JOURNAL ARTICLE

Sources of Racial-Ethnic Socialization Messages Across Latine Adolescents' Egocentric Social Networks.

  • Published In: Hispanic Journal of Behavioral Sciences, 2025, v. 47, n. 2. P. 221 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Satinsky, Emily N.; Rudrabhatla, Asha; Mateo Santana, Adrelys; Carreon, Eduardo; Iyer, Mythili; Manzur, Andrea; Velasquez, Keyrin; Valente, Thomas W.; Galán, Chardée A. 3 of 3

Abstract

This article focuses on identifying multiple sources of racial-ethnic socialization (RES) messages among Latine adolescents using a novel egocentric social network approach. In a pilot study with 44 Latine youth aged 10–17, participants named important individuals ("alters") across immediate family, extended family, peers, school, and community contexts who provided various RES messages, including preparation for bias, cultural socialization, familism, and immigration socialization. Findings revealed that RES messages come from diverse social network members beyond parents, with adult, Latine, and female alters more frequently providing cultural socialization, while non-Latine alters more often discussed racial-ethnic discrimination. The study demonstrated the acceptability of the social network interview method and highlighted opportunities for strengths-based interventions leveraging multiple RES agents to support Latine adolescents’ racial-ethnic identity development and mental health.

Additional Information

  • Source:Hispanic Journal of Behavioral Sciences. 2025/05, Vol. 47, Issue 2, p221
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:0739-9863
  • DOI:10.1177/07399863251315874
  • Accession Number:185066911
  • Copyright Statement:Copyright of Hispanic Journal of Behavioral Sciences is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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