21, p = 0.10) levels of

22
preparation for bias (see Figure 4). Specifically, youth who lived in neighborhoods with low
healthcare access reported more externalizing and internalizing symptoms if they received fewer
preparation for bias messages compared to their peers who received more preparation for bias
messages. Youth who lived in neighborhoods with greater access to healthcare reported
comparable levels of externalizing and internalizing symptoms irrespective of the level (e.g., low
or high) of preparation for bias messages provisioned by their caregivers. Model fit was good (χ2
(14) = 12.98, p = 0.53; RMSEA < 0.001; CFI = 1.00; SRMR = 0.07).
Main Effects of Food Insecurity Index of Structural Racism and RES. When
accounting for youths’ age and biological sex, the food insecurity index of structural racism was
unrelated to youth externalizing (β = 0.10, SE = 0.16, p = 0.52), anxiety (β = 0.08, SE = 0.13, p =
0.51), or depression (β = 0.06, SE = 0.09, p = 0.52) symptoms. Neither preparation for bias
(externalizing: β = - 0.29, SE = 0.15, p = 0.06, anxiety: β = - 0.24, SE = 0.14, p = 0.09,
depression: β = - 0.17, SE = 0.10, p = 0.07) nor minimization of racism (externalizing: β = -
0.12, SE = 0.13, p = 0.35, anxiety: β = - 0.10, SE = 0.11, p = 0.37, depression: β = - 0.07, SE =
0.08, p = 0.35) were related to youth outcomes. Consistent with prior models, higher levels of
cultural socialization predicted greater externalizing (β = 0.46, SE = 0.13, p < 0.001), anxiety (β
= 0.38, SE = 0.14, p = 0.01), and depression (β = 0.27, SE = 0.09, p = 0.004) symptoms.
Moderating Effect of RES on Food Insecurity Index of Structural Racism. There was
not a moderation effect of preparation for bias (externalizing: β = - 0.17, SE = 0.14, p = 0.21;
anxiety: β = - 0.15, SE = 0.11, p = 0.18; depression: β = - 0.11, SE = 0.08, p = 0.21), cultural
socialization (externalizing: β = 0.12, SE = 0.14, p = 0.42; anxiety: β = 0.10, SE = 0.11, p = 0.40;
depression: β = 0.07, SE = 0.09, p = 0.42), or minimization of racism (externalizing: β = - 0.13,
SE = 0.10, p = 0.20; anxiety: β = - 0.11, SE = 0.08, p = 0.18; depression: β = - 0.08, SE = 0.06, p

23
= 0.20) on the relationship between food insecurity and youths’ symptomatology. Overall model
fit ranged from poor to adequate (RMSEA = 0.12; CFI = 0.86; χ2 (14) = 22.75, p = 0.06; SRMR
= 0.07, respectively). Modification indices did not yield any alternative model above the
minimum value to improve model fit. Post-hoc analyses were conducted to improve model fit.
Specifically, we estimated three separate moderation models in which we constrained the other
two moderation paths to zero (e.g., constraint cultural socialization, minimization of racism, and
their respective interaction terms with structural racism when testing the moderation effect of
preparation for bias). However, model fit did not improve (see Table 2).
Main Effects of Housing Index of Structural Racism and RES. The housing index of
structural racism was not significantly related to youth externalizing (β = 0.09, SE = 0.11, p =
0.43), anxiety (β = 0.08, SE = 0.10, p = 0.45), or depression (β = 0.06, SE = 0.07, p = 0.43)
symptoms when accounting for age and biological sex. There was also not a main effect
preparation for bias (externalizing: β = - 0.27, SE = 0.15, p = 0.07; anxiety: β = - 0.23, SE =
0.14, p = 0.10; depression: β = - 0.16, SE = 0.09, p = 0.08) or minimization of racism
(externalizing: β = - 0.14, SE = 0.11, p = 0.21; anxiety: β = - 0.13, SE = 0.10, p = 0.23;
depression: β = - 0.09, SE = 0.07, p = 0.22). Consistent with other models, cultural socialization
was positively related to youth externalizing (β = 0.45, SE = 0.11, p < 0.001), anxiety (β = 0.39,
SE = 0.13, p = 0.002), and depression (β = 0.27, SE = 0.08, p = 0.001) symptoms.
Moderating Effect of RES on Housing Index of Structural Racism. There was not a
moderation effect of preparation for bias (externalizing: β = 0.05, SE = 0.14, p = 0.69; anxiety: β
= 0.05, SE = 0.12, p = 0.69; depression: β = 0.03, SE = 0.09, p = 0.69), cultural socialization
(externalizing: β = - 0.07, SE = 0.11, p = 0.55; anxiety: β = - 0.06, SE = 0.09, p = 0.53;
depression: β = - 0.04, SE = 0.07, p = 0.55), or minimization of racism (externalizing: β = 0.17,

24
SE = 0.09, p = 0.08; anxiety: β = 0.14, SE = 0.08, p = 0.07; depression: β = 0.10, SE = 0.06, p =
0.07) on the relationship between housing insecurity and youths’ symptomatology when
accounting for age and biological sex. Model fit was poor (χ2 (14) = 29.01, p = 0.01; RMSEA =
0.16; CFI = 0.80; SRMR = 0.09). Modification indices did not yield any alternative model above
the minimum value to improve model fit. Post-hoc analyses were conducted to improve model
fit. Specifically, we estimated three separate moderation models in which we constrained the
other two moderation paths to zero (e.g., constraint cultural socialization, minimization of
racism, and their respective interaction terms with structural racism when testing the moderation
effect of preparation for bias). However, model fit did not improve (see Table 2).
Main Effects of Employment & Income Index of Structural Racism and RES. There
was not a main effect of the employment/income index of structural racism on externalizing (β =
0.25, SE = 0.14, p = 0.08), anxiety (β = 0.23, SE = 0.14, p = 0.10), or depression (β = 0.16, SE =
0.09, p = 0.09) symptoms when accounting for age and biological sex. There was a negative
main effect of preparation for bias on externalizing (β = - 0.32, SE = 0.15, p = 0.03) and
depression (β = - 0.20, SE = 0.10, p = 0.04) but not on anxiety (β = - 0.29, SE = 0.15, p = 0.051).
It should be noted that when outliers were winsorized, there was a main effect of preparation for
bias on anxiety (β = - 0.31, SE = 0.14, p = 0.03) symptoms (see Table E6). There was also a
positive main effect of cultural socialization on externalizing (β = 0.46, SE = 0.11, p < 0.001),
anxiety (β = 0.41, SE = 0.13, p = 0.001), and depression (β = 0.28, SE = 0.08, p = 0.001)
symptoms. Minimization of racism was unrelated to youth outcomes (externalizing: β = - 0.12,
SE = 0.11, p = 0.28, anxiety: β = - 0.10, SE = 0.10, p = 0.30, depression: β = - 0.07, SE = 0.07, p
= 0.29).

25
Moderating Effect of RES on Employment & Income Index of Structural Racism.
There was not a moderation effect of preparation for bias (externalizing: β = - 0.05, SE = 0.13, p
= 0.72; anxiety: β = - 0.04, SE = 0.11, p = 0.73; depression: β = - 0.03, SE = 0.08, p = 0.72),
cultural socialization (externalizing: β = - 0.06, SE = 0.17, p = 0.74; anxiety: β = - 0.05, SE =
0.15, p = 0.73; depression: β = - 0.04, SE = 0.11, p = 0.74), or minimization of racism
(externalizing: β = 0.12, SE = 0.12, p = 0.31; anxiety: β = 0.10, SE = 0.10, p = 0.31; depression:
β = 0.07, SE = 0.07, p = 0.31) on the relationship between employment/income index of
structural racism and youth outcomes. Model fit was poor (χ2 (14) = 23.55, p = 0.052; RMSEA =
0.13; CFI = 0.85; SRMR = 0.10). Modification indices did not yield any alternative model above
the minimum value to improve model fit. Post-hoc analyses were conducted to improve model
fit. Specifically, we estimated three separate moderation models in which we constrained the
other two moderation paths to zero (e.g., constraint cultural socialization, minimization of
racism, and their respective interaction terms with structural racism when testing the moderation
effect of preparation for bias). However, model fit did not improve (see Table 2).
Main Effects of Policing Index of Structural Racism and RES. The policing index of
structural racism was not significantly related to youth externalizing (β = 0.04, SE = 0.13, p =
0.74), anxiety (β = 0.04, SE = 0.11, p = 0.74), or depression (β = 0.02, SE = 0.07, p = 0.74)
symptoms. There was also not a main effect of preparation for bias (externalizing: β = - 0.20, SE
= 0.18, p = 0.25, anxiety: β = - 0.17, SE = 0.16, p = 0.27, depression: β = - 0.12, SE = 0.10, p =
0.26) or minimization of racism (externalizing: β = - 0.15, SE = 0.12, p = 0.21, anxiety: β = -
0.13, SE = 0.10, p = 0.21, depression: β = - 0.08, SE = 0.07, p = 0.20) on youth outcomes. There
was, however, a positive main effect of cultural socialization on externalizing (β = 0.34, SE =

26
0.13, p = 0.01), anxiety (β = 0.29, SE = 0.14, p = 0.03), and depression (β = 0.19, SE = 0.09, p =
0.03) symptoms.
Moderating Effect of RES on Policing Index of Structural Racism. Cultural
socialization moderated the relationships between the policing index of structural racism and
youth anxiety (β = - 0.21, SE = 10, p = 0.04) and depression (β = - 0.14, SE = 0.07, p = 0.04)
symptoms but not externalizing (β = - 0.24, SE = 0.13, p = 0.06) symptoms. Upon probing the
interaction, results indicated that the associations between the policing index of structural racism
and youth anxiety, and depression symptoms were not significantly different at low (b = 3.07, SE
= 2.84, p = 0.28) or high (b = - 6.32, SE = 3.71, p = 0.09) levels of cultural socialization (see
Figure 5). Specifically, youth who lived in neighborhoods with high levels of policing
marginally reported more externalizing and internalizing symptoms when they received greater
cultural socialization messages compared to their peers who received fewer cultural socialization
messages. Youth who lived in neighborhoods with lower levels of policing reported comparable
levels of externalizing and internalizing symptoms, irrespective of whether they received low or
high levels of cultural socialization messages from their caregivers. Model fit was adequate (χ2
(14) = 17.19, p = 0.25; RMSEA = 0.08; CFI = 0.95; SRMR = 0.07).
It should be noted that when outliers were winsorized, there was also a moderating effect
of cultural socialization on the relationships between the policing index of structural racism and
youth anxiety (β = - 0.23, SE = 0.09, p = 0.01) and depression (β = - 0.14, SE = 0.06, p = 0.01),
as well as externalizing (β = - 0.28, SE = 0.11, p = 0.01) symptoms. However, simple slope
analyses revealed a slightly different pattern of results. Specifically, results indicated that the
associations between the policing index of structural racism and youth anxiety, depression, and
externalizing symptoms were significant at high levels (b = - 7.94, SE = 3.74, p = 0.03) of

27
cultural socialization but not at low levels (b = 2.69, SE = 2.99, p = 0.37) of cultural socialization
(see Figure 6 and Table E7). This means that youth who lived in neighborhoods with high levels
of policing reported more externalizing and internalizing symptoms when they received greater
cultural socialization messages compared to their peers who received fewer cultural socialization
messages. Youth who lived in neighborhoods with lower levels of policing reported comparable
levels of externalizing and internalizing symptoms, irrespective of whether they received low or
high levels of cultural socialization messages from their caregivers. Model fit was also adequate
(χ2
(14) = 17.20, p = 0.21; RMSEA = 0.09; CFI = 0.93; SRMR = 0.07).
There was not a moderating effect of preparation for bias (externalizing: β = 0.18, SE =
0.15, p = 0.25, anxiety: β = 0.15, SE = 0.12, p = 0.21, depression: β = 0.10, SE = 0.08, p = 0.24)
or minimization of racism (externalizing: β = - 0.19, SE = 0.16, p = 0.24, anxiety: β = - 0.16, SE
= 0.13, p = 0.22, depression: β = - 0.10, SE = 0.09, p = 0.23) on any youth outcomes. The
overall model fit was adequate (χ2
(14) = 17.19, p = 0.25; RMSEA = 0.08; CFI = 0.95; SRMR =
0.07).

