Rethinking the Minority-Mother Disadvantage in Multiracial Families: Parental Racial Configuration and Mental Health Across the Life Course

Rethinking the Minority-Mother Disadvantage in Multiracial Families: Parental Racial Configuration and Mental Health Across the Life Course | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rethinking the Minority-Mother Disadvantage in Multiracial Families: Parental Racial Configuration and Mental Health Across the Life Course Maia Roberson, Daniel Adkins This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9611238/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Prior research suggests that adolescents with a racially minoritized mother and White father experience elevated psychological distress, a pattern here described as a “minority-mother disadvantage.” Yet most evidence relies on self-identified multiracial samples, potentially conflating family structure with identity processes. Using five waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health; N ≈ 84,000 person-waves), this study examines whether parental racial configuration predicts trajectories of depressive symptoms and suicidal ideation from adolescence into early midlife. Multiracial family origin is defined using parent-reported race rather than respondent self-identification, allowing individuals from multiracial families to be identified independently of their own racial identification. Linear and logistic mixed-effects models reveal little evidence of a generalized minority-mother disadvantage. Depressive symptoms are modestly lower among respondents with a minority mother and White father relative to the reverse configuration, and no systematic differences emerge for suicidal ideation. These findings indicate that conclusions about parental-race asymmetries are sensitive to how multiracial populations are operationalized and are consistent with the possibility that earlier findings may partly reflect selection into multiracial self-identification. Figures Figure 1 Figure 2 Introduction Multiracial Americans represent one of the fastest-growing demographic groups in the United States. Since the U.S. Census first permitted respondents to select multiple races in 2000, the number of individuals identifying as multiracial has increased sharply, and a growing share of births now occur in interracial families (Liebler et al. 2017; Mitchell 2017). As this population expands, researchers have increasingly examined the health and well-being of multiracial youth and adults. Yet much of this work treats multiracial individuals as a single category or compares them primarily to monoracial peers, leaving an important question underdeveloped: do health outcomes vary depending on how multiracial families are racially configured, and in particular which parent occupies the minoritized versus White racial position? Research on multiracial health reveals a complex pattern of both vulnerability and resilience, with findings varying by outcome domain, racial heritage, and social context. Several studies document higher depressive symptoms, greater suicidal ideation, and elevated psychological distress among multiracial adolescents compared to monoracial peers (Asdigian et al. 2018; Fisher et al. 2014; Garcia et al. 2019; Miller et al. 2019; Udry, Li, and Hendrickson-Smith 2003; Whaley and Francis 2006; Wong et al. 2012). Other work, however, finds few differences once socioeconomic and contextual factors are taken into account (Campbell and Eggerling-Boeck 2006; Shih and Sanchez 2005). These mixed findings suggest that multiracial populations are not homogeneous, but instead reflect substantial variation in family background, social positioning, and lived experience. One important source of this heterogeneity concerns how multiracial status is defined. Most studies identify multiracial respondents using self-reported racial identity, a strategy that excludes individuals with multiracial parentage who identify as monoracial. National estimates indicate that many individuals with multiracial backgrounds identify with a single race rather than as multiracial (Parker et al. 2015). As a result, samples based solely on self-identification capture only a subset of individuals with multiracial heritage. This approach may complicate interpretation, as identity choices are shaped by social context and phenotype(Reece 2019; Saperstein and Penner 2014). Research on multiracial health has rarely examined whether outcomes vary depending on which parent occupies the minoritized versus dominant racial position. Most studies treat multiracial individuals as an aggregate group or disaggregate outcomes only by racial identity combinations, such as Black–White or Asian–White heritage. Yet mixed-race families differ in meaningful ways from both monoracial minority and monoracial White households, and the racial configuration of parents may shape children’s social environments, access to resources, and exposure to racialized stressors. Evidence suggests that parental configuration can influence family resources and social positioning. Multiracial families generally have higher incomes than monoracial minority households but lower incomes than monoracial White households (Kothari et al. 2022). Within Black–White families, however, socioeconomic resources vary by parental racial configuration. Families with a Black mother and White father tend to have higher levels of parental education and occupy higher socioeconomic positions than families with a White mother and Black father (Choi and Reichman 2019; Kothari et al. 2022). Prior work also shows that these differences extend to health-related outcomes, including general measures such as self-rated health, as well as access to diagnosis and treatment (Choi and Reichman 2019). Together, these findings suggest that parental racial configuration may structure access to both socioeconomic and institutional resources. Gendered patterns of caregiving may further shape how parental race influences children’s development. Despite increases in paternal involvement over time, mothers continue to spend substantially more time on childcare and emotional labor than fathers (Wang 2013). As a result, maternal experiences and social positioning may exert particularly strong influence on children’s socialization and daily environments. This issue is central to one of the most influential findings in the Add Health literature on multiracial adolescent well-being. Using Add Health data, Schlabach (2013) reports that adolescents with a minority mother and White father exhibit elevated depressive symptoms and suicidal ideation relative to comparison groups, a pattern described here as a “minority-mother disadvantage.” Although this finding has remained largely unchallenged, other research suggests that socioeconomic resources vary across parental racial configurations, particularly within Black–White families(Choi and Reichman 2019; Kothari et al. 2022). Campbell and Eggerling-Boeck (2006) further demonstrate that patterns of social and emotional well-being sometimes appear similar when multiracial status is defined through self-identification or parental race, but they also note meaningful differences for certain outcomes and subgroups. Theoretical perspectives from the stress process and life course traditions provide a framework for understanding how parental racial configuration may influence health. Stress process theory emphasizes how chronic, identity-relevant stressors such as discrimination, belonging uncertainty, and identity invalidation can undermine mental and physical well-being unless buffered by coping resources (Pearlin et al. 1981; Thoits 2010). Because parents’ racial identities shape exposure to racism, institutional positioning, and access to social resources, multiracial youth may encounter distinct stress environments depending on whether their mother or father holds minoritized status. Life course theory further highlights how these dynamics unfold across developmental transitions, particularly during adolescence and emerging adulthood when racial identity becomes central to self-concept and social classification intensifies(Elder 1998; Halfon and Hochstein 2002). The cumulative advantage and disadvantage perspective suggests that even modest early differences in exposure to social stressors or institutional resources may compound across the life course(Ferraro and Shippee 2009). The present study revisits the minority-mother disadvantage using an alternative operationalization of multiracial origin. Rather than identifying multiracial respondents through self-identification alone, this study defines multiracial family background using parent-reported race. This approach allows individuals from multiracial families to be included regardless of how they label their own racial identity, providing a broader test of whether parental racial configuration itself structures health outcomes. Using data across multiple waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examine whether three parental racial configurations—minority mother/White father, White mother/minority father, and two minority parents of different racial backgrounds—predict trajectories of depressive symptoms and suicidal ideation from ages 12 to 43. By shifting the analytic focus from identity to family heritage, this study evaluates whether previously documented parental race asymmetries persist when multiracial origin is defined independently of identity choice. METHODS Data This study uses data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative longitudinal study of adolescents who were enrolled in grades 7 through 12 during the 1994 to 1995 school year. Participants were followed across four additional waves as they aged into their twenties, thirties, and early forties (Wave 1: 1994 to 1995, Wave 2: 1996, Wave 3: 2001 to 2002, Wave 4: 2008, and Wave 5: 2016 to 2018). Following standard practice in Add Health research, respondents reporting Hispanic ethnicity were excluded because changes in survey wording across waves combine Hispanic origin with race, complicating interpretation of racial categories (Campbell and Eggerling-Boeck 2006); The sample was further restricted to exclude respondents whose parents reported Hispanic ethnicity. The final analytic sample includes approximately 83,945 person-wave observations spanning ages 12 to 43. Missing data were handled using multiple imputation with multivariate normal regression, a widely used and well validated approach for addressing missingness in longitudinal data (Little and Rubin 2019; Schafer 1997 ). All respondents provided written informed consent to participate in Add Health in accordance with the University of North Carolina School of Public Health Institutional Review Board guidelines. This study was approved by the University of Utah Institutional Review Board (IRB_00107767). Measures Parental racial configuration The key independent variable identifies family racial structure based on biological parents’ race at Wave 1. Parents’ self-reports were used to construct