Gender differences in Russian adolescent mental health from 1999 to 2021.

  • Published In: Journal of Research on Adolescence (Wiley-Blackwell), 2024, v. 34, n. 1. P. 222 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Privodnova, Evgeniya Yu; Semenova, Nadezhda B.; Kornienko, Olga S.; Varshal, Aleksandra V.; Slobodskaya, Helena R. 3 of 3

Abstract

This study examined secular trends in Russian adolescent mental health, the specific effects of the COVID‐19 pandemic, and associations with country‐level indicators. A cross‐sectional survey of 12,882 adolescents aged 11–18 years was carried out between 1999 and 2021 using the Strengths and Difficulties Questionnaire. The results showed an incline in girls' internalizing problems with a two‐fold increase in the gender gap. There was a decline in girls' prosocial behavior and an incline in peer problems, with decreasing gender differences. Conduct problems showed a reversal of gender differences. Changes during the pandemic were not greater than over‐time changes, with the exception of inclines in hyperactivity‐inattention in both genders. Time trends in adolescent mental health were associated with over‐time changes in national indicators of wealth and gender equality. The findings provide a strong basis for further research into the determinants of gender differences in adolescent mental health and for gender‐specific interventions. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Research on Adolescence (Wiley-Blackwell). 2024/03, Vol. 34, Issue 1, p222
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2024
  • ISSN:1050-8392
  • DOI:10.1111/jora.12911
  • Accession Number:175502431
  • Copyright Statement:Copyright of Journal of Research on Adolescence (Wiley-Blackwell) is the property of Wiley-Blackwell 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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