28
Chapter Four: Discussion
The present study used an exploratory design to establish an understanding of the impact
of structural racism on Latine youths’ mental health and explore the moderating role of caregiver
RES messages. In line with the first aim of developing various iterations of a structural racism
index, an equally-weighted multidimensional index of structural racism and multiple domainspecific indices of structural racism were developed. However, we were unable to create a
differently-weighted, data-driven index of structural racism.
Regarding our second aim, results indicated that structural racism was not significantly
associated with youth externalizing or internalizing symptoms. Moreover, when testing our third
aim, caregiver RES messages had mixed effects on youth externalizing and internalizing
symptoms. Specifically, caregiver cultural socialization messages were consistently, positively
associated with youths’ externalizing and internalizing symptoms across all estimated models.
Additionally, caregiver preparation for bias messages were negatively associated with youth
externalizing and internalizing symptoms, but only for two of the estimated models. On the
contrary, caregiver minimization of racism messages were not significantly linked to youth
externalizing or internalizing symptoms.
Finally, results from our fourth aim indicated that caregiver preparation for bias messages
moderated the impact of the healthcare index of structural racism on youth externalizing and
internalizing symptoms, but this pattern did not hold for other types of RES messages nor other
indices of structural racism. Similarly, caregiver cultural socialization messages moderated the
effect of the policing index of structural racism on youth externalizing and internalizing
symptoms, but the same was not found for other types of RES messages nor other indices of

29
structural racism. Findings suggest that the moderating role of RES messages generalizes to
different forms of racism, including specific domains of structural racism.
Main Effects of Structural Racism Indices
Structural racism, captured via an equally-weighted multidimensional index and multiple
domain-specific indices, was not significantly associated with Latine youths’ externalizing,
anxiety, or depression symptoms. Further, the effect sizes of the extant findings were small. This
is inconsistent with prior work that has linked structural racism to poor mental health outcomes
(Das et al., 2021; Do et al., 2019).
The lack of association between the structural racism indices and youths’ mental health
outcomes may be explained by an ecological fallacy, or the assumption that ecological analyses
accurately represent the realities of the participants (Piantadosi et al., 1988). Although the
indicators of structural racism were linked to the participants’ addresses at the neighborhood
level, it is likely that the realities captured through the indicators are not the realities of the
participants in the present study. Indeed, researchers posit that neighborhood boundaries are
often defined based on objective data collected at the area level, which may differ from how
communities define or perceive neighborhood boundaries (Acevedo-Garcia et al., 2023;
Hardeman et al., 2022). Thus, when using objective measures at the neighborhood level, there is
a slight risk of capturing experiences that participants are not actually exposed to, which may
consequently impact how the objective measure of structural racism relates to youth-reported
mental health outcomes.
Relatedly, even when objective indicators of structural racism are the lived realities of
participants, their perception of structural racism may also be an important driver of how
structural racism impacts their well-being (Neblett & Neal, 2022). This is related to the relative

30
deprivation theory, or the perception or feeling of being deprived of something relative to others
(Crosby, 1976). Importantly, the relative deprivation theory posits that subjective perceptions or
feelings of deprivation relative to others may drive outcomes, such as poor mental health, beyond
people’s objective experiences (Bernstein & Crosby, 1979). Relative deprivation could manifest,
for example, as the feeling or perception of experiencing structural racism when one becomes
aware of their lack of available resources in comparison to others who may have a plethora of
resources available. This awareness can result in anticipatory stress, impacting one’s behavior
and psychological well-being and potentially exacerbating the effects of structural racism (Hope
et al., 2021; Neblett & Neal, 2022). Conversely, those who experience structural racism but are
not aware of these inequities may not experience the same level of distress associated with
relative deprivation. Future work is warranted to simultaneously capture both objective and
subjective reports of structural racism to develop a more comprehensive understanding of its
impact on Latine youths’ mental health.
Main Effects of Racial-Ethnic Socialization Practices
Across a couple of estimated models, preparation for bias was negatively associated with
youths’ externalizing, anxiety, and depression symptoms, such that as preparation for bias
messages increased, youths’ symptomatology decreased. This is consistent with prior work that
has linked preparation for bias with better psychological outcomes (Hughes et al., 2006; UmañaTaylor & Hill, 2020). On the contrary, caregiver cultural socialization was positively associated
with youths’ externalizing, anxiety, and depression symptoms for all estimated models, such that
as cultural socialization increased, youths’ symptomatology also increased. This is inconsistent
with prior work that has found cultural socialization to be associated with fewer externalizing

31
and internalizing problems among racial-ethnic minoritized youth (Hughes et al., 2006; UmañaTaylor & Hill, 2020; Wang et al., 2020).
Acculturation, or the coexistence of people from different cultural backgrounds in which
there is cultural retention (e.g., maintaining cultural values, practices, and beliefs) and cultural
acquisition (e.g., adopting new values, practices, and beliefs from the new predominant culture),
may be contributing to the unexpected finding that cultural socialization is associated with worse
mental health outcomes (Berry, 1997; Ryder et al., 2000). Researchers have theorized that there
are four categories to acculturation: (1) integration, (2) assimilation, (3) separation, and (4)
marginalization, with integration being considered the most adaptive as it involves cultural
retention and acquisition (Berry, 1997). Discrepancies between caregivers’ and youths’
acculturation levels have been shown to negatively impact youth well-being (Huq et al., 2015).
Given the high percentage of immigrant families in our sample, it is possible that high levels of
cultural socialization messages may actually be indicators of caregivers’ efforts to increase their
child’s retention of their Latine values and heritage and decrease assimilation to Anglo-American
culture. Thus, although cultural socialization is generally thought to be protective in the context
of immigrant families, it is possible that such messages may actually reflect parent-child
discrepancies in acculturation levels. Indeed, a study by Huq and colleagues (2015) found that
acculturation conflict between Latine adolescents and caregivers was associated with lower
racial-ethnic pride and more depressive symptoms. More nuanced information about the content
and timing of caregiver-facilitated cultural socialization is needed in future work to further
unpack its impact on youths’ well-being.
The present unexpected finding might also be a result of capturing each RES practice in
isolation rather than as interconnected practices, which researchers posit may be more

32
informative (Umaña-Taylor & Hill, 2020). For instance, several studies have found that
caregivers of color use a combination of different RES practices to talk with their children about
race and racism (Dunbar et al., 2015; Varner et al., 2018). More importantly, these distinct
profiles have been found to differentially predict youths’ mental health outcomes (Dunbar et al.,
2015; Varner et al., 2018). Therefore, it is likely that youth are receiving high cultural
socialization messaging coupled with other forms of RES messages (e.g., minimization of
racism) that may not be as helpful. Future studies may benefit from leveraging latent profile
analyses and latent class analyses to comprehensively capture caregivers’ RES practices and
their joint effect on youths’ well-being.
Finally, minimization of racism was not significantly associated with youths’
internalizing or externalizing outcomes. This is inconsistent with prior work that has found an
association between minimization of racism and youths’ mental health outcomes (Wang et al.,
2020). However, it is worth noting that research on the impact of minimization of racism is still
fairly limited, and thus, there is not yet a clear consensus of how it relates to youths’ well-being.
More work is warranted to understand the unique impact of minimization of racism messaging
among racial-ethnic minoritized youth.
Moderation Effects
Results indicated that preparation for bias moderated the association between the
healthcare index of structural racism and youths’ externalizing, anxiety, and depression
symptoms. Specifically, symptoms were highest when youth had low access to healthcare and
low levels of preparation for bias and lowest when youth had low access to healthcare and
received high preparation for bias messaging. Findings suggest that Latine youth may experience
heightened levels of anxiety, depression, and externalizing symptoms when they are exposed to

33
high levels of structural racism (as indicated by limited access to healthcare) but do not receive
the necessary support to cope with these inequities. In contrast, caregivers help to buffer youth
from the pernicious mental health effects of structural racism when they proactively engage
youth in conversations focused on raising their awareness of racism and building the necessary
coping tools. These findings are consistent with extant research demonstrating that preparation
for bias protects racial-ethnic minoritized youth from the detrimental consequences of racism
(Ayón, 2018; Hughes et al., 2006; Umaña-Taylor & Hill, 2020).
Present findings also indicated that cultural socialization moderated the relationship
between policing and youths’ externalizing, anxiety, and depression symptoms. Specifically,
symptomatology was highest at high levels of policing and high levels of cultural socialization
and lowest at high levels of policing and low levels of cultural socialization. This finding was
surprising, as cultural socialization has consistently been linked to fewer externalizing and
internalizing problems among racial-ethnically minoritized youth (Ayón, 2018; Hughes et al.,
2006; Umaña-Taylor & Hill, 2020; Wang et al., 2020). This unexpected outcome may once again
be explained by other RES messaging that youth may be simultaneously receiving, but that are
not being captured in the present study. For instance, prior work using latent profile analyses to
elucidate RES profiles has found that moderate levels of cultural socialization, when coupled
with high promotion of mistrust and preparation for bias, are associated with more depressive
symptoms. In contrast, moderate levels of all three types of messaging were linked to less
depressive symptoms (Dunbar et al., 2015). Similarly, Varner et al. (2018) found that youths’
mental health outcomes were better when they received high levels of positive RES messages
(e.g., racial-ethnic pride) or low levels of all types of RES messages in comparison with high
levels of negative RES messages (e.g., reinforcement of racial-ethnic stereotypes). This work