mutually exclusive categories based on parental racial composition and are treated as time-invariant across the study period. Respondents were first classified into multiracial-origin families, defined as having parents of different racial backgrounds. Within this group, three categories were distinguished: (1) White mother and minority father, (2) minority mother and White father, and (3) two minority parents of different racial backgrounds. For comparison, respondents from monoracial-origin families were also included. This group includes respondents with two White parents and respondents with two minority parents of the same race. These categories provide a reference framework for interpreting differences associated with multiracial family structure. Following prior research using Add Health data (e.g., Schlabach 2013 ; Campbell and Eggerling-Boeck 2006), this study uses the term “minority” to refer to non-White racial groups for consistency with existing literature on parental race asymmetries in multiracial families. While this terminology has limitations, its use here facilitates direct comparison with prior findings, including the minority-mother disadvantage. Depressive symptoms Depressive symptoms were measured at each of the five waves using four items from the 20-item Center for Epidemiologic Studies Depression Scale (CES-D). Respondents indicated how often during the past week they (1) could not shake off the blues, (2) felt depressed, (3) felt sad, and (4) felt happy (reverse coded). Response categories ranged from 0 (“never or rarely”) to 3 (“most of the time”), and the items were summed to create a composite score ranging from 0 to 12. Cronbach’s α for this four-item scale ranged from .77 to .82 across waves (Hargrove et al. 2020 ). Prior psychometric analyses using Add Health data demonstrate that these items form a unidimensional measure and show measurement invariance across racial/ethnic and immigrant groups, supporting their use as comparable indicators of depressive affect (Perreira et al. 2005 ). The CES-D module in Add Health includes four items, but the happiness item (“I felt happy”), corresponding to item four of the standard CES-D scale, was not administered at Wave 3. Because of this we constructed a harmonized measure by imputing this item using responses from adjacent waves (Waves 2 and 4), following prior work. This step ensures comparability of the CES-D scale across waves and reflects a measurement harmonization procedure rather than the primary missing-data strategy (Hargrove et al. 2020 ). Remaining missing data were handled using multiple imputation with multivariate normal regression. Imputation models included all variables used in the analysis, along with auxiliary variables to improve estimation, following established approaches for longitudinal analyses of Add Health data (Adkins, Christensen, and Korinek 2025; Roberson and Adkins 2026 ). Suicidal ideation Suicidal ideation was assessed at each wave with a single dichotomous question asking whether respondents had seriously considered suicide during the past 12 months. Responses were coded as 0 (“no”) or 1 (“yes”). Sociodemographic controls All conditional models adjust for biological sex, immigrant generation, and time-varying measures of education and household income. Immigrant generation is defined as respondents who were foreign-born and whose parents were also foreign-born. Socioeconomic status (SES) was measured using educational attainment and household income, with indicators harmonized across waves. Parental SES measures were drawn from Waves 1 and 2, while respondents’ own SES measures were taken from Waves 3 through 5. Because household income and parental education were not collected at Wave 2, Wave 1 values were carried forward as substitutes. Parental education at Wave 1 reflected responses from biological and other residential parents; when more than one parent reported education, values were averaged. Educational attainment was coded on ordered scales ranging from “no formal education” to “graduate education” for parents and from “eighth grade or less” to “graduate education” for respondents. Age was calculated using birth date and interview date and recentered so that the youngest observed age equals zero. Analytic strategy We first addressed missing data using multivariate normal multiple imputation (MI), an approach that performs well across mixed variable types and arbitrary patterns of missingness (Little and Rubin 2019; Schafer 1997 ). All analytic variables were imputed in a wide file and subsequently reshaped to long format for longitudinal modeling. Fifty imputations were generated, a conservative number that substantially reduces Monte Carlo error even when the fraction of missing information is moderate (Von Hippel 2020 ). MI improves statistical power and reduces bias relative to listwise deletion under standard MCAR and MAR assumptions. All analyses were conducted in Stata 17 SE. Primary analyses modeled age trajectories separately for each outcome. Depressive symptoms were estimated using linear mixed-effects models, while suicidal ideation was estimated using multilevel logistic regression. Following established approaches for modeling nonlinear health trajectories in Add Health, age was specified as a fifth-degree (quintic) polynomial to capture complex changes across adolescence and adulthood (Adkins et al. 2025; Hargrove et al. 2020 ; Roberson and Adkins 2026 ). This specification is supported by exploratory model fit tests and prior work demonstrating that higher-order polynomials improve model fit for long-term developmental trajectories in this cohort. Each model includes a random intercept for respondents and a random linear slope for age when likelihood-ratio tests indicated improved model fit. Parental racial configuration entered the models as a time-invariant main effect. Age-by-configuration interaction terms were included to assess whether disparities widened or narrowed across development. Control covariates were introduced in the final step and include sex, immigrant generation, and time-varying measures of education and household income. Respondents with a White mother and minority father served as the reference category throughout. Robustness checks paralleled the primary analyses but adapted model specifications to account for outcome distribution. For depressive symptoms, models were re-estimated using a Poisson mixed-effects specification to account for right-skewed CES-D scores. Suicidal ideation models were re-estimated using linear probability. Additional sensitivity analyses incorporated corrections for Add Health’s clustered sampling design. Across all alternative specifications, substantive conclusions were unchanged, indicating that results do not depend on distributional assumptions, link-function choice, or sampling adjustments. RESULTS Descriptive statistics Table 1 presents descriptive statistics for all variables used in the analysis, derived from the multiply imputed dataset. The average CES-D score across waves is 2.25 (SD = 2.22; theoretical range = 0–12), indicating relatively low to moderate depressive symptomatology overall. Suicidal ideation is uncommon, observed in 8.7% of observations. The mean age of respondents is 25.10 years (SD = 8.53; range ≈ 12–43.5), and just over half of observations are from female respondents (50.8%). About 3.4% of the sample is first-generation immigrant. Educational attainment averages 6.35 (SD = 1.89) on the 13-point scale, and mean household income is 8.09 (SD = 2.79) on the harmonized income scale. Table 1 Descriptive Statistics (Observed Data) Variable Mean SD Min Max Parental Racial Configuration Minority Mother–White Father 0.019 0.137 0 1 White Mother–Minority Father 0.019 0.136 0 1 Two Minority Parents (Different Races) 0.013 0.112 0 1 Monoracial (all) 0.949 0.22 0 1 CES-D Item: “Felt Blue” Wave 1 0.398 0.705 0 3 Wave 2 0.414 0.71 0 3 Wave 3 0.334 0.657 0 3 Wave 4 0.328 0.646 0 3 Wave 5 0.36 0.695 0 3 CES-D Item: “Felt Depressed” Wave 1 0.526 0.755 0 3 Wave 2 0.507 0.739 0 3 Wave 3 0.34 0.646 0 3 Wave 4 0.392 0.681 0 3 Wave 5 0.394 0.682 0 3 CES-D Item: “Felt Happy,” reverse coded Wave 1 0.884 0.809 0 3 Wave 2 0.874 0.798 0 3 Wave 4 0.847 0.813 0 3 Wave 5 0.955 0.822 0 3 CES-D Item: “Felt Sad” Wave 1 0.575 0.677 0 3 Wave 2 0.553 0.671 0 3 Wave 3 0.508 0.674 0 3 Wave 4 0.571 0.665 0 3 Wave 5 0.576 0.668 0 3 Suicidal Ideation Wave 1 0.134 0.34 0 1 Wave 2 0.108 0.31 0 1 Wave 3 0.062 0.24 0 1 Wave 4 0.07 0.255 0 1 Wave 5 0.067 0.25 0 1 Educational Attainment Parental education (Wave 1) 5.869 1.901 0 9 Respondent education (Wave 4) 6.677 1.806 1 9 Respondent education (Wave 5) 6.768 1.792 1 9 Household Income Parental income (Wave 1) 8.024 2.68 1 12 Respondent income (Wave 4) 8.021 2.696 1 12 Respondent income (Wave 5) 8.712 3.035 1 12 Sociodemographic variables Female 0.508 0.5 0 1 Age 16.099 1.736 12 21 First-generation status 0.035 0.183 0 1 With respect to family racial configuration, the analytic sample is overwhelmingly monoracial (94.1%), with multiracial parentage groups comprising relatively small shares: White mother–minority father (2.26%), minority mother–White father (2.06%), and two minority parents of different racial backgrounds (1.55%). Depressive symptoms A series of linear mixed-effects models (Table 2 ) examine whether depressive symptom trajectories vary by parental racial configuration among respondents from multiracial families. Model 1 includes racial configuration main effects and a quadratic age term; Model 2 adds a random age slope; Model 3 introduces higher-order (quintic) age polynomials to capture nonlinear change across adolescence and adulthood. Model 4 incorporates Age×Parental-Race interactions, and Model 5 adjusts for gender, immigrant generation, education, and household income. Respondents with a White mother and minority father serve as the reference category throughout. Table 2 Mixed Effects Multilevel Linear Regression Models Predicting Depressive Symptoms (CES-D) by Parental Dyads CES-D 4-item Model 1 Model 2 Model 3 Model 4 Model 5 Monoracial -0.596*** (-3.993) -0.606*** (-3.928) -0.604*** (-3.958) -0.659** (-3.008) -0.647** (-3.069) Minority Mom-White Dad -0.335 (-1.856) -0.382* (-2.066) -0.378* (-2.057) -0.626* (-2.174) -0.590* (-2.139) Minority Mom-Minority Dad -0.365 (-1.693) -0.362 (-1.693) -0.349 (-1.645) -0.319 (-1.123) -0.394 (-1.391) Age -0.031*** (-8.152) -0.033*** (-8.824) 0.607*** (15.614) 0.603*** (14.852) 0.552*** (13.539) Age^2 0.001*** (7.272) 0.001*** (7.953) -0.098*** (-13.214) -0.098*** (-13.233) -0.088*** (-11.844) Age^3 0.006*** (10.451) 0.006*** (10.470) 0.006*** (9.170) Age^4 -0.000*** (-8.081) -0.000*** (-8.100) -0.000*** (-6.920) Age^5 0.000*** (6.212) 0.000*** (6.230) 0.000*** (5.163) Monoracial x Age 0.005 (0.435) 0.009 (0.811) Minority Mom/White Dad x Age 0.023 (1.268) 0.025 (1.424) Minority Mom/Minority Dad x Age -0.003 (-0.158) 0.003 (0.178) Female 0.419*** (17.717) First Generation 0.216** (3.142) Education -0.093*** (-15.908) Income -0.077*** (-20.788) Constant 