ArticleJournal of nursing management2026
Associations Between Nursing Faculty Expertise in the United Nations Sustainable Development Goals and Research Impact Metrics: A Cross-Sectional Study.
Article in Journal of nursing management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Associations Between Nursing Faculty Expertise in the United Nations Sustainable Development Goals and Research Impact Metrics: A Cross-Sectional Study.Journal of nursing management · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundThe 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.
objectiveTo investigate the relationship between nursing faculty expertise in SDGs and research impact metrics.
methodsA 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.
resultsFaculty 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.
conclusionSDG 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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