Associations Between Nursing Faculty Expertise in the United Nations Sustainable Development Goals and Research Impact Metrics: A Cross-Sectional Study
Issued Date
2026-01-01
Resource Type
eISSN
13652834
Scopus ID
2-s2.0-105035036162
Pubmed ID
41943903
Journal Title
Journal of Nursing Management
Volume
2026
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Nursing Management Vol.2026 No.1 (2026) , e9740644
Suggested Citation
Ruksakulpiwat S., Thongking W., Niyomyart A., Benjasirisan C., Phianhasin L., Kongkar R., Praha N., Adams J., El-Osta A. Associations Between Nursing Faculty Expertise in the United Nations Sustainable Development Goals and Research Impact Metrics: A Cross-Sectional Study. Journal of Nursing Management Vol.2026 No.1 (2026) , e9740644. doi:10.1155/jonm/9740644 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116175
Title
Associations Between Nursing Faculty Expertise in the United Nations Sustainable Development Goals and Research Impact Metrics: A Cross-Sectional Study
Corresponding Author(s)
Other Contributor(s)
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.