3.006*** (20.460) 3.029*** (19.998) 1.765*** (10.445) 1.821*** (7.983) 2.805*** (12.535) Random Intercept SD [aid] 1.251*** (55.360) 1.191*** (49.216) 1.187*** (49.033) 1.187*** (49.051) 1.106*** (46.105) Residual SD 1.833*** (141.116) 1.780*** (130.743) 1.774*** (131.349) 1.774*** (131.014) 1.780*** (131.017) Random Slope (Age) SD [aid] 0.041*** (15.105) 0.041*** (15.205) 0.041 (15.281) 0.038*** (13.725) Observations 83945 83945 83945 83945 83945 Note : t statistics in parentheses. Respondents with a White Mother- Minority Father dyad serve as the reference group. Random effect parameters shown as standard deviations (SDs). Higher CES-D scores indicate worse depressive symptoms. *p < 0.05 **p < 0.01 ***p < 0.001 In the fully adjusted model (Model 5), respondents with a minority mother and White father report lower depressive symptoms than those with a White mother and minority father (b = − 0.590, p .05). These estimates indicate that, net of sociodemographic controls, having a minority mother is not associated with elevated depressive symptoms among youth from multiracial families and may, in some configurations, be associated with modestly better mental health outcomes. Age effects follow the expected nonlinear pattern: depressive symptoms decline sharply from early adolescence into young adulthood and then level off across adulthood. No significant Parental-Race×Age interactions emerge, indicating that while baseline symptom levels differ modestly by parental configuration, age-related trajectories are largely parallel across groups. Covariates behave as expected. Female respondents report higher depressive symptoms (p < .001), while higher educational attainment and household income are both associated with lower symptom levels (p < .001). Taken together, these findings provide little evidence of a minority-mother disadvantage for depressive symptoms once multiracial family origins are defined using parent-reported race rather than self-identified multiracial status. Suicidal ideation Mixed-effects logistic regression models (Table 3 ) examine whether suicidal ideation varies by parental racial configuration among respondents from multiracial families. Model 1 includes racial configuration main effects and a quadratic age term; Model 2 introduces higher-order (quintic) age polynomials to capture nonlinear change across adolescence and adulthood. Model 3 incorporates Age×Parental-Race interactions, and Model 4 adjusts for gender, immigrant generation, education, and household income. Respondents with a White mother and minority father serve as the reference category throughout. Across the model sequence, results indicate little evidence of baseline differences by parental configuration and no evidence of divergence or convergence with age. Table 3 Mixed Effects Multilevel Logistic Regression Models Predicting Suicidal Ideation by Parental Dyads Suicidal Ideation Model 1 Model 2 Model 3 Model 4 Monoracial 0.402*** (-4.761) 0.399*** (-4.779) 0.385*** (-3.425) 0.390*** (-3.439) Minority Mom-White Dad 0.764 (-0.957) 0.763 (-0.954) 0.631 (-1.157) 0.656 (-1.092) Minority Mom-Minority Dad 0.475* (-2.287) 0.478* (-2.262) 0.613 (-1.093) 0.591 (-1.172) Age 0.873*** (-16.740) 2.365*** (10.455) 2.361*** (10.309) 2.352*** (10.232) Age^2 1.003*** (10.945) 0.832*** (-11.458) 0.832*** (-11.460) 0.833*** (-11.365) Age^3 1.014*** (10.634) 1.014*** (10.636) 1.014*** (10.557) Age^4 1.000*** (-9.455) 1.000*** (-9.457) 1.000*** (-9.404) Age^5 1.000*** (8.277) 1.000*** (8.279) 1.000*** (8.251) Monoracial x Age 1.003 (0.210) 1.006 (0.357) Minority Mom/White Dad x Age 1.017 (0.723) 1.018 (0.772) Minority Mom/Minority Dad x Age 0.976 (-0.827) 0.980 (-0.704) Female 1.503*** (10.024) First Generation 0.778* (-2.006) Education 0.956*** (-4.231) Income 0.960*** (-5.591) Constant 0.273*** (-6.728) 0.054*** (-12.137) 0.056*** (-9.405) 0.081*** (-8.121) Random Intercept Variance [aid] 8.082*** (22.355) 8.407*** (22.410) 8.422*** (22.453) 7.834*** (22.261) Observations 83945 83945 83945 83945 Note : Exponentiated coefficients; t statistics in parentheses. Respondents with a White Mother- Minority Father dyad serve as the reference group. *p < 0.05 **p < 0.01 ***p < 0.001 In the fully adjusted model, neither respondents with a minority mother and White father nor those with two minority parents of different races differ significantly from the reference group in their odds of suicidal ideation (p > .05). Point estimates for both groups are close to unity, indicating substantively small differences in baseline risk. These results suggest that, once multiracial family origins are defined using parent-reported race, suicidal ideation does not systematically vary by which parent holds the minoritized racial identity. No Parental-Race × Age interactions are statistically significant, indicating that trajectories of suicidal ideation remain largely parallel across adolescence and adulthood. In other words, the absence of baseline differences by parental configuration persists over time rather than emerging or dissipating with age. Covariates operate in expected directions. Female respondents report higher odds of suicidal ideation (p < .001), while higher education and household income are each associated with lower odds of ideation (p < .001). Immigrant generation is also protective. Overall, suicidal ideation patterns provide no evidence of a minority-mother disadvantage and instead indicate broadly similar risk profiles across parental racial configurations within multiracial families. Synthesis across outcomes Taken together, these models provide little evidence of a generalized minority-mother disadvantage when multiracial family origins are defined using parent-reported race rather than self-identification alone. Across outcomes, differences by parental racial configuration are modest and largely nonsignificant, and where differences do appear, they do not align with expectations derived from prior work. For depressive symptoms, the fully adjusted models indicate a small but consistent advantage for respondents with a minority mother and White father relative to the White mother–minority father reference group. Respondents with two minority parents of different races do not differ significantly from either mixed-parent group. Although the magnitude of this difference is modest, its direction contrasts with earlier research suggesting elevated psychological risk among youth with minority mothers. Results for suicidal ideation are notably uniform across parental configurations. Respondents with minority mothers, White mothers, and two minority parents of different races show no statistically significant differences in the likelihood of reporting suicidal ideation. Across outcomes, the dominant pattern is therefore one of limited or absent disparities tied to parental racial configuration. DISCUSSION Reconsidering the Minority-Mother Disadvantage Prior research suggests that adolescents from multiracial families experience worse mental health outcomes when their mother is racially minoritized, a pattern here termed a “minority-mother disadvantage.” Using Add Health data, Schlabach ( 2013 ) reports that maternal minority status is associated with elevated depressive symptoms and suicidal ideation among self-identified multiracial youth, attributing this pattern in part to gendered exposure to racism and the centrality of mothers in children’s socialization. The present study revisits this claim using a broader definition of multiracial origin based on parental race rather than self-identification. In contrast to prior findings, adolescents with a minority mother and White father reported lower depressive symptom scores than those with a White mother and minority father, and no significant differences emerged across parental configurations for suicidal ideation. Taken together, these findings suggest that parental racial configuration alone does not produce consistent disparities in mental health outcomes, and that previously documented patterns may depend on how multiracial populations are defined. One possible explanation for this divergence concerns how multiracial populations are defined. Prior literature identifies multiracial respondents through self-reported identity, which excludes adolescents with multiracial parentage who identify as monoracial. As a result, these samples may capture only a subset of youth from multiracial families rather than the broader population of individuals with multiracial origins. The present findings are consistent with the possibility that such selection processes play a role in earlier results. When adolescents from multiracial families are included regardless of how they label themselves, parental race asymmetries appear weaker and less consistent. This distinction has important implications for how parental race is theorized in relation to adolescent health. The gendered nature of parenthood, differential exposure to racism, and unequal transmission of institutional privilege remain plausible mechanisms shaping child outcomes. However, the present findings suggest that these mechanisms do not operate uniformly across all youth with minority mothers, and may instead depend on broader social and developmental processes that shape how race is experienced within families. More broadly, these findings underscore the importance of measurement decisions in research on race and health. Defining multiracial status exclusively through self-identification may conflate family background with identity development, potentially obscuring how parental characteristics relate to health. By distinguishing parental race from racial self-labeling, the present study shows that conclusions about parental influence, including maternal disadvantage, are sensitive to how multiracial populations are defined. Several limitations warrant consideration. First, the exclusion of respondents reporting Hispanic ethnicity reflects constraints in Add Health survey design, which conflates Hispanic origin with race and complicates interpretation of racial categories (Campbell and Eggerling-Boeck 2006). Second, parental race information is available only when both biological parents are present in the Wave 1 household roster, excluding some family structures and potentially limiting representativeness. Third, parental race is measured at a single time point and treated as time-invariant, preventing assessment of changes in family structure or parental involvement over time. Fourth, the relatively small size of some multiracial subgroups constrains statistical power and precludes more detailed analyses by specific racial pairings. Finally, although longitudinal models strengthen temporal ordering, the observational design does not permit causal claims regarding mechanisms linking parental racial configuration to health. In sum, the present study does not deny that maternal race shapes children’s experiences or well-being. Rather, it shows that maternal minority status alone does not constitute a generalizable disadvantage for adolescents from multiracial families. Instead, the implications of parental race may depend in part on how multiracial populations are defined, shifting attention away from deterministic family configurations and toward the social processes through which race, identity, and health become intertwined. Declarations Author Contribution Author Contributions: Maia Roberson conceptualized the study, conducted the analysis, and wrote the manuscript. Daniel E. Adkins contributed to study design, provided methodological guidance, and critically reviewed and edited the manuscript. Both authors approved the final version of the manuscript. Data Availability This study uses data from the National Longitudinal Study of Adolescent to Adult Health (Add Health). Restricted-use data are available from the Carolina Population Center at the University of North Carolina at Chapel Hill under a data use agreement and are not publicly available. Researchers may obtain access by applying for a restricted-use data contract through the Carolina Population Center. No external funding was received for this study. References Adkins, D. E., & Christensen, E. (2025). and Kim Korinek. Immigrant Generation and Depressive Symptom Trajectories from Early Adolescence to Midlife: Evidence from Five Waves of Add Health. Available at SSRN 5265275 . https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5265275 Asdigian, N. L., Running, U., Bear, J., Beals, S. M., Manson, & Kaufman, C. E. (2018). Mental Health Burden in a National Sample of American Indian and Alaska Native Adults: Differences between Multiple-Race and Single-Race Subgroups. Social Psychiatry & Psychiatric Epidemiology , 53 (5), 521–530. 