Understanding nursing students' awareness of the 2030 sustainable development goals and perceptions of the global future: A comparative and explainable machine learning analysis

Bodur G., Göktaş P., Küçükkaya A., Turhan Z., Altun Y. E.

NURSE EDUCATION TODAY, cilt.1, sa.1, ss.1-12, 2026 (SCI-Expanded, SSCI, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.nedt.2026.107493
  • Dergi Adı: NURSE EDUCATION TODAY
  • Derginin Tarandığı İndeksler: Social Science Premium Collection (ProQuest), Education Collection (ProQuest), Education Source Ultimate (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Agricultural & Environmental Science Database, Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), CINAHL, Education Abstracts, Educational research abstracts (ERA), EMBASE, EBSCO Education Source, MEDLINE
  • Sayfa Sayıları: ss.1-12
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

Aims

This study aimed to examine nursing students' awareness of the 2030 Sustainable Development Goals (SDGs) and their perceptions of the global future, and to identify key predictors of sustainability awareness using explainable artificial intelligence (XAI)-based analytical methods.

Background

Sustainability has become a central component of global health systems, requiring future nurses to develop competencies that extend beyond traditional clinical roles. However, existing evidence suggests that sustainability education in nursing remains fragmented, and the relationship between SDG awareness and global future perceptions is not yet fully understood.

Design

Descriptive and correlational study with a XAI framework.

Methods

Data were collected from 229 nursing students. SDG awareness and global future perceptions were measured using validated scales. Conventional statistical analyses (t-test, ANOVA, correlation) were performed alongside machine learning models, including CatBoost, Random Forest, and KNN. Model performance was evaluated using R2, MAE, and RMSE withhold-out and 5-fold cross-validation. Feature importance and SHAP analyses were conducted to interpret model outputs.

Results

Students demonstrated high SDG awareness (166.27 ± 11.19) and moderately positive global future perceptions (137.20 ± 22.46). A low but significant positive correlation was found between the two constructs (r = 0.14, p < 0.05). The CatBoost model showed the best generalizable performance in predicting SDG awareness (CV R2 = 0.439). Key predictors included accommodation status, family income, SDG knowledge, and academic year. In contrast, global future perceptions were not meaningfully predicted by the models.

Conclusion

Sustainability awareness among nursing students is shaped by both contextual and educational factors, whereas global future perceptions appear more complex and less predictable. XAI-based approaches provide valuable insights for designing more effective and responsive sustainability-oriented nursing education programs.

Keywords

Sustainable Development Goals
; 
Nursing students
; 
Sustainability
; 
Future perception
; 
Explainable artificial intelligence
; 
Machine learning
; 
Nursing education
; 
AI