JOURNAL ARTICLE

Put On Your Own Mask First Before Assisting Others: Prioritizing a Collaborative Approach to the Wellness of Student and Academic Affairs Professionals.

  • Published In: Journal of Education Human Resources, 2025, v. 43, n. 1. P. 96 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: González, Carolina E.; Soufleris, Dawn Meza 3 of 3

Abstract

This article examines the increasing mental health needs of college students and the corresponding demands placed on academic and student affairs professionals who support them, particularly in the context of the COVID-19 pandemic. It highlights how student affairs practitioners often serve as frontline responders to student crises, experiencing secondary traumatic stress and burnout that are insufficiently addressed by current employee wellness programs. The article emphasizes the limited institutional capacity and inconsistent implementation of workplace wellness initiatives for higher education staff, advocating for collaborative efforts among academic and student affairs leaders and human resources to develop intentional, evidence-based wellness programs tailored to the unique stressors of these professionals. It also discusses disparities in mental health service utilization among minoritized and first-generation students, underscoring the need for culturally responsive support. Ultimately, the article calls for systemic changes to prioritize the well-being of higher education employees to sustain student success and staff retention.

Additional Information

  • Source:Journal of Education Human Resources. 2025/01, Vol. 43, Issue 1, p96
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2025
  • ISSN:2562-783X
  • DOI:10.3138/jehr-05_Gonzalez_Soufleris_4
  • Accession Number:184529481
  • Copyright Statement:Copyright of Journal of Education Human Resources is the property of University of Toronto Press 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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