10.1007/s00127-018-1494-1 Campbell, M. E., & Jennifer Eggerling-Boeck (2006). What About the Children?’ The Psychological and Social Well-Being of Multiracial Adolescents. The Sociological Quarterly , 47 (1), 147–173. 10.1111/j.1533-8525.2006.00041.x Choi, K. H., & Reichman, N. E. (2019). The Health of Biracial Children in Two-Parent Families in the United States. Demographic Research , 41 , 197–230. Elder, G. H. (1998). The Life Course as Developmental Theory. Child Development , 69 (1), 1–12. 10.1111/j.1467-8624.1998.tb06128.x Ferraro, K. F., and Tetyana Pylypiv Shippee (2009). Aging and Cumulative Inequality: How Does Inequality Get under the Skin? The Gerontologist , 49 (3), 333–343. Fisher, S., Reynolds, J. L., Hsu, W. W., & Barnes, J., and Kenneth Tyler (2014). Examining Multiracial Youth in Context: Ethnic Identity Development and Mental Health Outcomes. Journal of Youth and Adolescence , 43 (10), 1688–1699. 10.1007/s10964-014-0163-2 Garcia, G., Macasiray, T., Hedwig, B. L., Hanson, M., Rivera, & Smith, C. A. (2019). The Relationship between Mixed Race/Ethnicity, Developmental Assets, and Mental Health among Youth. Journal of Racial and Ethnic Health Disparities , 6 , 77–85. Halfon, N., and Miles Hochstein (2002). Life Course Health Development: An Integrated Framework for Developing Health, Policy, and Research. The Milbank Quarterly , 80 (3), 433–479. 10.1111/1468-0009.00019 Hargrove, T. W., Carolyn, T., Halpern, L., Gaydosh, J. M., Hussey, E. A., Whitsel, N., Dole, R. A., & Hummer, and Kathleen Mullan Harris (2020). Race/Ethnicity, Gender, and Trajectories of Depressive Symptoms Across Early- and Mid-Life Among the Add Health Cohort. Journal of Racial and Ethnic Health Disparities , 7 (4), 619–629. 10.1007/s40615-019-00692-8 Kothari, C. L., Corbit, K., Presberry, J., Bautista, T., & Brenda, O. R., and Debra Lenz (2022). Race, Multiraciality, Income, and Infant Mortality: Markers of Racial Equity. Journal of Child and Family Studies , 31 (3), 689–702. 10.1007/s10826-022-02246-9 Liebler, C. A., Sonya, R., Porter, L. E., Fernandez, J. M., Noon, & Ennis, S. R. (2017). America’s Churning Races: Race and Ethnicity Response Changes Between Census 2000 and the 2010 Census. Demography 54(1):259–284. doi: 10.1007/s13524-016-0544-0 . Little, R., and Donald Rubin (2019). Statistical Analysis with Missing Data, Third Edition (1st ed.). Wiley Series in Probability and Statistics. Wiley. Miller, B., Rocks, S., Catalina, S., Zemaitis, N., & Daniels, K., and Jaime Londono (2019). The Missing Link in Contemporary Health Disparities Research: A Profile of the Mental and Self-Rated Health of Multiracial Young Adults. Health Sociology Review , 28 (2), 209–227. 10.1080/14461242.2019.1607524 Mitchell, T. (2017). 1. Trends and Patterns in Intermarriage . https://www.pewresearch.org/social-trends/2017/05/18/1-trends-and-patterns-in-intermarriage/ Parker, K., Morin, R., Lopez, M. H., & Horowitz, J. (2015). Multiracial in America: Proud, Diverse and Growing Numbers . Pew Research Center . https://www.pewresearch.org/social-trends/2015/06/11/chapter-3-the-multiracial-identity-gap/ Pearlin, L. I., Menaghan, E. G., & Lieberman, M. A. (1981). and Joseph T. Mullan. The Stress Process. Journal of Health and Social Behavior 337–356. Perreira, K. M., Deeb-Sossa, N., & Harris, K. M., and Kenneth Bollen (2005). What Are We Measuring? An Evaluation of the CES-D across Race/Ethnicity and Immigrant Generation. Social Forces , 83 (4), 1567–1601. Reece, R. L. (2019). Coloring Racial Fluidity: How Skin Tone Shapes Multiracial Adolescents’ Racial Identity Changes. Race and Social Problems , 11 (4), 290–298. 10.1007/s12552-019-09269-w Roberson, M., & Adkins, D. E. (2026). Race Against Time . Fluid Racial Identity and Trajectories of Mental Health from Adolescence to Midlife. Saperstein, A., and Andrew M. Penner (2014). Beyond the Looking Glass: Exploring Fluidity in Racial Self-Identification and Interviewer Classification. Sociological Perspectives , 57 (2), 186–207. 10.1177/0731121414523732 Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data . Chapman and Hall/CRC. Schlabach, S. (2013). The Importance of Family, Race, and Gender for Multiracial Adolescent Well-being. Family Relations , 62 (1), 154–174. 10.1111/j.1741-3729.2012.00758.x Shih, M., & Sanchez, D. T. (2005). Perspectives and Research on the Positive and Negative Implications of Having Multiple Racial Identities. Psychological Bulletin , 131 (4), 569–591. 10.1037/0033-2909.131.4.569 Thoits, P. A. (2010). Stress and Health: Major Findings and Policy Implications. Journal of Health and Social Behavior , 51 Suppl , S41–53. 10.1177/0022146510383499 Udry, J. R., & Li, R. M., and Janet Hendrickson-Smith (2003). Health and Behavior Risks of Adolescents with Mixed-Race Identity. American Journal of Public Health , 93 (11), 1865–1870. 10.2105/AJPH.93.11.1865 Von Hippel, P. T. (2020). How Many Imputations Do You Need? A Two-Stage Calculation Using a Quadratic Rule. Sociological Methods & Research , 49 (3), 699–718. 10.1177/0049124117747303 Wang, K. P. (2013). and Wendy. Chapter 4: How Mothers and Fathers Spend Their Time. https://www.pewresearch.org/social-trends/2013/03/14/chapter-4-how-mothers-and-fathers-spend-their-time/ Whaley, A. L., and Kimberly Francis (2006). Behavioral Health in Multiracial Adolescents: The Role of Hispanic/Latino Ethnicity. Public Health Reports (1974) , 121 (2), 169–174. 10.1177/003335490612100211 Wong, S., Shucheng, J. J., Sugimoto-Matsuda, J. Y., Chang, Earl, S., & Hishinuma (2012). Ethnic Differences in Risk Factors for Suicide among American High School Students, 2009: The Vulnerability of Multiracial and Pacific Islander Adolescents. Archives of Suicide Research: Official Journal of the International Academy for Suicide Research , 16 (2), 159–173. 10.1080/13811118.2012.667334 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9611238","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636330479,"identity":"13e38d90-bf73-46b0-b6e7-6e26eed98ca1","order_by":0,"name":"Maia Roberson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYFACxgYQmQDhVCALHiBKyxmoEH4tEADRwthGhBbd9sONHxj32OTxSx9/+LhwXp0cfwOP+YOfOxjk+G4kYNVidiaxWYLhWVqxZF+OsfHMbYeNJQ7wGDb2nmEwlsSl5UBigwTDgcOJG87wsEnzbjuQuIGBx7CBt40hcQMuLecfNv8Aadl/hv2ZNO+cOrCWxr9tDPU4tdxIbIPYwsNgJs3bwAzW0gy0JcEAp5aHbRYJB9KKJc7wGBvzHAP65TBb4WzZNgnDmWce4HBY+uMbHw4AQ6yH/eFjnhpgiLU3b/j4ts1Gnu84dlvAAFWKGUxK4FY+CkbBKBgFo4AgAACrH2JXjzpNcQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Utah","correspondingAuthor":true,"prefix":"","firstName":"Maia","middleName":"","lastName":"Roberson","suffix":""},{"id":636330482,"identity":"45dee167-9ba9-433a-a1e4-3d1bd757b17b","order_by":1,"name":"Daniel Adkins","email":"","orcid":"","institution":"University of Utah","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Adkins","suffix":""}],"badges":[],"createdAt":"2026-05-04 18:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9611238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9611238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108780162,"identity":"c5b596fa-12fc-49d9-853d-0699816408ba","added_by":"auto","created_at":"2026-05-08 10:07:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273230,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9611238/v1/38a03b5301ed87ae2c2cc91d.png"},{"id":108780163,"identity":"e194762a-8cce-4761-b2bc-b9198188a7be","added_by":"auto","created_at":"2026-05-08 10:07:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":281568,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9611238/v1/91397e1aaaf518c179d19f44.png"},{"id":108807501,"identity":"486434ee-4a6d-4174-a5aa-d06b9a650fd6","added_by":"auto","created_at":"2026-05-08 15:30:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1082570,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9611238/v1/acbe801a-5074-4b80-b75e-ea2f96de1c90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rethinking the Minority-Mother Disadvantage in Multiracial Families: Parental Racial Configuration and Mental Health Across the Life Course","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiracial Americans represent one of the fastest-growing demographic groups in the United States. Since the U.S. Census first permitted respondents to select multiple races in 2000, the number of individuals identifying as multiracial has increased sharply, and a growing share of births now occur in interracial families (Liebler et al. 2017; Mitchell 2017). As this population expands, researchers have increasingly examined the health and well-being of multiracial youth and adults. Yet much of this work treats multiracial individuals as a single category or compares them primarily to monoracial peers, leaving an important question underdeveloped: do health outcomes vary depending on how multiracial families are racially configured, and in particular which parent occupies the minoritized versus White racial position?