34
suggests that studying the independent effects of distinct RES practices in isolation may not
accurately capture the complete array of messages youth receive about race and racism, which
may, in turn, lead to inaccurate or incomplete findings (Umaña-Taylor & Hill, 2020). Thus, it is
critical that future RES work adopts profile-driven approaches to understanding the impacts of
distinct RES messages on youths’ mental well-being.
Consistent with the earlier argument regarding the unexpected negative effect of cultural
socialization on youths’ internalizing and externalizing outcomes, it is likely that caregiveradolescent acculturation conflict may also be informing this unanticipated negative impact of
cultural socialization on the relationship between the policing index of structural racism and
youths’ mental health outcomes. As previously explained, studies have found that when there are
acculturation differences between Latine youth and their caregivers, youth report lower levels of
racial-ethnic pride and more depressive symptoms (Huq et al., 2015). Thus, it is likely that if our
participant youths and their caregivers are adopting different acculturation styles, higher cultural
socialization messaging from caregivers may be driving the negative mental health consequences
found in the present study. Future studies may benefit from assessing the interacting effect of
cultural socialization and acculturation on Latine youths’ well-being.
Strengths, Limitations, and Future Directions
Growing evidence suggests that structural racism is a fundamental driver of health
disparities (Alvarez et al., 2022; Bailey et al., 2017; Dennis et al., 2021). Yet, very few studies
have explored the mental health consequences of structural racism. This work advances our
understanding of the consequences of structural racism by being one of the few studies exploring
how structural racism impacts the mental health outcomes of Latine youth. The present work is
further strengthened by its unique approach of exploring how different iterations of structural

35
racism are differently related to mental health outcomes. This is particularly informative given
that there is not an extant gold standard approach for measuring structural racism, and thus, there
is not a clear understanding of how and what indicators of structural racism impact mental health
outcomes. Relatedly, although structural racism has been theoretically conceptualized as a
system of interconnected issues, very few studies have assessed structural racism using a
multidimensional index (Brown & Homan, 2024; Chantarat et al., 2021; Dougherty et al., 2020).
Indeed, most prior work has relied on the use of single indicators to assess structural racism (e.g.,
over police surveillance and killings of Black people; Das et al., 2021). Thus, another strength of
this study is its consideration of the multidimensional nature of structural racism.
Further, most extant work on the health consequences of structural racism, to date, has
focused on adult Black populations (Brown & Homan, 2024; Chantarat et al., 2021; Dougherty
et al., 2020). Thus, prior findings may not translate to Latine individuals, as Latine communities
have a different historical context and, therefore, face unique structural inequities than those of
other racial-ethnic groups. Consequently, there is a pressing need to elucidate what indicators of
structural racism are most salient to Latine communities and better understand to what extent
structural racism impacts the mental well-being of Latine individuals. The present study
addresses this gap by assessing structural racism and its mental health consequences in relation
to Latine youth. Finally, despite extensive work on the buffering effects of caregiver-facilitated
RES messages (Hughes et al., 2006; Umaña-Taylor & Hill, 2020; Wang et al., 2020), no study to
our knowledge has explored the moderating role of RES in relation to structural racism.
Therefore, this work further advances our understanding of how RES practices uniquely
moderate different forms of racism.

36
Despite its strengths, this study has a few limitations that must be acknowledged. First,
the small sample size of the present study limits the generalizability of these findings and
warrants cautious interpretation. Specifically, this study had a small sample size compared to the
number of parameters in the estimated models (e.g., 10-20 observations are recommended per
parameter). Thus, we were likely statistically underpowered to detect medium-large effect sizes
and to elucidate any statistically significant patterns (Harrell, 2001). Relatedly, the small sample
size of this study increases the likelihood of Type II error, while the exploratory nature of the
study (e.g., seven different models were estimated) increases the probability of Type I error
(Johnson, 1999). Nonetheless, the purpose of the present study was to address some of the gaps
in the literature by exploring what might be the best strategies for capturing structural racism
experienced by Latine youth and further an understanding of how objective indicators of arealevel inequities may impact mental health outcomes. This study may serve as a steppingstone for
future work seeking to expand the structural racism literature to other non-Black-White
populations, such as Latine communities. Further, it advances our understanding of how
structural racism may be implicated in the mental health outcomes of racial-ethnically
minoritized youth and how cultural practices, such as RES, may moderate these effects. It is
critical that the present study is replicated with a larger sample size that adequately powers the
complexity of the proposed analyses.
Second, all participants were Latine, and thus, the findings may not generalize to other
racial-ethnic groups. However, it is important to highlight that Latine youth and families may
face unique structural inequities that differ from the experiences of other racial-ethnic
minoritized communities. Relatedly, prior research has found that caregivers’ experiences with
racism inform the frequency and type of RES messages they provide to their children (Hughes et

37
al., 2006; Umaña-Taylor & Hill, 2020). Therefore, the current study needed to focus on the
experiences of Latine families. However, this work sets the stage for future studies to develop
multidimensional indices of structural racism relevant to other racial-ethnic minoritized groups
and expand our understanding of how structural racism impacts the mental health of children and
adolescents.
Third, due to sample size, insufficient power, and inability to identify a clear, data-driven
pattern of how indicators should be differentially weighted, this study relied on equally-weighted
indices of structural racism created via simple averaging indicators into a composite. This is a
limitation because it assumes that all the indicators contribute equally to structural racism.
However, it is likely that certain indicators may have more salient associations with structural
racism in the real world and may differently impact mental health outcomes. For instance, a
study using latent class analyses found complex interactions between indicators of structural
racism, yielding three different classes of structural racism experienced by participants in the
study (Chantarat et al., 2021). In a different study using confirmatory factor analyses, researchers
were able to estimate the most reliable combination of indicators across domains of structural
racism, with some indicators having stronger factor loadings than others (Dougherty et al.,
2020). Thus, equal weighting structural racism indicators may have oversimplified the
complexity of structural racism, likely biasing the estimation index and heightening its
vulnerability to measurement error. Importantly, there is a possibility that the present study
missed critical nuance that may differently impact how structural racism is associated with
Latine youths’ mental health outcomes. Future work aiming to develop a data-driven
multidimensional index of structural racism may benefit from a greater number of observations
to estimate more robust models, such as latent class analyses and confirmatory factor analyses.

38
These models can better account for nuanced intercorrelations across indicators, which is better
aligned with the multifaceted nature of structural racism (Brown & Homan, 2024; Chantarat et
al., 2021; Hardeman et al., 2022). Further, these models can better capture underlying latent
constructs without relying on arbitrary correlation thresholds. This may help elucidate constructs
that would not otherwise be detected through bivariate correlations and simple averaging.
Fourth, this study conceptualized structural racism based on exposure to resources (e.g.,
healthcare) and risks (e.g., over-policing), but it did not assess differential exposure to these
resources and risks between Latine and White communities. This is in contrast with how most
other studies have conceptualized structural racism in the past (Groos et al., 2018). Specifically,
most of the structural racism literature to date has focused on Black-White segregation and
unequal allocation of resources between Black and White communities (Groos et al., 2018). It is
likely that the effect of structural racism on mental health outcomes may be more robust when
assessing differences in the allocation of resources and mental health outcomes by racial-ethnic
groups. This means that the interaction between racial-ethnic background and social
determinants of health (e.g., food and housing insecurity) may be what drives the mental health
effects of structural racism (Groos et al., 2018). Therefore, it is important that future work
assesses differential exposure to resources and risks between Latine and White communities, as
this may further illustrate structural racism and provide more information to help us identify
targets for intervention and policy changes. It is important to note, however, that the present
study is one of the few to examine structural racism in relation to Latine youth, setting up the
stage for future work to continue elucidating what indicators of structural racism are most salient
to Latine communities and how their exposure to structural racism compares in relation to other
racial-ethnic groups.

39
Fifth, given our reliance on objective indicators of structural racism collected at the
neighborhood level, this study is limited in the inferences that it can make due to the modifiable
areal unit (MAUP; i.e., variability in results due to sensitivity to the geographic unit scale) and
the uncertain geographic context problems (UGCoP; i.e., variability in results due to how
geographic units are defined and deviate from how communities define them; Kwan, 2012).
These problems highlight that geographic boundaries can often be misspecified and incorrectly
defined, increasing the likelihood of Type I error (e.g., by chance or by incorrectly defining the
geographic unit in a manner that is related to the individual-level outcomes) and Type II error
(e.g., by misspecifying geographic units, by mismatching the timing between participants’
exposure to geographic units and development of individual-level outcomes, and by misusing the
geographic unit) (Kwan, 2012). Thus, future studies may benefit from conducting sensitivity
analyses by assessing the impact of structural racism at different geographic units (e.g.,
neighborhood, county, and state). Most importantly, the MAUP and UGCoP problems
underscore the fact that geographic units are not always defined by how the population defines
them, and as a result, objective indicators of structural racism may not always fully capture
people’s lived realities. Therefore, it is also important that future studies capture subjective
perceptions of structural racism to assess if there are any discrepancies between objective and
subjective reports and examine if differences between objective and subjective reports of
structural racism differentially inform mental health outcomes.
Sixth, the present study assessed RES practices (e.g., cultural socialization, preparation
for bias, and minimization of racism) separately. However, recent work has found that assessing
profiles of RES practices in combination rather than isolation may provide more insight into
when and how different RES messages may be protective or unhelpful (Umaña-Taylor & Hill,

40
2020). Although the limited sample size precluded the utilization of person-centered analyses to
identify distinct RES profiles, future work may benefit from adopting such an approach, which
may yield more nuanced insights into the direct and interactive effects of RES on youth mental
health.
Seventh, caregiver RES practices were assessed using caregiver self-report, which
captures the frequency and content of caregivers’ messaging but overlooks more implicit features
of how caregivers socialize their children with respect to race and racism. For instance,
observational assessments of RES messages may allow us to capture accompanied behaviors
such as affect and other nonverbal behaviors that may influence how youth perceive or
understand RES messages (Yasui, 2015). This is important because prior studies have found that
the association between RES messages and mental well-being is contingent on the context, such
as the adolescent-caregiver relationship and family and neighborhood composition (UmañaTaylor & Hill, 2020). Therefore, it is essential that future work assesses whether the moderating
role of preparation for bias, cultural socialization, and minimization of racism varies based on the
content and context of these messages.
Lastly, due to insufficient sample size, the present study did not test the association
between caregivers’ immigrant generation status and the RES messages they provide. This is
important as prior work has linked immigration status to the type and frequency of RES
messages caregivers communicate to their children (Hughes et al., 2006). Specifically,
researchers have found that recent immigrants are more likely to discuss racism than immigrants
from the same racial-ethnic group who have been in the U.S. for longer (Hughes et al., 2006;
Knight et al., 1993; Umaña-Taylor & Fine, 2004). Thus, there may be generational immigrant
status differences that might be important to assess to further contextualize the present findings.

