Women's academic leadership in STEM: a systematic literature review on challenges, opportunities and strategies.
Saved in:
| Title: | Women's academic leadership in STEM: a systematic literature review on challenges, opportunities and strategies. |
|---|---|
| Authors: | Pereira, Leihge Roselle Rondon (AUTHOR), Maciel, Cristiano (AUTHOR), Guzman, Indira Rita (AUTHOR) |
| Source: | Studies in Higher Education. Jun2026, Vol. 51 Issue 6, p1375-1388. 14p. |
| Subjects: | Leadership in women, Gender inequality, Universities & colleges, STEM occupations, Educational equalization |
| Abstract: | This study examines the intersection of gender equity and inclusion in education through a Systematic Literature Review of academic articles and conference proceedings focused on women's leadership in Science, Technology, Engineering, and Mathematics (STEM). The studies were collected in three languages. Portuguese, as the official language of the country where the study was developed; Spanish, because it covers countries in Latin America; and English, because it is the predominant language in global scientific literature. The review identified studies made it possible to define twenty categories of challenges, nine categories of opportunities associated with women's leadership in STEM, and sixteen categories of strategies to support their development within the field. Findings highlight the critical need for cultural and structural reforms, alongside the implementation of targeted policies within academic institutions, to foster equitable opportunities, enhance recognition, and promote women into leadership positions in higher education. These insights emphasize the importance of sustained efforts to advance gender equity and inclusivity in STEM academic leadership. [ABSTRACT FROM AUTHOR] |
| Copyright of Studies in Higher Education is the property of Taylor & Francis Ltd 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.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