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch on multiracial health reveals a complex pattern of both vulnerability and resilience, with findings varying by outcome domain, racial heritage, and social context. Several studies document higher depressive symptoms, greater suicidal ideation, and elevated psychological distress among multiracial adolescents compared to monoracial peers (Asdigian et al. 2018; Fisher et al. 2014; Garcia et al. 2019; Miller et al. 2019; Udry, Li, and Hendrickson-Smith 2003; Whaley and Francis 2006; Wong et al. 2012). Other work, however, finds few differences once socioeconomic and contextual factors are taken into account (Campbell and Eggerling-Boeck 2006; Shih and Sanchez 2005). These mixed findings suggest that multiracial populations are not homogeneous, but instead reflect substantial variation in family background, social positioning, and lived experience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne important source of this heterogeneity concerns how multiracial status is defined. Most studies identify multiracial respondents using self-reported racial identity, a strategy that excludes individuals with multiracial parentage who identify as monoracial. National estimates indicate that many individuals with multiracial backgrounds identify with a single race rather than as multiracial (Parker et al. 2015). As a result, samples based solely on self-identification capture only a subset of individuals with multiracial heritage. This approach may complicate interpretation, as identity choices are shaped by social context and phenotype(Reece 2019; Saperstein and Penner 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch on multiracial health has rarely examined whether outcomes vary depending on which parent occupies the minoritized versus dominant racial position. Most studies treat multiracial individuals as an aggregate group or disaggregate outcomes only by racial identity combinations, such as Black\u0026ndash;White or Asian\u0026ndash;White heritage. Yet mixed-race families differ in meaningful ways from both monoracial minority and monoracial White households, and the racial configuration of parents may shape children\u0026rsquo;s social environments, access to resources, and exposure to racialized stressors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvidence suggests that parental configuration can influence family resources and social positioning. Multiracial families generally have higher incomes than monoracial minority households but lower incomes than monoracial White households (Kothari et al. 2022). Within Black\u0026ndash;White families, however, socioeconomic resources vary by parental racial configuration. Families with a Black mother and White father tend to have higher levels of parental education and occupy higher socioeconomic positions than families with a White mother and Black father (Choi and Reichman 2019; Kothari et al. 2022). Prior work also shows that these differences extend to health-related outcomes, including general measures such as self-rated health, as well as access to diagnosis and treatment (Choi and Reichman 2019). Together, these findings suggest that parental racial configuration may structure access to both socioeconomic and institutional resources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGendered patterns of caregiving may further shape how parental race influences children\u0026rsquo;s development. Despite increases in paternal involvement over time, mothers continue to spend substantially more time on childcare and emotional labor than fathers (Wang 2013). As a result, maternal experiences and social positioning may exert particularly strong influence on children\u0026rsquo;s socialization and daily environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis issue is central to one of the most influential findings in the Add Health literature on multiracial adolescent well-being. Using Add Health data, Schlabach (2013) reports that adolescents with a minority mother and White father exhibit elevated depressive symptoms and suicidal ideation relative to comparison groups, a pattern described here as a \u0026ldquo;minority-mother disadvantage.\u0026rdquo; Although this finding has remained largely unchallenged, other research suggests that socioeconomic resources vary across parental racial configurations, particularly within Black\u0026ndash;White families(Choi and Reichman 2019; Kothari et al. 2022). Campbell and Eggerling-Boeck (2006) further demonstrate that patterns of social and emotional well-being sometimes appear similar when multiracial status is defined through self-identification or parental race, but they also note meaningful differences for certain outcomes and subgroups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTheoretical perspectives from the stress process and life course traditions provide a framework for understanding how parental racial configuration may influence health. Stress process theory emphasizes how chronic, identity-relevant stressors such as discrimination, belonging uncertainty, and identity invalidation can undermine mental and physical well-being unless buffered by coping resources (Pearlin et al. 1981; Thoits 2010). Because parents\u0026rsquo; racial identities shape exposure to racism, institutional positioning, and access to social resources, multiracial youth may encounter distinct stress environments depending on whether their mother or father holds minoritized status. Life course theory further highlights how these dynamics unfold across developmental transitions, particularly during adolescence and emerging adulthood when racial identity becomes central to self-concept and social classification intensifies(Elder 1998; Halfon and Hochstein 2002). The cumulative advantage and disadvantage perspective suggests that even modest early differences in exposure to social stressors or institutional resources may compound across the life course(Ferraro and Shippee 2009).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present study revisits the minority-mother disadvantage using an alternative operationalization of multiracial origin. Rather than identifying multiracial respondents through self-identification alone, this study defines multiracial family background using parent-reported race. This approach allows individuals from multiracial families to be included regardless of how they label their own racial identity, providing a broader test of whether parental racial configuration itself structures health outcomes.\u003c/p\u003e\n\u003cp\u003eUsing data across multiple waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examine whether three parental racial configurations\u0026mdash;minority mother/White father, White mother/minority father, and two minority parents of different racial backgrounds\u0026mdash;predict trajectories of depressive symptoms and suicidal ideation from ages 12 to 43. By shifting the analytic focus from identity to family heritage, this study evaluates whether previously documented parental race asymmetries persist when multiracial origin is defined independently of identity choice.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThis study uses data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative longitudinal study of adolescents who were enrolled in grades 7 through 12 during the 1994 to 1995 school year. Participants were followed across four additional waves as they aged into their twenties, thirties, and early forties (Wave 1: 1994 to 1995, Wave 2: 1996, Wave 3: 2001 to 2002, Wave 4: 2008, and Wave 5: 2016 to 2018). Following standard practice in Add Health research, respondents reporting Hispanic ethnicity were excluded because changes in survey wording across waves combine Hispanic origin with race, complicating interpretation of racial categories (Campbell and Eggerling-Boeck 2006); The sample was further restricted to exclude respondents whose parents reported Hispanic ethnicity. The final analytic sample includes approximately 83,945 person-wave observations spanning ages 12 to 43. Missing data were handled using multiple imputation with multivariate normal regression, a widely used and well validated approach for addressing missingness in longitudinal data (Little and Rubin 2019; Schafer \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). All respondents provided written informed consent to participate in Add Health in accordance with the University of North Carolina School of Public Health Institutional Review Board guidelines. This study was approved by the University of Utah Institutional Review Board (IRB_00107767).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eParental racial configuration\u003c/p\u003e \u003cp\u003eThe key independent variable identifies family racial structure based on biological parents\u0026rsquo; race at Wave 1. Parents\u0026rsquo; self-reports were used to construct mutually exclusive categories based on parental racial composition and are treated as time-invariant across the study period.\u003c/p\u003e \u003cp\u003eRespondents were first classified into multiracial-origin families, defined as having parents of different racial backgrounds. Within this group, three categories were distinguished: (1) White mother and minority father, (2) minority mother and White father, and (3) two minority parents of different racial backgrounds.\u003c/p\u003e \u003cp\u003eFor comparison, respondents from monoracial-origin families were also included. This group includes respondents with two White parents and respondents with two minority parents of the same race. These categories provide a reference framework for interpreting differences associated with multiracial family structure.\u003c/p\u003e \u003cp\u003eFollowing prior research using Add Health data (e.g., Schlabach \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Campbell and Eggerling-Boeck 2006), this study uses the term \u0026ldquo;minority\u0026rdquo; to refer to non-White racial groups for consistency with existing literature on parental race asymmetries in multiracial families. While this terminology has limitations, its use here facilitates direct comparison with prior findings, including the minority-mother disadvantage.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDepressive symptoms\u003c/h3\u003e\n\u003cp\u003eDepressive symptoms were measured at each of the five waves using four items from the 20-item Center for Epidemiologic Studies Depression Scale (CES-D). Respondents indicated how often during the past week they (1) could not shake off the blues, (2) felt depressed, (3) felt sad, and (4) felt happy (reverse coded). Response categories ranged from 0 (\u0026ldquo;never or rarely\u0026rdquo;) to 3 (\u0026ldquo;most of the time\u0026rdquo;), and the items were summed to create a composite score ranging from 0 to 12. Cronbach\u0026rsquo;s α for this four-item scale ranged from .77 to .82 across waves (Hargrove et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Prior psychometric analyses using Add Health data demonstrate that these items form a unidimensional measure and show measurement invariance across racial/ethnic and immigrant groups, supporting their use as comparable indicators of depressive affect (Perreira et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The CES-D module in Add Health includes four items, but the happiness item (\u0026ldquo;I felt happy\u0026rdquo;), corresponding to item four of the standard CES-D scale, was not administered at Wave 3.