41
Future studies may benefit from accounting for these differences or from assessing group
differences.
Conclusion
This study provides an important foundation for understanding structural racism and its
mental health consequences on Latine youth. Findings suggest that robust, differentiallyweighted measures of structural racism may be necessary to capture how structural racism
impacts Latine youths’ mental health. Results also point to the need to capture subjective
perceptions of structural racism, which may better complement and contextualize more objective
measures obtained via geocoded residential addresses. Notably, the extant results underscore the
importance of conducting sensitivity analyses by assessing how different iterations of structural
racism may be linked to mental health outcomes, as there is an extant lack of tools to measure
structural racism objectively. Given that structural racism is a key driver of health disparities,
continuous effort, time, and resources must be invested into the development of tools that
adequately capture it and that accurately predict mental health disparities and outcomes. This is
particularly important to inform equity-driven policies and culturally- and contextually-informed
mental health interventions.
In addition to exploring the mental health consequences of structural racism, the present
study was, to our knowledge, the first to assess the buffering effects of culturally-salient
practices, such as RES, in relation to structural racism. Findings indicate that RES messages,
such as preparation for bias and cultural socialization, are indeed moderators of the impact of
structural racism on youths’ mental health. These results represent an advancement in the
literature compared to other studies that have only assessed RES in relation to interpersonal
forms of racism. Specifically, the present findings suggest that the benefits of RES practices are

42
generalizable to different experiences of racism. Most importantly, these results highlight the
unrelenting resilience of racial-ethnic minoritized communities and further underscores that as
we work toward dismantling structural racism, we should simultaneously work on advancing our
understanding of how communities’ assets may be leveraged to inform culturally-congruent care.

43 1. Externalizing Symptoms 1.00 2. Anxiety Symptoms 0.53** 1.00 3. Depression Symptoms 0.53** 0.50** 1.00 -1 -0.5 0 0.5 1 4. Preparation for Bias 0.01 -0.09 -0.12 1.00 5. Cultural Socialization 0.26 0.30 0.25 0.54** 1.00 6. Minimization of Racism -0.02 -0.12 -0.15 -0.12 0.11 1.00 7. Chronic Absenteeism (R) 0.25 0.09 0.16 0.40** 0.18 -0.27 1.00 8. Less than High School (R) 0.04 0.20 -0.08 0.24 0.04 -0.34* 0.47** 1.00 9. Associate's Degree -0.03 0.16 -0.16 0.08 0.09 -0.10 -0.01 0.71** 1.00 10. Bachelor's Degree 0.17 0.16 0.03 0.26 0.03 -.42** 0.66** 0.90** 0.51** 1.00 11. College Enrollment 0.22 0.14 0.04 0.30* 0.25 -0.17 0.33* 0.29 0.13 0.34* 1.00 12. Number of Hospitals 0.16 -0.23 0.23 0.14 0.12 -0.03 0.07 -0.27 -0.20 -0.13 -0.11 1.00 13. Uninsured Rates (R) 0.04 0.16 -0.22 0.19 0.07 -0.25 0.29 0.90** 0.68** 0.79** 0.34* -0.23 1.00 14. Low Access to Food (R) 0.25 0.13 0.17 0.20 0.13 -0.04 0.16 -0.23 -0.38* -0.05 -0.10 0.01 -0.22 1.00 15. FRPL -0.14 -0.08 0.04 -0.20 -0.03 0.27 -0.74** -0.74** -0.22 -0.86** -0.23 0.04 -0.72** 0.03 1.00 16. Places Accepting SNAP 0.18 -0.11 0.38* -0.15 -0.09 0.13 -0.20 -0.58** -0.28 -0.38* -0.22 0.62** -0.66** 0.08 0.45** 1.00 17. Housing Stability -0.20 -0.02 -0.09 -0.26 -0.22 0.16 -0.49** -0.34* -0.10 -0.51** -0.54** 0.06 -0.35* -0.14 0.34* 0.15 1.00 18. Rent Burden (R) 0.22 0.29 -0.04 0.14 0.17 0.01 0.42** 0.41** 0.09 0.34* 0.06 -0.05 0.41** -0.05 -0.62** -0.50** 0.06 1.00 19. Homeownership 0.00 0.21 -0.29 -0.02 0.03 0.01 -0.01 0.47** 0.37* 0.32* 0.10 -0.27 0.61** -0.37* -0.47** -0.59** 0.31* 0.65** 1.00 20. Overcrowding (R) -0.05 0.18 -0.15 0 1. 4 0.00 -0.23 0.29 0.92** 0.68** 0.74** 0.22 -0.38* 0.89** -0.26 -0.64** -0.73** -0.21 0.43** 0.59** 1.00 21. Medium Income 0.13 0.21 -0.17 0.12 0.01 -0.18 0.46** 0.71** 0.40** 0.73** 0.15 -0.12 0.72** -0.16 -0.86** -0.46** 0.03 0.66** 0.78** 0.67** 1.00 22. Youth Opportunity (R) 0.33* 0.05 0.09 0.34* 0.29 -0.18 0.42** 0.23 0.04 0.37* 0.83** -0.01 0.24 0.05 -0.23 -0.13 -0.53** 0.04 0.06 0.17 0.17 1.00 23. Labor Force 0.08 0.04 0.03 0.12 -0.05 -0.12 0.46** 0.28 0.12 0.37* -0.26 0.07 -0.01 0.10 -0.50** 0.16 0.21 0.26 0.10 0.08 0.49** -0.15 1.00 24. Unemployment (R) 0.06 0.20 -0.01 0.03 -0.15 -0.10 0.23 0.37* 0.30 0.31* -0.24 -0.03 0.19 -0.04 -0.30 -0.13 0.47** 0.42** 0.44** 0.32* 0.60** -0.12 0.53** 1.00 25. Poverty (R) 0.08 0.27 -0.14 0.10 0.02 -0.12 0.34* 0.72** 0.54** 0.59** 0.03 -0.17 0.64** -0.31* -0.66** -0.47** 0.24 0.69** 0.82** 0.72** 0.89** 0.04 0.49** 0.75** 1.00 26. Total Rate of Stops (R) 0.04 0.11 0.01 0.28 0.12 -0.44** 0.41** 0.70** 0.34* 0.69** 0.37* -0.16 0.66** -0.36* -0.61** -0.46** -0.18 0.57** 0.52** 0.63** 0.57** 0.35* 0.18 0.12 0.61** 1.00 27. Latine Stops (R) 0.36* 0.36* -0.05 -0.01 0.15 0.11 0.21 0.27 0.17 0.28 0.06 -0.03 0.40** 0.04 -0.43** -0.32* 0.05 0.64** 0.61** 0.29 0.53** 0.04 0.05 0.43** 0.55** -0.15 1.00 28. Total Rate of Arrests (R) -0.17 0.09 -0.04 0.05 -0.03 -0.41** 0.05 0.50** 0.28 0.47** 0.13 -0.07 0.45** -0.43** -0.44** -0.25 0.24 0.49** 0.73** 0.46** 0.62** 0.03 0.32* 0.23 0.67** 0.72** -0.19 1.00 29. Latine Arrests (R) 0.36* 0.36* -0.05 -0.01 0.15 0.11 0.21 0.27 0.17 0.28 0.06 -0.03 0.40** 0.04 -0.43** -0.32* 0.05 0.64** 0.61** 0.29 0.53** 0.04 0.05 0.43** 0.55** -0.16 1.00** -0.21 1.00 30. Violent Crime (R) 0.12 0.15 0.12 0.21 0.13 -0.26 0.46** 0.41** 0.12 0.49** 0.24 0.06 0.32* -0.37* -0.56** -0.06 -0.06 0.66** 0.45** 0.32* 0.56** 0.24 0.34* 0.18 0.59** 0.72** -0.49** 0.61** -0.53** 1.00 31. Property Crime (R) -0.04 0.14 0.11 -0.12 -0.10 0.05 -0.20 -0.22 -0.23 -0.34* -0.18 0.10 -0.26 -0.22 0.13 0.19 0.71** 0.24 0.45** -0.13 0.15 -0.26 0.21 0.28 0.34* 0.10 -0.46** 0.51** -0.45** 0.45** 1.00 Mean 46.23 48.19 49.67 2.57 44.65 1.70 -19.57 -35.57 5.36 20.03 45.41 0.30 -12.83 -505.62 87.16 36.18 91.64 -58.38 34.54 -20.22 58211.80 -13.26 64.30 -8.77 -21.78 -233.58 -52.26 -23.01 -54.14 -6.65 -18.18 SD 8.09 10.14 13.96 1.15 10.97 0.90 7.10 14.59 1.64 14.94 13.71 0.64 4.72 1168.17 13.69 24.81 4.20 5.45 15.95 9.80 19373.90 2.81 3.73 2.20 8.52 95.51 19.62 8.24 20.09 2.75 8.83 N 43 43 43 44 43 44 43 44 44 44 44 44 44 44 43 44 44 44 44 44 44 44 44 44 44 40 40 40 40 40 40 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Pearson's r * p < 0.05 ** p < 0.01 Note. (R) indicates that variables were reversed coded by multiplying by -1 so that low scores always indicate worse outcomes, and high scores always indicate better outcomes. FRPL = Free and Reduced-Price Lunch SNAP = Supplemental Nutrition Assistance Program SD = Standard Deviation. N = Number of participants. Table 1 Correlations and Descriptives for All Variables
Tables

44
Table 2
Fit Statistics Across Three Moderation Models
Model
Panel A: Food Insecurity Index
Fit Statistic
Model 1
(Simultaneous Model)
Model 2
(PB)
Model 3
(CS)
Model 4
(MR)
RMSEA 0.12 0.15 0.13 0.16
SRMR 0.07 0.11 0.09 0.10
CFI 0.86 0.72 0.81 0.72
χ
2M 22.75 35.59 29.90 35.71
df 14 18 18 18
p 0.06 0.01 0.04 0.01
Panel B: Housing Index
RMSEA 0.16 0.19 0.16 0.18
SRMR 0.09 0.11 0.10 0.11
CFI 0.80 0.63 0.75 0.66
χ
2M 29.01 45.62 36.34 42.88
df 14 18 18 18
p 0.01 < 0.001 0.01 0.001
Panel C: Employment & Income Index
RMSEA 0.13 0.15 0.12 0.15
SRMR 0.10 0.11 0.09 0.11
CFI 0.85 0.73 0.82 0.74
χ
2M 23.55 35.25 29.41 34.68
df 14 18 18 18
p 0.052 0.01 0.04 0.01
Note. Model 1 refers to the moderation model that accounted for all moderators and interaction
terms. Models 2-4 refer to models where additional moderators and interaction terms were
constrained to zero.
PB = Preparation for Bias; CS = Cultural Socialization; MR = Minimization of Racism; RMSEA
= Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square
Residual; CFI = Comparative Fit Index; χ
2M = Chi-Square Test of Model Fit; df = Degrees of
Freedom.

