Psychological Well‐Being of Female High School Students: Effectiveness of Transactional Analysis.

  • Published In: Journal of Child & Adolescent Psychiatric Nursing, 2025, v. 38, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Talebi Siavashani, Fatemeh; Bazrafshan, Fatemeh; Javid, Mehravar; Hedayatollahnajafi, Elaheh 3 of 3

Abstract

Background: Adolescence is a critical developmental stage, characterized by personal challenges and transitions, highlighting the importance of psychological well‐being in maintaining positive mental health. Purpose: This study aimed to evaluate the impact of transactional analysis (TA) training in improving psychological well‐being among adolescent girls. Methodology: A quasi‐experimental study employed convenience sampling with 30 seventh‐grade female high school students (M_age = 13.5). The participants were randomly assigned to either an experimental group receiving eight sessions of TA (n = 15) or a waitlist control group (n = 15). Both groups completed the short form version of Ryff's Psychological Well‐Being Scale pre‐ and post‐intervention. Results: TA significantly improved psychological well‐being in the experimental group compared to the control group (F = 4.68, p = 0.04). Conclusion: The findings suggest that TA is an effective intervention for enhancing adolescent psychological well‐being, highlighting its potential as a valuable mental health strategy. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Child & Adolescent Psychiatric Nursing. 2025/05, Vol. 38, Issue 2, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:1073-6077
  • DOI:10.1111/jcap.70020
  • Accession Number:185490625
  • Copyright Statement:Copyright of Journal of Child & Adolescent Psychiatric Nursing 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.