Associations Between Nursing Faculty Expertise in the United Nations Sustainable Development Goals and Research Impact Metrics: A Cross‐Sectional Study
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Suebsarn Ruksakulpiwat, Witchuda Thongking, Atsadaporn Niyomyart, Chitchanok Benjasirisan, Lalipat Phianhasin, Ruttanaporn Kongkar, Nattaya Praha, Jon Adams, Austen El-Osta
Publisher: Wiley
Publication year: 2026
Start page: 1
End page: 12
Number of pages: 12
ISSN: 0966-0429
eISSN: 1365-2834
Languages: English-United States (EN-US)
Abstract
Background
The United Nations Sustainable Development Goals (SDGs) offer a comprehensive global framework for promoting health, equity, and sustainability. Whereas alignment with the SDGs is increasingly encouraged in academic institutions, the extent to which faculty expertise in SDGs influences traditional research impact metrics remains insufficiently explored.
Objective
To investigate the relationship between nursing faculty expertise in SDGs and research impact metrics.
Methods
A retrospective cross-sectional design was employed using data from 121 nursing faculty members at Mahidol University, Thailand. Information on SDG-related expertise and research performance was obtained from the Mahidol University Research Excellence Database (MUREX) and Scopus. SDG expertise was operationalized using SDG alignment data derived from the Scopus Author Profile, which applies machine learning and keyword-based text mining to map publications to the 17 SDGs. Descriptive statistics, Pearson’s correlation, and multiple linear regression analyses were used to examine associations between SDG expertise, academic experience, and research impact metrics, including H-index, citation count, and research output. Extreme Gradient Boosting (XGBoost) was applied as a complementary machine learning approach to identify influential features and potential nonlinear patterns, with the Synthetic Minority Oversampling Technique (SMOTE) used to address imbalance in categorical SDG expertise classes.
Results
Faculty members with greater expertise in SDGs demonstrated significantly higher research impact metrics. SDG expertise significantly predicted H-index (β = 0.65, p < 0.001), total citations (β = 31.78, p = 0.004), and total research output (β = 2.41, p < 0.001). Research experience was also a significant predictor of research impact. Machine learning analyses identified SDG expertise breadth and international collaboration as influential features, and faculty aligned with SDG13 (Climate Action) demonstrated a higher proportion of top-cited publications.
Conclusion
SDG expertise is a key determinant of academic impact, reinforcing the need for greater institutional support for SDG-aligned research. Interdisciplinary collaboration and engagement with broader sustainability challenges may enhance faculty research visibility. Future research should explore longitudinal trends and policy implications for integrating SDGs into faculty assessment frameworks.
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