JOURNAL ARTICLE

Interdisciplinary Approaches to LGBTQ+ Inclusion in Education: A Qualitative Study from Slovenia.

  • Published In: Journal of Applied Social Sciences (19367244), 2025, v. 19, n. 1. P. 30 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Ferbežar, Nika; Štefanec, Vesna; Gavriloski Tretjak, Marko; Kopinič, Alja 3 of 3

Abstract

This article examines the need for interdisciplinary collaboration to improve the well-being of LGBTQ+ youth within the Slovenian education system. Based on qualitative interviews with 27 LGBTQ+ individuals, it identifies four key areas for intervention: creating a supportive educational climate with respectful LGBTQ+ representation, promoting mental health and preventing disorders, implementing inclusive sexuality education, and addressing homelessness among LGBTQ+ youth. The study highlights that educational institutions often lack the knowledge, resources, or motivation to adequately support LGBTQ+ students, and suggests collaboration with mental health professionals, LGBTQ+-specific non-governmental organizations (NGOs), health services, and social care providers as pragmatic short-term solutions. It calls for systemic, multi-stakeholder approaches involving educators, families, and community organizations to foster safer, more inclusive educational environments and recommends further research including diverse perspectives and longitudinal analysis.

Additional Information

  • Source:Journal of Applied Social Sciences (19367244). 2025/03, Vol. 19, Issue 1, p30
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2025
  • ISSN:1936-7244
  • DOI:10.1177/19367244241269858
  • Accession Number:182848866
  • Copyright Statement:Copyright of Journal of Applied Social Sciences (19367244) 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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