Treffer: Structuring higher-order thinking: a national analysis of learning outcomes in Swedish undergraduate nursing thesis courses.

Background: Higher-order thinking is a central objective in nursing education, particularly within thesis courses where students are expected to demonstrate analytical reasoning and scholarly autonomy.
Aim: The aim of this study is to examine the structure, cognitive complexity, and knowledge domain classification of learning outcomes in degree project courses within Swedish undergraduate nursing education.
Methods: This national cross-sectional study examined the cognitive structure of 236 intended learning outcomes derived from 23 universities and university colleagues offering undergraduate nursing thesis courses across all Swedish higher education institutions (N = 25). Active verbs were extracted and analyzed using manifest content analysis, descriptive statistics, Mann-Whitney U tests, and Spearman's rank correlation.
Results: Using Bloom's revised taxonomy as the analytical framework, we identified 58 unique active verbs. All institutions included outcomes at multiple taxonomic levels, with "Analyzing" and "Applying" most frequently used. In contrast, "Understanding" was rarely represented, despite its foundational role in cognitive progression. Lexical diversity and alignment with higher-order thinking varied significantly across institutions. One-third of the verbs were not included in Bloom's taxonomy, highlighting the interpretive challenges in applying taxonomic models to curriculum analysis.
Conclusion: These findings suggest divergent pedagogical assumptions underlying outcome design and underscore the need for more coherent, epistemologically informed approaches to ensure thesis courses truly support academic development. The results may inform quality assurance practices and contribute to ongoing debates about the role of research training in undergraduate nursing education.
(© 2025. The Author(s).)

Declarations. Ethical approval and consent to participate: Not applicable. Consent for publication: Not applicable. AI declaration: Use of large language models (LLMs) in statistical calculations and visualization is described in Sect. “Use of AI tools for statistical calculations and visualization”. During multiple stages of this study ChatGPT-4o, ChatGPT-4.5, ChatGPT-o1 (April 2025), was employed as a coding assistant to develop Python scripts for data processing and visualization. Code was generated iteratively in collaboration with the authors via Google Colab, and targeted tasks included cleaning and structuring Excel-derived datasets, computing descriptive and inferential statistics (e.g., Spearman’s ρ), and rendering figures (e.g., Bloom category distributions, institutional typologies). All AI-generated code was manually reviewed, edited, and executed by the authors to ensure computational validity, reproducibility, and alignment with the study’s analytical aims. No statistical inference or interpretive output was directly accepted without verification. Additionally, ChatGPT was used for linguistic polishing, reformulation and reorganization of paragraphs for clarity, and condensation of dense sections, without altering the original argumentation, epistemic stance, or analytical conclusions. The final manuscript reflects the authors’ conceptual design, analytic decisions, and scholarly interpretation. All AI contributions were non-autonomous and subject to critical human oversight. Competing interests: The authors declare no competing interests.