\u003c/p\u003e \u003cp\u003eBecause of this we constructed a harmonized measure by imputing this item using responses from adjacent waves (Waves 2 and 4), following prior work. This step ensures comparability of the CES-D scale across waves and reflects a measurement harmonization procedure rather than the primary missing-data strategy (Hargrove et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRemaining missing data were handled using multiple imputation with multivariate normal regression. Imputation models included all variables used in the analysis, along with auxiliary variables to improve estimation, following established approaches for longitudinal analyses of Add Health data (Adkins, Christensen, and Korinek 2025; Roberson and Adkins \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSuicidal ideation\u003c/h3\u003e\n\u003cp\u003eSuicidal ideation was assessed at each wave with a single dichotomous question asking whether respondents had seriously considered suicide during the past 12 months. Responses were coded as 0 (\u0026ldquo;no\u0026rdquo;) or 1 (\u0026ldquo;yes\u0026rdquo;).\u003c/p\u003e\n\u003ch3\u003eSociodemographic controls\u003c/h3\u003e\n\u003cp\u003eAll conditional models adjust for biological sex, immigrant generation, and time-varying measures of education and household income. Immigrant generation is defined as respondents who were foreign-born and whose parents were also foreign-born. Socioeconomic status (SES) was measured using educational attainment and household income, with indicators harmonized across waves. Parental SES measures were drawn from Waves 1 and 2, while respondents\u0026rsquo; own SES measures were taken from Waves 3 through 5. Because household income and parental education were not collected at Wave 2, Wave 1 values were carried forward as substitutes. Parental education at Wave 1 reflected responses from biological and other residential parents; when more than one parent reported education, values were averaged. Educational attainment was coded on ordered scales ranging from \u0026ldquo;no formal education\u0026rdquo; to \u0026ldquo;graduate education\u0026rdquo; for parents and from \u0026ldquo;eighth grade or less\u0026rdquo; to \u0026ldquo;graduate education\u0026rdquo; for respondents. Age was calculated using birth date and interview date and recentered so that the youngest observed age equals zero.\u003c/p\u003e\n\u003ch3\u003eAnalytic strategy\u003c/h3\u003e\n\u003cp\u003eWe first addressed missing data using multivariate normal multiple imputation (MI), an approach that performs well across mixed variable types and arbitrary patterns of missingness (Little and Rubin 2019; Schafer \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). All analytic variables were imputed in a wide file and subsequently reshaped to long format for longitudinal modeling. Fifty imputations were generated, a conservative number that substantially reduces Monte Carlo error even when the fraction of missing information is moderate (Von Hippel \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MI improves statistical power and reduces bias relative to listwise deletion under standard MCAR and MAR assumptions. All analyses were conducted in Stata 17 SE.\u003c/p\u003e \u003cp\u003ePrimary analyses modeled age trajectories separately for each outcome. Depressive symptoms were estimated using linear mixed-effects models, while suicidal ideation was estimated using multilevel logistic regression. Following established approaches for modeling nonlinear health trajectories in Add Health, age was specified as a fifth-degree (quintic) polynomial to capture complex changes across adolescence and adulthood (Adkins et al. 2025; Hargrove et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Roberson and Adkins \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This specification is supported by exploratory model fit tests and prior work demonstrating that higher-order polynomials improve model fit for long-term developmental trajectories in this cohort. Each model includes a random intercept for respondents and a random linear slope for age when likelihood-ratio tests indicated improved model fit.\u003c/p\u003e \u003cp\u003eParental racial configuration entered the models as a time-invariant main effect. Age-by-configuration interaction terms were included to assess whether disparities widened or narrowed across development. Control covariates were introduced in the final step and include sex, immigrant generation, and time-varying measures of education and household income. Respondents with a White mother and minority father served as the reference category throughout.\u003c/p\u003e \u003cp\u003eRobustness checks paralleled the primary analyses but adapted model specifications to account for outcome distribution. For depressive symptoms, models were re-estimated using a Poisson mixed-effects specification to account for right-skewed CES-D scores. Suicidal ideation models were re-estimated using linear probability. Additional sensitivity analyses incorporated corrections for Add Health\u0026rsquo;s clustered sampling design. Across all alternative specifications, substantive conclusions were unchanged, indicating that results do not depend on distributional assumptions, link-function choice, or sampling adjustments.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for all variables used in the analysis, derived from the multiply imputed dataset. The average CES-D score across waves is 2.25 (SD\u0026thinsp;=\u0026thinsp;2.22; theoretical range\u0026thinsp;=\u0026thinsp;0\u0026ndash;12), indicating relatively low to moderate depressive symptomatology overall. Suicidal ideation is uncommon, observed in 8.7% of observations. The mean age of respondents is 25.10 years (SD\u0026thinsp;=\u0026thinsp;8.53; range\u0026thinsp;\u0026asymp;\u0026thinsp;12\u0026ndash;43.5), and just over half of observations are from female respondents (50.8%). About 3.4% of the sample is first-generation immigrant. Educational attainment averages 6.35 (SD\u0026thinsp;=\u0026thinsp;1.89) on the 13-point scale, and mean household income is 8.09 (SD\u0026thinsp;=\u0026thinsp;2.79) on the harmonized income scale.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics (Observed Data)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental Racial Configuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mother\u0026ndash;White Father\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Mother\u0026ndash;Minority Father\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo Minority Parents (Different Races)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonoracial (all)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCES-D Item: \u0026ldquo;Felt Blue\u0026rdquo;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCES-D Item: \u0026ldquo;Felt Depressed\u0026rdquo;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCES-D Item: \u0026ldquo;Felt Happy,\u0026rdquo; reverse coded\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCES-D Item: \u0026ldquo;Felt Sad\u0026rdquo;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuicidal Ideation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWave 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Attainment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education (Wave 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent education (Wave 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent education (Wave 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental income (Wave 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent income (Wave 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent income (Wave 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-generation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWith respect to family racial configuration, the analytic sample is overwhelmingly monoracial (94.1%), with multiracial parentage groups comprising relatively small shares: White mother\u0026ndash;minority father (2.26%), minority mother\u0026ndash;White father (2.06%), and two minority parents of different racial backgrounds (1.55%).\u003c/p\u003e \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here. \u0026gt;\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDepressive symptoms\u003c/h3\u003e\n\u003cp\u003eA series of linear mixed-effects models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) examine whether depressive symptom trajectories vary by parental racial configuration among respondents from multiracial families. Model 1 includes racial configuration main effects and a quadratic age term; Model 2 adds a random age slope; Model 3 introduces higher-order (quintic) age polynomials to capture nonlinear change across adolescence and adulthood. Model 4 incorporates Age\u0026times;Parental-Race interactions, and Model 5 adjusts for gender, immigrant generation, education, and household income. Respondents with a White mother and minority father serve as the reference category throughout.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed Effects Multilevel Linear Regression Models Predicting Depressive Symptoms (CES-D) by Parental Dyads\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCES-D 4-item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonoracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.596***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.606***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.604***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.659**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-3.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.647**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-3.069)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom-White Dad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.382*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.378*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.626*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-2.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.590*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-2.139)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom-Minority Dad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-1.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-1.391)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-8.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-8.824)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.607***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(15.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.603***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(14.852)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.552***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(13.539)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-13.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-13.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-11.844)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(10.451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(10.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(9.170)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-8.