45
Figures
Figure 1
Conceptual Model of Estimated Regression and Moderation Paths
Note. Structural racism indicates an equally-weighted multidimensional index and domain-specific indices.
All models accounted for youths’ age and biological sex.

46
Figure 2
Distribution of Families Across Los Angeles County Neighborhoods
Note. Addresses were mapped using latitudinal and longitudinal data.

47
Figure 3
Interaction of Healthcare Index of Structural Racism and Preparation for Bias with Outliers
Note. Outliers were not modified.
Graphed interaction was not significant.

48
Figure 4
Interaction of Healthcare Index of Structural Racism and Preparation for Bias with Outliers Winsorized
Winsorized
Note. * p < 0.05
Outliers were winsorized.

49
Figure 5
Interaction of Policing Index of Structural Racism and Cultural Socialization with Outliers
Note. Outliers were not modified.
Graphed interaction was not significant.

50
Figure 6
Interaction of Policing Index of Structural Racism and Preparation for Bias with Outliers Winsorized
Note. * p < 0.05
Outliers were winsorized.

51
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57
Appendix A
Example of Demographic Questionnaire Completed by Adolescents
Date: ____ ____/____ ____/____ ____ ____ ____
Youth - Demographic Survey
1. How old are you (in years)? ____________________
2. What sex were you assigned at birth, on your original birth certificate? (select one)
◯ Male
◯ Female
◯ Intersex
◯ Something not listed: __________________
3. How do you currently describe your gender? (please select all that apply)
☐ Agender
☐ Genderqueer or genderfluid
☐ Boy
☐ Non-binary
☐ Questioning or unsure
☐ Two-spirit
☐ Girl
☐ Something not listed: ____________________
4. Do you identify as being transgender?
◯ Yes
◯ No
5. How would you describe your sexual orientation? (please select all that apply)
☐ Asexual
☐ Bisexual
☐ Gay or lesbian
☐ Heterosexual or straight
☐ Mostly heterosexual or straight
☐ Queer
☐ Not sure
☐ Something not listed: _________________
6. Do you speak any language(s) other than English at home? (select one)
◯ No
◯ Yes

58
If yes, what other languages do you speak at home?: ____________________
7. What grade are you in in school? (Note: If not in school, record the grade you are going
into). (select one)
◯ Kindergarten
◯ 1
st grade
◯ 2
nd grade
◯ 3
rd grade
◯ 4
th grade
◯ 5
th grade
◯ 6
th grade
◯ 7
th grade
◯ 8
th grade
◯ 9
th grade
◯ 10th grade
◯ 11th grade
◯ 12th grade
◯ College
◯ Something not listed: ________________
8. What is the name of your school?:
_____________________________________________________________________
9. How many schools have you attended since Kindergarten (including
Kindergarten)?:_________________
10. What grades do you typically get in school?
◯ Mostly A’s
◯ A’s and B’s
◯ B’s and C’s
◯ C’s and D’s
◯ Mostly F’s
◯ I do not receive letter grades: _________________
If child doesn’t receive letter grades:
10a. What grading system does your school use? [note to examiner: open-ended response to
collect as much information re. grading system as possible!]
10b. What specific scores do you typically receive? [note to examiner: open-ended response to
collect as much information re. grading system as possible!]

59
11. What is the highest grade or year of school that any of your guardians (parents or
caregivers) completed? (select one)
◯ No formal schooling
◯ 7
th grade or less
◯ Junior high completed
◯ Partial high school (at least one year)
◯ High school diploma
◯ GED or alternative credential
◯ Partial college (at least one year), no degree
◯ Specialized training
◯ Junior college/Associate’s degree (for example: AA, AS)
◯ Bachelor’s degree (for example: BA, BS)
◯ Master’s degree (for example: MA, MS, MEng, MEd, MSW, MBA)
◯ Professional degree beyond a Bachelor’s degree (for example: MD, DDS, DVM,
LLB, JD)
◯ Doctoral degree (for example: PhD, EdD)
12. Were you born outside of the United States?
◯ Yes, I was born outside of the United States
◯ No, I was born in the United States
13. Were any of your parents/guardians born outside of the United States? (select one)
◯ Do not know
◯ Yes, they were all born outside of the United States
◯ Yes, one of them was born outside of the United States
◯ No, they were all born in the United States
14. Where do you live now? That is, where do you stay most often?
◯ With your parent(s)
◯ With other relative(s)
◯ With friend(s)
◯ With romantic partner
◯ On my own
◯ Somewhere else: __________________
15. How many times have you and your family moved since you were born?:
____________________
16. a. How many people, including yourself, live in your home?: ___________________
b. How many of these are children? (under the age of 18): ______________________
c. How many of these are adults? (18 years and older): ________________________
17. In your home, is there enough money to buy food?

60
◯ Never
◯ Almost Never
◯ Sometimes
◯ Often
◯ Always
18. In your home, is there enough money to buy gasoline for the car or truck or take the
bus?
◯ Never
◯ Almost Never
◯ Sometimes
◯ Often
◯ Always
19. In your home, is there enough money to pay utilities (light, water, etc.)?
◯ Never
◯ Almost Never
◯ Sometimes
◯ Often
◯ Always
20. In your home, is there enough money to pay for school expenses (notebooks, textbooks,
computer)?
◯ Never
◯ Almost Never
◯ Sometimes
◯ Often
◯ Always
21. In your home, is there enough money to buy clothing you need?
◯ Never
◯ Almost Never
◯ Sometimes
◯ Often
◯ Always
22. In your home, is there enough money to buy clothing you want to have?
◯ Never
◯ Almost Never
◯ Sometimes
◯ Often
◯ Always

61
23. In your home, is there enough money to do fun things (take vacations, go to the movies,
go out)?
◯ Never
◯ Almost Never
◯ Sometimes
◯ Often
◯ Always
24. a. Do you have religious or spiritual beliefs?
◯ Yes
◯ No
If no, skip to the end of the demographic questionnaire.
If yes….
24b. How would you describe your religious or spiritual orientation?
◯ Christian (e.g., Catholic, Protestant, Mormon, Jehovah's Witness, Baptist ...)
◯ Eastern (e.g., Buddhist, Hindu ...)
◯ Jewish
◯ Muslim
◯ Other organized religion (specify):
◯ Personal spiritual (unorganized) (specify):
24c. How important are these beliefs in your life?
◯ Very important
◯ Important
◯ Somewhat important
◯ Slightly important
◯ Not at all important
24d. In general, how often do you practice your religion or spirituality? For example,
attending services, individual prayers, meditation, inspirational readings, or Bible study?
◯ Daily
◯ Several times a week
◯ Weekly
◯ Less than weekly
◯ Holidays
◯ Not at all
24e. How happy are you on a scale from 1 to 10, where 10 means you are a very happy
person and 1 means that you are not at all happy?
1 2 3 4 5 6 7 8 9 10
Not at all happy Very happy

62
Appendix B
Example of Demographic Questionnaire Completed by Caregivers
Date: ____ ____/____ ____/____ ____ ____ ____
Parent – Demographic Survey
1. Are you the participating child's biological parent?
◯ Yes
◯ No
If no, what is your relationship to the participating child?: ____________________
2. What sex were you assigned at birth, on your original birth certificate? (select one)
◯ Male
◯ Female
◯ Intersex
◯ Something not listed: __________________
3. How do you currently describe your gender? (please select all that apply)
☐ Agender
☐ Genderqueer or genderfluid
☐ Man
☐ Non-binary
☐ Questioning or unsure
☐ Two-spirit
☐ Woman
☐ Something not listed: ____________________
4. Do you identify as being transgender?
◯ Yes
◯ No
5. How would you describe your sexual orientation? (please select all that apply)
☐ Asexual
☐ Bisexual
☐ Gay or lesbian
☐ Heterosexual or straight
☐ Mostly heterosexual or straight
☐ Queer
☐ Not sure
☐ Something not listed: _________________
6. What do YOU identify as your race? (please select all that apply)

63
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________
7. What do YOU identify as your ethnicity (e.g., Mexican, Puerto Rican, Cuban,
Guatemalan, African American)?:
________________________________________________________________________
8. How would you describe the race of your child’s biological mother? (please select all
that apply)
Skip If PC is bio mom.
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________
9. How would you describe the race of your child’s biological father? (please select all that
apply)
Skip If PC is bio dad.
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________
10. How old are you (in years)? ____________________
11. What is the last level of formal education you completed? (select one)
◯ No formal schooling
◯ 7
th grade or less

64
◯ Junior high completed
◯ Partial high school (at least one year)
◯ High school diploma
◯ GED or alternative credential
◯ Partial college (at least one year), no degree
◯ Specialized training
◯ Junior college/Associates degree (for example: AA, AS)
◯ Bachelor’s degree (for example: BA, BS)
◯ Master’s degree (for example: MA, MS, MEng, MEd, MSW, MBA)
◯ Professional degree beyond a bachelor’s degree (for example: MD, DDS, DVM, LLB,
JD)
◯ Doctorate degree (for example: PhD, EdD)
12. What is the last level of formal education your child’s biological mother completed?
(select one)
Skip If PC is bio mom.
◯ No formal schooling
◯ 7
th grade or less
◯ Junior high completed
◯ Partial high school (at least one year)
◯ High school diploma
◯ GED or alternative credential
◯ Partial college (at least one year), no degree
◯ Specialized training
◯ Junior college/Associates degree (for example: AA, AS)
◯ Bachelor’s degree (for example: BA, BS)
◯ Master’s degree (for example: MA, MS, MEng, MEd, MSW, MBA)
◯ Professional degree beyond a bachelor’s degree (for example: MD, DDS, DVM, LLB,
JD)
◯ Doctorate degree (for example: PhD, EdD)
13. What is the last level of formal education your child’s biological father completed?
(select one)
Skip If PC is bio dad.
◯ No formal schooling
◯ 7
th grade or less
◯ Junior high completed
◯ Partial high school (at least one year)
◯ High school diploma
◯ GED or alternative credential
◯ Partial college (at least one year), no degree

