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Gender Differential Analysis of Closeness, Dependency and Anxiety Attachment Styles among College Going Students in Punjab.

  • Published In: Indian Journal of Health & Wellbeing, 2025, v. 16, n. 2-I. P. 226 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Romanpreet; Prachi, Bisht; Seema, Sharma 3 of 3

Abstract

This study investigates gender differences in attachment styles among college students in Punjab, focusing on closeness, dependency, and anxiety in relationships with family, romantic partners, and close friends. The sample comprised 420 undergraduate students, equally divided between males and females, aged 18-22. Results indicate significant gender disparities in attachment styles across different relational contexts. Males reported higher levels of closeness in family and romantic relationships, while females exhibited greater dependency and anxiety in familial settings. No significant gender differences were observed in attachment styles with close friends, suggesting that friendships serve as an equalizing domain for emotional connection. These findings highlight the influence of traditional gender roles and cultural norms on relationship behaviors, emphasizing the need for targeted interventions to address these disparities. Future research should explore the longitudinal changes in attachment styles and their broader implications for relational and mental health in the Indian context. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Indian Journal of Health & Wellbeing. 2025/06, Vol. 16, Issue 2-I, p226
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2025
  • ISSN:2229-5356
  • Accession Number:186759223
  • Copyright Statement:Copyright of Indian Journal of Health & Wellbeing is the property of Indian Association of Health, Research & Welfare 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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