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-8.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-6.920)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(6.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(5.163)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonoracial x Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.811)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom/White Dad x Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.424)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom/Minority Dad x Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.178)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.419***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(17.717)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst Generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.216**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(3.142)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.093***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-15.908)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-20.788)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(20.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(19.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.765***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(10.445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.821***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(7.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.805***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(12.535)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Intercept SD [aid]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.251***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(55.360)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.191***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(49.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(49.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(49.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.106***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(46.105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.833***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(141.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.780***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(130.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.774***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(131.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.774***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(131.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.780***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(131.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Slope (Age) SD [aid]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(15.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(15.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(15.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(13.725)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eNote\u003c/b\u003e: t statistics in parentheses. Respondents with a White Mother- Minority Father dyad serve as the reference group. Random effect parameters shown as standard deviations (SDs). Higher CES-D scores indicate worse depressive symptoms. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the fully adjusted model (Model 5), respondents with a minority mother and White father report lower depressive symptoms than those with a White mother and minority father (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.590, p \u0026lt; .05). In contrast, respondents with two minority parents of different races do not differ significantly from the reference group (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.394, p \u0026gt; .05). These estimates indicate that, net of sociodemographic controls, having a minority mother is not associated with elevated depressive symptoms among youth from multiracial families and may, in some configurations, be associated with modestly better mental health outcomes.\u003c/p\u003e \u003cp\u003eAge effects follow the expected nonlinear pattern: depressive symptoms decline sharply from early adolescence into young adulthood and then level off across adulthood. No significant Parental-Race\u0026times;Age interactions emerge, indicating that while baseline symptom levels differ modestly by parental configuration, age-related trajectories are largely parallel across groups.\u003c/p\u003e \u003cp\u003eCovariates behave as expected. Female respondents report higher depressive symptoms (p \u0026lt; .001), while higher educational attainment and household income are both associated with lower symptom levels (p \u0026lt; .001). Taken together, these findings provide little evidence of a minority-mother disadvantage for depressive symptoms once multiracial family origins are defined using parent-reported race rather than self-identified multiracial status.\u003c/p\u003e \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here. \u0026gt;\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSuicidal ideation\u003c/h2\u003e \u003cp\u003eMixed-effects logistic regression models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) examine whether suicidal ideation varies by parental racial configuration among respondents from multiracial families. Model 1 includes racial configuration main effects and a quadratic age term; Model 2 introduces higher-order (quintic) age polynomials to capture nonlinear change across adolescence and adulthood. Model 3 incorporates Age\u0026times;Parental-Race interactions, and Model 4 adjusts for gender, immigrant generation, education, and household income. Respondents with a White mother and minority father serve as the reference category throughout. Across the model sequence, results indicate little evidence of baseline differences by parental configuration and no evidence of divergence or convergence with age.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed Effects Multilevel Logistic Regression Models Predicting Suicidal Ideation by Parental Dyads\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicidal Ideation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonoracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.402***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-4.761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.399***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.385***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.390***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-3.439)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom-White Dad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-1.092)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom-Minority Dad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.475*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.478*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-1.172)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.873***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-16.740)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.365***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(10.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.361***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(10.309)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.352***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(10.232)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(10.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.832***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-11.458)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.832***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-11.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.833***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-11.365)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(10.634)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(10.636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(10.557)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-9.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-9.457)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-9.404)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge^5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8.251)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonoracial x Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.357)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom/White Dad x Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.772)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority Mom/Minority Dad x Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.704)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.503***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(10.024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst Generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.778*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-2.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.956***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-4.231)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.960***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-5.591)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.273***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-6.728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-12.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-9.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.081***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-8.121)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Intercept Variance [aid]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.082***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(22.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.407***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(22.410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.422***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(22.453)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.834***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(22.261)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote\u003c/b\u003e: Exponentiated coefficients; t statistics in parentheses. Respondents with a White Mother- Minority Father dyad serve as the reference group. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the fully adjusted model, neither respondents with a minority mother and White father nor those with two minority parents of different races differ significantly from the reference group in their odds of suicidal ideation (p \u0026gt; .05). Point estimates for both groups are close to unity, indicating substantively small differences in baseline risk. These results suggest that, once multiracial family origins are defined using parent-reported race, suicidal ideation does not systematically vary by which parent holds the minoritized racial identity.