65
◯ Specialized training
◯ Junior college/Associates degree (for example: AA, AS)
◯ Bachelor’s degree (for example: BA, BS)
◯ Master’s degree (for example: MA, MS, MEng, MEd, MSW, MBA)
◯ Professional degree beyond a bachelor’s degree (for example: MD, DDS, DVM, LLB,
JD)
◯ Doctorate degree (for example: PhD, EdD)
14. Did your child immigrate to the United States?
◯ No, they were born in the United States
◯ Yes, they immigrated to the United States
If yes, from where?: __________________________
If yes, in what year?: __________________________
15. Did you immigrate to the United States?
◯ No, I was born in the United States
◯ Yes, I immigrated to the United States
If yes, from where?: __________________________
If yes, in what year?: __________________________
16. Did your child’s biological mother immigrate to the United States?
Skip If PC is bio mom.
◯ No, she was born in the United States
◯ Yes, she immigrated to the United States
If yes, from where?: __________________________
If yes, in what year?: __________________________
17. Did your child’s biological father immigrate to the United States?
Skip If PC is bio dad.
◯ No, he was born in the United States
◯ Yes, he immigrated to the United States
If yes, from where?: __________________________
If yes, in what year?: __________________________
18. Where was your biological mother born?
◯ She was born in the United States
◯ She was born in another country
If born in another country: Did she immigrate to the United States?
◯ Yes
◯ No
19. Where was your biological father born?
◯ He was born in the United States

66
◯ He was born in another country
If born in another country: Did he immigrate to the United States?
◯ Yes
◯ No
20. Skip if PC is bio mom - Where was your child’s maternal grandmother born?
◯ She was born in the United States
◯ She was born in another country
If born in another country: Did she immigrate to the United States?
◯ Yes
◯ No
21. Skip if PC is bio mom - Where was your child’s maternal grandfather born?
◯ He was born in the United States
◯ He was born in another country
If born in another country: Did he immigrate to the United States?
◯ Yes
◯ No
22. Skip if PC is bio dad - Where was your child’s paternal grandmother born?
◯ She was born in the United States
◯ She was born in another country
If born in another country: Did she immigrate to the United States?
◯ Yes
◯ No
23. Skip if PC is bio dad - Where was your child’s paternal grandfather born?
◯ He was born in the United States
◯ He was born in another country
If born in another country: Did he immigrate to the United States?
◯ Yes
◯ No
24. What is your current legal marital status? (select one)
◯ Single – never married
◯ Currently married (and not separated)
◯ Living with partner
◯ Separated
◯ Divorced
◯ Widowed
If “currently married”:
17a. How long have you been married? You can report in months or years, whichever is
easier for you.

67
Months: ___________
Years:_________
If “living with partner”:
17b. How long have you been living with your current partner? You can report in months
or years, whichever is easier for you.
Months: ___________
Years:_________
If “living with partner” or “separated”:
17c. Have you ever been married?
◯ No
◯ Yes
If “married,” “divorced,” or “widowed” is endorsed, or if 17c is “yes”:
17d. Have you ever been divorced?
◯ Yes, once
◯ Yes, more than once
◯ No
25. Do you have a romantic partner?
◯ Yes
◯ No
If no, skip to 19.
If yes to 18:
18a. is s/he your child’s biological parent?
◯ No
◯ Yes
18b. How long have you and your romantic partner been together? You can report in
months or years, whichever is easier for you.
Months: ___________
Years:_________
18c. Which of the following best describes your living situation with your current
romantic partner?
◯ I do not live with my romantic partner
◯ My romantic partner sometimes stays with me, but we don’t live together full-time
◯ I live with my romantic partner but maintain financial independence
◯ I live with my romantic partner and share expenses
26. What is your household income including child support and any other financial aid before
taxes? You can report monthly or yearly, whichever is easiest for you.

68
a. Monthly: ____ ____ ____ , ____ ____ ____
1. Yearly: ____ ____ ____ , ____ ____ ____
27. Do you receive any of the following? (please select all that apply)
☐ Supplemental Nutrition Assistance Program (SNAP) or CalFresh
☐ Special Supplemental Nutrition Program for Women, Infants, and Children (WIC)
☐ Temporary Assistance for Needy Families (TANF), including Pass through Child
Support
☐ Medical assistance
☐ Heating & Electric bill assistance – e.g., Low-Income Home Energy Assistance
Program (LIHEAP)
☐ Social Security Income (disability or death benefits)
☐ Child support for [TC]
☐ Child support for other children (if applicable)
☐ Spousal support/alimony
☐ Something else: _____________________________
☐ None
28. Last week, did you work at a job for pay, even for one hour? Include jobs like babysitting
or pickup work, and temporary jobs, as well as regular, steady jobs.
◯ No (skip to 21a) ◯ Yes (skip to 21d)
If no to 21:
21a. Below is a list of reasons for which people don’t or can’t work for pay. Please tell
me the main reason you were not working for pay last week. (select one)
◯ Temporarily away from a job I have
◯ Hired for new job and waiting to start
◯ Have been actively looking for work
◯ Illness or disability prevents me from working
◯ No reliable transportation
◯ Cannot arrange childcare
◯ No jobs available
◯ Recently laid off
◯ Would lose welfare benefits or medical coverage
◯ Was going to school
◯ Taking care of home or family
◯ Retired
◯ Incarcerated/in jail
◯ Something else:______________
21b. Were you employed in the last 12 months?
◯ No (skip to 22) ◯ Yes (go to 21c)

69
21c. What was your primary occupation? ________________________
If yes to 21:
21d. What is your primary occupation? ______________________________
21e. How many hours per week do you usually work? _______________________
29. Do you have religious or spiritual beliefs?
◯ Yes
◯ No
If no, skip to 23.
If yes, to 22….
22a. How would you describe your religious or spiritual orientation?
◯ Christian (e.g. Catholic, Protestant, Mormon, Jehovah’s Witness, Baptist …)
◯ Eastern (e.g., Buddhist, Hindu ...)
◯ Jewish
◯ Muslim
◯ Other organized religion (specify):
◯ Personal spiritual (unorganized) (specify):
22c. How important are these beliefs in your life?
◯ Very important
◯ Important
◯ Somewhat important
◯ Slightly important
◯ Not at all important
22d. In general, how often do you practice your religion or spirituality? For example,
attending services, individual prayers, meditation, inspirational readings, or Bible study?
◯ Daily
◯ Several times a week
◯ Weekly
◯ Less than weekly
◯ Holidays
◯ Not at all
30. What is your current type of housing (mark only one)?
◯ Apartment/duplex/townhouse
◯ Single family home
◯ Mobile home
◯ Motel/hotel
◯ Mission, emergency housing, group shelter, camping

70
◯ Homeless
◯ Something else: _________________________
31. a. How many people, including yourself, live in your home?: __________
b. How many of these are children? (under the age of 18): _________________
c. How many of these are adults? (18 years and older): _________________
Next, list all children under the age of 18 living in your child’s
home starting with the target child [TC]. RECORD ONLY FIRST AND LAST INITIAL.
Child’s Name Age
1. _______________
[TC]
FI LI Age Date of Birth

Check here if more than 5 children living in the home
32. Now list all of the adults living in your child’s home, starting with you if you live
together. For all adults other than the primary caregiver [PC], RECORD ONLY THE
FIRST AND LAST INITIAL.
Check here if more than 5 adults living in the home

71
33. Does the participating child have any siblings living outside the home?
◯ No
◯ Yes
If no, skip to question 27.
34. Next, list all of the participating child's siblings living outside the home. ONLY
RECORD FIRST AND LAST INITIAL.
35. How often does your child stay with you?
◯ This child lives with me full time
◯ This child lives with me part-time
If yes: How many days per week does the child live with you, on average?
If yes: Who does the child live with the rest of the time?
◯ This child does not live with me
If yes: Who does the child live with?
Child’s Education
1. What type of school does your child attend? (select one)
◯ Public
◯ Private
◯ Home Schooled
◯ Something else:_________________
2. What is the name of your child’s school?:
______________________________________________________________________
3. At school, what grade is your child in? (Note: If not in school, record the grade they are
going into).
◯ Kindergarten ◯ 5
th grade ◯ 10th grade
◯ 1
st grade ◯ 6
th grade ◯ 11th grade
◯ 2
nd grade ◯ 7
th grade ◯ 12th grade

72
◯ 3
rd grade ◯ 8
th grade ◯ Something not listed:
__________
◯ 4
th grade ◯ 9
th grade
4. Has your child ever repeated a grade?
◯ Yes
◯ No
If yes, which grade?: ___________________________
5. What grades does your child typically get in school?
◯ Mostly A’s
◯ A’s and B’s
◯ B’s and C’s
◯ C’s and D’s
◯ Mostly F’s
◯ My child doesn’t receive letter grades
If child doesn’t receive letter grades:
6a. What grading system does your child’s school use? [note to examiner: open-ended response
to collect as much information re. grading system as possible!]
6b. What specific scores does your child typically receive? [note to examiner: open-ended
response to collect as much information re. grading system as possible!]
Community Demographics
1. What race are most of your friends? (please select all that apply)
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________
2. What race are most of your child’s friends? (please select all that apply)
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)

73
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________
3. What race are most of your child’s classmates? (please select all that apply)
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________
4. What race(s) is/are most of your child’s teacher(s)? (please select all that apply)
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________
5. What race are most of your neighbors? (please select all that apply)
☐ American Indian or Alaska Native (Navajo Nation, Blackfeet Tribe, Mayan, etc.)
☐ Asian (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese, etc.)
☐ Black or African American (Jamaican, Nigerian, Ethiopian, etc.)
☐ Hispanic, Latinx, or Spanish Origin (Mexican, Puerto Rican, Cuban, etc.)
☐ Middle Eastern or North African (Lebanese, Iranian, Egyptian, Moroccan, etc.)
☐ Native Hawaiian or Pacific Islander (Native Hawaiian, Samoan, Tongan, etc.)
☐ White (German, Irish, English, Italian, Polish, French, etc.)
☐ Something not listed: ____________________

74
Appendix C
Information about the Quality of the Geocoding Results
Table C1
Quality of Geocoded Addresses
Address ID Match Type Match Score (%)
1 Building Centroid 100
2 Parcel 98.93
3 Parcel 100
4 Street Segment 100
5 Parcel 97.93
6 Parcel 100
7 Parcel 97.93
8 Building Centroid 96
9 Parcel 100
10 Parcel 100
11 Building Centroid 100
12 Parcel 100
13 Parcel 100
14 Building Centroid 99
15 Building Centroid 99
16 USPS Zip 60
17 Street Segment 100
18 Parcel 100
19 Parcel 100
20 Building Centroid 99
21 Building Centroid 100
22 Parcel 100
23 Parcel 100
24 Parcel 100
25 Parcel 100
26 Parcel 100
27 Parcel 100
28 Street Segment 100
29 Parcel 100
30 Building Centroid 100
31 Parcel 100
32 Building Centroid 100
33 Building Centroid 99
34 Parcel 100
35 Building Centroid 99
36 Parcel 100
37 Building Centroid 100
38 Building Centroid 100
39 Street Segment 100
40 Street Segment 100
41 Building Centroid 93.93
42 Street Segment 100
43 Parcel 100
44 Building Centroid 100
Note. Match type refers to the geographic level of the geocode match (e.g., building, parcel, street, postcode) and
the match score indicates the quality of the match in percentages.

