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Mental Health across the Early Life Course at the Intersection of Race, Skin Tone, and School Racial Context.

Taylor W Hargrove
Published in: Social forces; a scientific medium of social study and interpretation (2023)
Prior research documents higher levels of depressive symptoms among Black Americans relative to Whites. Yet, we know less about the role of other dimensions of stratification (e.g., skin tone) in shaping mental health inequality between Black and White adults, and whether mental health trajectories by race and skin tone among Black adults are contingent upon social contexts in childhood and adolescence. To address these gaps, this study asks: 1) to what extent do self-identified race and interviewer-rated skin tone among Black respondents shape inequalities in depressive symptoms between Black and White Americans across ages 12-42? 2) Are trajectories of depressive symptoms by race and skin tone among Black respondents contingent on school racial contexts (e.g., school racial composition)? Using five waves of data from the National Longitudinal Study of Adolescent to Adult Health and growth curve models, results suggest trajectories of depressive symptoms across ages 12-42 vary by race, school racial context, and skin tone among Black respondents. Specifically, Black students rated as having very dark, dark, and medium brown skin who attended high proportion Black schools in adolescence experienced lower levels of depressive symptoms than their White and light-skinned Black counterparts, particularly across the teen years and early 20s. Conversely, attending higher proportion White schools led to increases in depressive symptoms across earlier ages for Black students, particularly those who fell within the middle of the skin color continuum. Findings highlight competing advantages and disadvantages of navigating racialized spaces in childhood/adolescence for Black Americans of different skin tones.
Keyphrases
  • depressive symptoms
  • mental health
  • social support
  • soft tissue
  • wound healing
  • early life
  • physical activity
  • sleep quality
  • public health
  • young adults
  • machine learning
  • high school
  • big data
  • risk assessment