\u003c/p\u003e \u003cp\u003eNo Parental-Race \u0026times; Age interactions are statistically significant, indicating that trajectories of suicidal ideation remain largely parallel across adolescence and adulthood. In other words, the absence of baseline differences by parental configuration persists over time rather than emerging or dissipating with age.\u003c/p\u003e \u003cp\u003eCovariates operate in expected directions. Female respondents report higher odds of suicidal ideation (p \u0026lt; .001), while higher education and household income are each associated with lower odds of ideation (p \u0026lt; .001). Immigrant generation is also protective. Overall, suicidal ideation patterns provide no evidence of a minority-mother disadvantage and instead indicate broadly similar risk profiles across parental racial configurations within multiracial families.\u003c/p\u003e \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here. \u0026gt;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSynthesis across outcomes\u003c/h2\u003e \u003cp\u003eTaken together, these models provide little evidence of a generalized minority-mother disadvantage when multiracial family origins are defined using parent-reported race rather than self-identification alone. Across outcomes, differences by parental racial configuration are modest and largely nonsignificant, and where differences do appear, they do not align with expectations derived from prior work.\u003c/p\u003e \u003cp\u003eFor depressive symptoms, the fully adjusted models indicate a small but consistent advantage for respondents with a minority mother and White father relative to the White mother\u0026ndash;minority father reference group. Respondents with two minority parents of different races do not differ significantly from either mixed-parent group. Although the magnitude of this difference is modest, its direction contrasts with earlier research suggesting elevated psychological risk among youth with minority mothers.\u003c/p\u003e \u003cp\u003eResults for suicidal ideation are notably uniform across parental configurations. Respondents with minority mothers, White mothers, and two minority parents of different races show no statistically significant differences in the likelihood of reporting suicidal ideation.\u003c/p\u003e \u003cp\u003eAcross outcomes, the dominant pattern is therefore one of limited or absent disparities tied to parental racial configuration.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReconsidering the Minority-Mother Disadvantage\u003c/h2\u003e \u003cp\u003ePrior research suggests that adolescents from multiracial families experience worse mental health outcomes when their mother is racially minoritized, a pattern here termed a \u0026ldquo;minority-mother disadvantage.\u0026rdquo; Using Add Health data, Schlabach (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reports that maternal minority status is associated with elevated depressive symptoms and suicidal ideation among self-identified multiracial youth, attributing this pattern in part to gendered exposure to racism and the centrality of mothers in children\u0026rsquo;s socialization. The present study revisits this claim using a broader definition of multiracial origin based on parental race rather than self-identification.\u003c/p\u003e \u003cp\u003eIn contrast to prior findings, adolescents with a minority mother and White father reported lower depressive symptom scores than those with a White mother and minority father, and no significant differences emerged across parental configurations for suicidal ideation. Taken together, these findings suggest that parental racial configuration alone does not produce consistent disparities in mental health outcomes, and that previously documented patterns may depend on how multiracial populations are defined.\u003c/p\u003e \u003cp\u003eOne possible explanation for this divergence concerns how multiracial populations are defined. Prior literature identifies multiracial respondents through self-reported identity, which excludes adolescents with multiracial parentage who identify as monoracial. As a result, these samples may capture only a subset of youth from multiracial families rather than the broader population of individuals with multiracial origins. The present findings are consistent with the possibility that such selection processes play a role in earlier results. When adolescents from multiracial families are included regardless of how they label themselves, parental race asymmetries appear weaker and less consistent.\u003c/p\u003e \u003cp\u003eThis distinction has important implications for how parental race is theorized in relation to adolescent health. The gendered nature of parenthood, differential exposure to racism, and unequal transmission of institutional privilege remain plausible mechanisms shaping child outcomes. However, the present findings suggest that these mechanisms do not operate uniformly across all youth with minority mothers, and may instead depend on broader social and developmental processes that shape how race is experienced within families.\u003c/p\u003e \u003cp\u003eMore broadly, these findings underscore the importance of measurement decisions in research on race and health. Defining multiracial status exclusively through self-identification may conflate family background with identity development, potentially obscuring how parental characteristics relate to health. By distinguishing parental race from racial self-labeling, the present study shows that conclusions about parental influence, including maternal disadvantage, are sensitive to how multiracial populations are defined.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. First, the exclusion of respondents reporting Hispanic ethnicity reflects constraints in Add Health survey design, which conflates Hispanic origin with race and complicates interpretation of racial categories (Campbell and Eggerling-Boeck 2006). Second, parental race information is available only when both biological parents are present in the Wave 1 household roster, excluding some family structures and potentially limiting representativeness. Third, parental race is measured at a single time point and treated as time-invariant, preventing assessment of changes in family structure or parental involvement over time. Fourth, the relatively small size of some multiracial subgroups constrains statistical power and precludes more detailed analyses by specific racial pairings. Finally, although longitudinal models strengthen temporal ordering, the observational design does not permit causal claims regarding mechanisms linking parental racial configuration to health.\u003c/p\u003e \u003cp\u003eIn sum, the present study does not deny that maternal race shapes children\u0026rsquo;s experiences or well-being. Rather, it shows that maternal minority status alone does not constitute a generalizable disadvantage for adolescents from multiracial families. Instead, the implications of parental race may depend in part on how multiracial populations are defined, shifting attention away from deterministic family configurations and toward the social processes through which race, identity, and health become intertwined.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Maia Roberson conceptualized the study, conducted the analysis, and wrote the manuscript. Daniel E. Adkins contributed to study design, provided methodological guidance, and critically reviewed and edited the manuscript. Both authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study uses data from the National Longitudinal Study of Adolescent to Adult Health (Add Health). Restricted-use data are available from the Carolina Population Center at the University of North Carolina at Chapel Hill under a data use agreement and are not publicly available. Researchers may obtain access by applying for a restricted-use data contract through the Carolina Population Center.\u003c/p\u003e\u003cp\u003eNo external funding was received for this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdkins, D. E., \u0026amp; Christensen, E. (2025). and Kim Korinek. 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Ethnic Differences in Risk Factors for Suicide among American High School Students, 2009: The Vulnerability of Multiracial and Pacific Islander Adolescents. \u003cem\u003eArchives of Suicide Research: Official Journal of the International Academy for Suicide Research\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 159\u0026ndash;173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13811118.2012.667334\u003c/span\u003e\u003cspan address=\"10.1080/13811118.2012.667334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9611238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9611238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrior research suggests that adolescents with a racially minoritized mother and White father experience elevated psychological distress, a pattern here described as a “minority-mother disadvantage.” Yet most evidence relies on self-identified multiracial samples, potentially conflating family structure with identity processes. Using five waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health; N ≈ 84,000 person-waves), this study examines whether parental racial configuration predicts trajectories of depressive symptoms and suicidal ideation from adolescence into early midlife. Multiracial family origin is defined using parent-reported race rather than respondent self-identification, allowing individuals from multiracial families to be identified independently of their own racial identification. Linear and logistic mixed-effects models reveal little evidence of a generalized minority-mother disadvantage. Depressive symptoms are modestly lower among respondents with a minority mother and White father relative to the reverse configuration, and no systematic differences emerge for suicidal ideation. These findings indicate that conclusions about parental-race asymmetries are sensitive to how multiracial populations are operationalized and are consistent with the possibility that earlier findings may partly reflect selection into multiracial self-identification.\u003c/p\u003e","manuscriptTitle":"Rethinking the Minority-Mother Disadvantage in Multiracial Families: Parental Racial Configuration and Mental Health Across the Life Course","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 10:07:28","doi":"10.21203/rs.3.rs-9611238/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8e86317-00b3-47f0-b467-554dd7980228","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"5","date":"2026-05-09T14:33:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-08T03:59:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-08T03:59:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Race and Social Problems","date":"2026-05-04T18:07:48+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T14:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 10:07:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9611238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9611238","identity":"rs-9611238","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}