75
Appendix D
Information about the Indicators of Structural Racism
Table D1
Description of the Indicators of Structural Racism and Year of Data Collection
Variable Description Year
Education
Chronic Absenteeism (R) Percentage of students who were absent 10% or more of the days that
they were enrolled within a school year.
2021
College Enrollment Percentage of people between 18- and 24-years old attending public or
private schools, not including vocational or trade schools.
2021
Educational Attainment
< High School diploma (R)
Associate’s degree or Higher
Bachelor’s degree or Higher
Percentage of people who are 25 years old or older who do not hold a
high school diploma, that hold an Associate’s degree or higher, or that
hold a Bachelor’s degree or higher.
2021
Healthcare
Hospitals Number of hospitals within a given neighborhood. 2021
Uninsured Rates (R) Percentage of people without health insurance or a health coverage
plan.
2021
Food Insecurity
Free or Reduced-Price Lunch Percentage of students who meet eligibility to receive free or reducedprice lunch from the National School Lunch Program.
2022
SNAP Acceptance Number of stores and food businesses that accept Supplemental
Nutrition Assistance Program benefits within a given neighborhood.
2022
Grocery Store Access (R) Number of people who do not live near a grocery store. Defined as 1
mile for urban areas and 10 miles for rural areas.
2019
Housing
Homeownership Percentage of housing units occupied by the homeowner(s). 2021
Housing Stability Percentage of households residing in the same house they were living
in one year prior.
2021
Overcrowding (R) Percentage of households with more than one person per room. 2021
Rent Burden (R) Percentage of people spending over 30% of their monthly earnings on
rent and utilities.
2021
Employment & Income
Medium Household Income Middle value for household income within a given neighborhood, with
the median household income capped at $250,000 so that everyone
who made over that median income was shown as $250,000.
2021
Youth Opportunity (R) Percentage of people between the ages of 16- and 24-years old who are
not in schools nor working.
2020
Employment Status
Labor Force
Unemployment (R)
Percentage of people older than 16 years old who are employed or
unemployed and searching for employment.
Percentage of people within the labor force who are unemployed.
2021
Poverty (R) Percentage of people within a household earning below 100% of the
federal poverty line.
2021
Policing

76
Table D1
Description of the Indicators of Structural Racism and Year of Data Collection
Variable Description Year
LAPD Stops (R)
Total Stops Rate
Latine Stops Rate
Number of vehicles and pedestrians stopped by the police per every
10,000 people.
Percentage of Latine or Hispanic identified people stopped by the
police.
2019
LAPD Arrests (R)
Total Arrests Rate
Latine Arrests Rate
Number of police arrests per every 1,000 people.
Percentage of Latine or Hispanic identified people arrested by the
police.
2019
LASD/LAPD Crimes (R)
Property Crimes
Violent Crimes
Number of property crimes reported per every 10,000 people.
Number of reported violent crimes per every 10,000 people.
2019
Note. Data and data details were extracted from the Neighborhood Data for Social Change developed by the
USC Sol Price Center for Social Innovation.
(R) indicates that variables were reversed coded by multiplying by -1 so that low scores always indicate worse
outcomes, and high scores always indicate better outcomes.
SNAP = Supplemental Nutrition Assistance Program.
LAPD = Los Angeles Police Department.
LASD = Los Angeles County Sheriff’s Department.

77
Appendix E
Tables of Main Effects and Moderation Effects with Outliers and with Outliers Winsorized
Table E1
Main Effects and Moderation Effects of Structural Racism with Outliers and with Outliers Winsorized
Model
Outliers Outliers Winsorized
β SE p β SE p
Externalizing ON
Structural Racism 0.12 0.17 0.45 0.07 0.17 0.67
Preparation for Bias -0.23 0.18 0.20 -0.24 0.19 0.21
Cultural Socialization 0.34 0.13 0.01* 0.32 0.13 0.02*
Minimization of Racism -0.12 0.13 0.33 -0.15 0.13 0.24
Structural Racism by PB 0.05 0.19 0.78 0.13 0.18 0.47
Structural Racism by CS -0.06 0.17 0.69 -0.10 0.14 0.47
Structural Racism by MR 0.05 0.16 0.75 -0.01 0.16 0.96
Anxiety ON
Structural Racism 0.11 0.15 0.48 0.06 0.14 0.68
Preparation for Bias -0.201 0.17 0.23 -0.20 0.17 0.23
Cultural Socialization 0.29 0.14 0.03* 0.27 0.13 0.03*
Minimization of Racism -0.11 0.11 0.32 -0.12 0.11 0.24
Structural Racism by PB 0.05 0.16 0.77 0.11 0.14 0.45
Structural Racism by CS -0.06 0.14 0.67 -0.08 0.11 0.45
Structural Racism by MR 0.05 0.15 0.75 -0.01 0.13 0.96
Depression ON
Structural Racism 0.07 0.10 0.48 0.04 0.09 0.67
Preparation for Bias -0.13 0.11 0.22 -0.12 0.10 0.23
Cultural Socialization 0.19 0.09 0.03* 0.17 0.08 0.04*
Minimization of Racism -0.07 0.07 0.31 -0.08 0.07 0.24
Structural Racism by PB 0.03 0.11 0.78 0.07 0.09 0.47
Structural Racism by CS -0.04 0.09 0.68 -0.05 0.07 0.46
Structural Racism by MR 0.03 0.10 0.75 -0.004 0.08 0.96
Note. * p < 0.05.
Main effects and moderation effects were estimated in separate models accounting for youths’ age and biological
sex.
β = Standardized Betas; SE = Standard Error; PB = Preparation for Bias; CS = Cultural Socialization; MR =
Minimization of Racism.

78
Table E2
Main Effects and Moderation Effects of Education with Outliers and with Outliers Winsorized
Model
Outliers Outliers Winsorized
β SE p β SE p
Externalizing ON
Education 0.20 0.13 0.13 0.20 0.13 0.13
Preparation for Bias -0.34 0.16 0.03* -0.37 0.15 0.02*
Cultural Socialization 0.43 0.12 < 0.001** 0.43 0.13 0.001*
Minimization of Racism -0.05 0.12 0.70 -0.08 0.12 0.53
Education by PB 0.07 0.17 0.68 0.002 0.15 1.00
Education Racism by CS 0.01 0.16 0.94 0.03 0.14 0.82
Education Racism by MR 0.11 0.19 0.57 0.08 0.20 0.70
Anxiety ON
Education 0.18 0.12 0.16 0.18 0.12 0.14
Preparation for Bias -0.30 0.15 0.051 -0.33 0.15 0.03*
Cultural Socialization 0.38 0.13 0.004* 0.39 0.14 0.004*
Minimization of Racism -0.04 0.10 0.70 -0.07 0.11 0.53
Education by PB 0.06 0.15 0.67 0.002 0.14 1.00
Education Racism by CS 0.01 0.14 0.94 0.03 0.13 0.82
Education Racism by MR 0.09 0.16 0.57 0.07 0.18 0.70
Depression ON
Education 0.12 0.09 0.16 0.11 0.08 0.14
Preparation for Bias -0.20 0.10 0.04* -0.21 0.09 0.03*
Cultural Socialization 0.26 0.09 0.003* 0.25 0.08 0.004*
Minimization of Racism -0.03 0.07 0.69 -0.04 0.07 0.53
Education by PB 0.04 0.10 0.68 0.001 0.09 1.00
Education Racism by CS 0.01 0.09 0.94 0.03 0.08 0.82
Education Racism by MR 0.06 0.11 0.57 0.04 0.11 0.70
Note. ** p < 0.001; * p < 0.05.
Main effects and moderation effects were estimated in separate models accounting for youths’ age and biological
sex. Bolded are findings that shifted when outliers were winsorized.
β = Standardized Betas; SE = Standard Error; PB = Preparation for Bias; CS = Cultural Socialization; MR =
Minimization of Racism.

79
Table E3
Main Effects and Moderation Effects of Healthcare with Outliers and with Outliers Winsorized
Model
Outliers Outliers Winsorized
β SE p β SE p
Externalizing ON
Healthcare 0.08 0.12 0.53 0.03 0.15 0.83
Preparation for Bias -0.28 0.15 0.07 -0.29 0.15 0.06
Cultural Socialization 0.45 0.12 < 0.001** 0.44 0.12 < 0.001**
Minimization of Racism -0.13 0.13 0.33 -0.18 0.11 0.12
Healthcare by PB 0.39 0.24 0.10 0.39 0.16 0.01*
Healthcare by CS -0.24 0.17 0.17 -0.20 0.13 0.14
Healthcare by MR -0.15 0.17 0.37 -0.06 0.22 0.80
Anxiety ON
Healthcare 0.06 0.10 0.51 0.03 0.13 0.83
Preparation for Bias -0.23 0.14 0.09 -0.25 0.14 0.07
Cultural Socialization 0.37 0.13 0.004* 0.38 0.12 0.002*
Minimization of Racism -0.10 0.11 0.36 -0.15 0.10 0.14
Healthcare by PB 0.33 0.19 0.09 0.33 0.13 0.01*
Healthcare by CS -0.20 0.15 0.19 -0.17 0.11 0.14
Healthcare by MR -0.13 0.15 0.38 -0.05 0.19 0.80
Depression ON
Healthcare 0.05 0.07 0.52 0.02 0.08 0.83
Preparation for Bias -0.17 0.10 0.08 -0.17 0.09 0.07
Cultural Socialization 0.27 0.09 0.003* 0.25 0.08 0.001*
Minimization of Racism -0.08 0.08 0.34 -0.10 0.07 0.13
Healthcare by PB 0.23 0.14 0.10 0.22 0.09 0.02*
Healthcare by CS -0.14 0.11 0.18 -0.11 0.08 0.14
Healthcare by MR -0.09 0.10 0.37 -0.03 0.12 0.80
Note. ** p < 0.001; * p < 0.05.
Main effects and moderation effects were estimated in separate models accounting for youths’ age and biological
sex. Bolded are findings that shifted when outliers were winsorized.
β = Standardized Betas; SE = Standard Error; PB = Preparation for Bias; CS = Cultural Socialization; MR =
Minimization of Racism.

