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The impact of adolescents' racial and ethnic self-identity on hope.

Cheryl ZlotnickHadass GoldblattDaphna Birenbaum-CarmeliYael DishonOmer TaychawEfrat Shadmi
Published in: Health & social care in the community (2019)
The two components of hope (i.e., hope-agency defined as the ability to envision and believe in one's ability to achieve goals; hope-pathway defined as belief in one's ability to devise strategies to achieve one's goals) propel adolescents toward well-being, academic achievement and personal fulfillment. This study compares levels of hope and its components, for different groups of immigrant and ethnic non-immigrant youths, while adjusting for and measuring the impact of racism, school and family characteristics, and the youth's unique individual attributes. Using a community-based participatory research approach and a cross-sectional study design, data were collected from immigrant and non-immigrant youth (n = 567) between May 2015 and December 2015 at three Israeli public high schools. The study included five groups of youth based on their self-descriptions: Ethiopian immigrant (n = 48), Russian immigrant (n = 145), Israeli-born Mizrachi/Sephardi (n = 59), Israeli-born Ashkenazi (n = 49), or Israeli-born Unspecified (n = 266). Linear regression models showed that Ethiopian immigrant youth, compared to Russian immigrant youth and all Israeli-born groups of youth, had significantly lower hope-agency, hope-pathway and overall hope. However, an interaction effect between racism and ethnicity indicated that adolescents who perceived racism and self-identified as Ethiopian had higher hope-agency, hope-pathway and overall hope. This effect was not found with Russian immigrant or Israeli-born youth. Immigrants of color compared to other immigrants and ethnicities have less overall hope; but those who acknowledge racism feel more control over their future (hope-agency), able to devise strategies to surmount barriers blocking goals (hope-pathway), and have greater overall hope.
Keyphrases
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  • depressive symptoms
  • machine learning
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  • deep learning
  • big data
  • african american
  • adverse drug