80
Table E4
Main Effects and Moderation Effects of Food Insecurity with Outliers and with Outliers Winsorized
Model
Outliers Outliers Winsorized
β SE p β SE p
Externalizing ON
Food Insecurity 0.10 0.16 0.52 0.02 0.14 0.90
Preparation for Bias -0.29 0.15 0.06 -0.31 0.16 0.051
Cultural Socialization 0.46 0.13 < 0.001** 0.44 0.13 0.001*
Minimization of Racism -0.12 0.13 0.35 -0.14 0.12 0.24
Food Insecurity by PB -0.17 0.14 0.21 -0.03 0.12 0.81
Food Insecurity by CS 0.12 0.14 0.42 0.02 0.13 0.90
Food Insecurity by MR -0.13 0.10 0.20 -0.15 0.11 0.16
Anxiety ON
Food Insecurity 0.08 0.13 0.51 0.02 0.12 0.90
Preparation for Bias -0.24 0.14 0.09 -0.27 0.15 0.08
Cultural Socialization 0.38 0.14 0.01* 0.39 0.14 0.004*
Minimization of Racism -0.10 0.11 0.37 -0.12 0.11 0.25
Food Insecurity by PB -0.15 0.11 0.18 -0.02 0.10 0.81
Food Insecurity by CS 0.10 0.11 0.40 0.01 0.11 0.90
Food Insecurity by MR -0.11 0.08 0.18 -0.13 0.09 0.15
Depression ON
Food Insecurity 0.06 0.09 0.52 0.01 0.08 0.90
Preparation for Bias -0.17 0.10 0.07 -0.17 0.09 0.07
Cultural Socialization 0.27 0.09 0.004* 0.25 0.08 0.003*
Minimization of Racism -0.07 0.08 0.35 -0.08 0.07 0.24
Food Insecurity by PB -0.11 0.08 0.21 -0.02 0.07 0.81
Food Insecurity by CS 0.07 0.09 0.42 0.01 0.07 0.90
Food Insecurity by MR -0.08 0.06 0.20 -0.08 0.06 0.16
Note. ** p < 0.001; * p < 0.05.
Main effects and moderation effects were estimated in separate models accounting for youths’ age and biological
sex.
β = Standardized Betas; SE = Standard Error; PB = Preparation for Bias; CS = Cultural Socialization; MR =
Minimization of Racism.

81
Table E5
Main Effects and Moderation Effects of Housing with Outliers and with Outliers Winsorized
Model
Outliers Outliers Winsorized
β SE p β SE p
Externalizing ON
Housing 0.09 0.11 0.43 0.02 0.14 0.90
Preparation for Bias -0.27 0.15 0.07 -0.31 0.16 0.051
Cultural Socialization 0.45 0.11 < 0.001** -0.31 0.13 0.001*
Minimization of Racism -0.14 0.11 0.21 -0.14 0.12 0.24
Housing by PB 0.05 0.14 0.69 0.12 0.13 0.37
Housing by CS -0.07 0.11 0.55 -0.05 0.11 0.63
Housing by MR 0.17 0.09 0.08 0.08 0.10 0.41
Anxiety ON
Housing 0.08 0.10 0.45 0.02 0.12 0.90
Preparation for Bias -0.23 0.14 0.10 -0.27 0.15 0.08
Cultural Socialization 0.39 0.13 0.002* 0.39 0.14 0.004*
Minimization of Racism -0.13 0.10 0.23 -0.12 0.11 0.25
Housing by PB 0.05 0.12 0.69 0.10 0.11 0.35
Housing by CS -0.06 0.09 0.53 -0.05 0.10 0.63
Housing by MR 0.14 0.08 0.07 0.07 0.09 0.42
Depression ON
Housing 0.06 0.07 0.43 0.01 0.08 0.90
Preparation for Bias -0.16 0.09 0.08 -0.17 0.09 0.07
Cultural Socialization 0.27 0.08 0.001* 0.25 0.08 0.003*
Minimization of Racism -0.09 0.07 0.22 -0.08 0.07 0.24
Housing by PB 0.03 0.09 0.69 0.07 0.08 0.37
Housing by CS -0.04 0.07 0.55 -0.03 0.07 0.63
Housing by MR 0.10 0.06 0.07 0.05 0.06 0.41
Note. ** p < 0.001; * p < 0.05.
Main effects and moderation effects were estimated in separate models accounting for youths’ age and biological
sex.
β = Standardized Betas; SE = Standard Error; PB = Preparation for Bias; CS = Cultural Socialization; MR =
Minimization of Racism.

82
Table E6
Main Effects and Moderation Effects of Employment and Income with Outliers and with Outliers Winsorized
Model
Outliers Outliers Winsorized
β SE p β SE p
Externalizing ON
Employment/Income 0.25 0.14 0.08 0.28 0.15 0.05
Preparation for Bias -0.32 0.15 0.03* -0.34 0.15 0.02*
Cultural Socialization 0.46 0.11 < 0.001** 0.44 0.10 < 0.001**
Minimization of Racism -0.12 0.11 0.28 -0.15 0.11 0.18
Employment/Income by PB -0.05 0.13 0.72 0.01 0.13 0.92
Employment/Income by CS -0.06 0.17 0.74 0.01 0.14 0.96
Employment/Income by MR 0.12 0.12 0.31 0.03 0.13 0.84
Anxiety ON
Employment/Income 0.23 0.14 0.10 0.25 0.14 0.07
Preparation for Bias -0.29 0.15 0.051 -0.31 0.14 0.03*
Cultural Socialization 0.41 0.13 0.001* 0.39 0.11 < 0.001**
Minimization of Racism -0.10 0.10 0.30 -0.13 0.10 0.19
Employment/Income by PB -0.04 0.11 0.73 0.01 0.12 0.92
Employment/Income by CS -0.05 0.15 0.73 0.01 0.13 0.96
Employment/Income by MR 0.10 0.10 0.31 0.02 0.12 0.84
Depression ON
Employment/Income 0.16 0.09 0.09 0.16 0.09 0.06
Preparation for Bias -0.20 0.10 0.04* -0.20 0.09 0.02*
Cultural Socialization 0.28 0.08 0.001* 0.25 0.07 < 0.001**
Minimization of Racism -0.07 0.07 0.29 -0.09 0.06 0.19
Employment/Income by PB -0.03 0.08 0.72 0.01 0.08 0.92
Employment/Income by CS -0.04 0.11 0.74 0.004 0.08 0.96
Employment/Income by MR 0.07 0.07 0.31 0.02 0.08 0.84
Note. ** p < 0.001; * p < 0.05.
Main effects and moderation effects were estimated in separate models accounting for youths’ age and biological
sex. Bolded are findings that shifted when outliers were winsorized.
β = Standardized Betas; SE = Standard Error; PB = Preparation for Bias; CS = Cultural Socialization; MR =
Minimization of Racism.

83
Table E7
Main Effects and Moderation Effects of Policing with Outliers and with Outliers Winsorized
Model
Outliers Outliers Winsorized
β SE p β SE p
Externalizing ON
Policing 0.04 0.13 0.74 0.02 0.13 0.87
Preparation for Bias -0.20 0.18 0.25 -0.22 0.18 0.22
Cultural Socialization 0.34 0.13 0.01* 0.32 0.14 0.02*
Minimization of Racism -0.15 0.12 0.21 -0.16 0.12 0.17
Policing by PB 0.18 0.15 0.25 0.19 0.15 0.19
Policing by CS -0.24 0.13 0.06 -0.28 0.11 0.01*
Policing by MR -0.19 0.16 0.24 -0.24 0.18 0.20
Anxiety ON
Policing 0.04 0.11 0.74 0.02 0.11 0.87
Preparation for Bias -0.17 0.16 0.27 -0.18 0.15 0.23
Cultural Socialization 0.29 0.14 0.03* 0.27 0.13 0.03*
Minimization of Racism -0.13 0.10 0.21 -0.14 0.10 0.17
Policing by PB 0.15 0.12 0.21 0.16 0.11 0.17
Policing by CS -0.21 0.10 0.04* -0.23 0.09 0.01*
Policing by MR -0.16 0.13 0.22 -0.20 0.15 0.18
Depression ON
Policing 0.02 0.07 0.74 0.01 0.07 0.87
Preparation for Bias -0.12 0.10 0.26 -0.12 0.10 0.24
Cultural Socialization 0.19 0.09 0.03* 0.17 0.08 0.04*
Minimization of Racism -0.08 0.07 0.20 -0.09 0.06 0.17
Policing by PB 0.10 0.08 0.24 0.10 0.08 0.20
Policing by CS -0.14 0.07 0.04* -0.14 0.06 0.01*
Policing by MR -0.10 0.09 0.23 -0.12 0.09 0.18
Note. ** p < 0.001; * p < 0.05.
Main effects and moderation effects were estimated in separate models accounting for youths’ age and biological
sex. Bolded are findings that shifted when outliers were winsorized.
β = Standardized Betas; SE = Standard Error; PB = Preparation for Bias; CS = Cultural Socialization; MR =
Minimization of Racism.

Abstract (if available)

Abstract Structural racism heightens racial-ethnic disparities in mental health, increasing the risk for poor psychological outcomes among racial-ethnic minoritized youth. Racial-ethnic socialization (RES)–messages about race and racism–simultaneously buffer against the impact of racism. However, extant work overlooks the influence of structural racism on mental health and how RES might moderate these effects. This study explored the link between structural racism and Latine youths’ externalizing and internalizing symptoms and examined the moderating effect of caregiver RES messages (e.g., preparation for bias, cultural socialization, minimization of racism). Participants included forty-four Latine caregiver-adolescent (10-17 years old) dyads. Youth reported on their externalizing and internalizing symptoms, while caregivers reported on their RES practices. Geospatial modeling was leveraged to develop indices of structural racism. Path models were estimated to test the main effects of structural racism on youths’ symptomatology and the moderating effect of caregiver RES practices. Structural racism was not associated with youths’ externalizing or internalizing symptoms. However, preparation for bias moderated the impact of the healthcare index of structural racism, and cultural socialization moderated the impact of the policing index of structural racism on youths’ externalizing and internalizing outcomes. This pattern of results did not emerge for minimization of racism messages nor other indices of structural racism. Overall, findings suggest that indicators of structural racism differentially impact mental health outcomes, and thus robust, differently-weighted structural racism indices are warranted. Findings also underscore the benefits of RES practices and suggest that its moderating role should be considered in the context of other racist experiences.

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Asset Metadata

Creator Mateo Santana, Adrelys (author)

Core Title Mental health consequences of structural racism on Latine youth: moderating role of racial-ethnic socialization

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Psychology

Degree Conferral Date 2024-12

Publication Date 11/22/2024

Defense Date 04/11/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag Latine youths,mental health,racial-ethnic socialization,structural racism

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Advisor Galán, Chardée A. (committee chair), Habre, Rima (committee member), Huey, Stanley Jr. (committee member), Schwartz, David (committee member)

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