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Advancing qualitative analysis in nursing research: Comparing text analysis rigor and efficiency of large language
Yingchun Zeng1, Yanyu Chen2, Jun Yu2
1Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
International Journal of Nursing Studies
|June 3, 2026
Summary
Large language models (LLMs) can aid nursing research by automating qualitative analysis, showing strong alignment with human coders and improving efficiency. Human oversight remains crucial for nuanced interpretation in this hybrid approach.
Area of Science:
- Nursing Research
- Artificial Intelligence in Healthcare
- Qualitative Data Analysis
Background:
- Qualitative data analysis in nursing is time-intensive and prone to bias.
- Large language models (LLMs) offer potential for automating thematic extraction and enhancing consistency.
- LLMs' rigor, alignment with human analysis, and nursing applicability require further investigation.
Purpose of the Study:
- To evaluate LLMs' utility in qualitative descriptive analysis for nursing research.
- To assess the alignment between AI-generated summaries and human-generated descriptive syntheses.
- To propose a hybrid framework combining LLMs and human coding for enhanced rigor and efficiency in analyzing patient accounts.
Main Methods:
- Analysis of 15 semi-structured interviews with postoperative bone tumor patients.
- Comparison of large language model (ChatGPT, DeepSeek) analysis with human-coded analysis by an experienced researcher.
- Assessment of methodological trustworthiness via coding consistency and time-efficiency metrics.
Main Results:
- Both LLMs identified four key themes consistent with human analysis.
- LLM thematic output showed strong overlap with human coding (Cohen's κ = 0.89).
- LLMs substantially reduced analysis time, though discrepancies noted in interpreting emotional cues and analytic scope.
Conclusions:
- LLMs show promise as supplementary tools in qualitative nursing research, boosting efficiency and reducing bias.
- Human expertise is essential for interpreting psychosocial nuances and ensuring contextual relevance.
- A hybrid LLM-human methodology enhances qualitative rigor while preserving a patient-centered approach.
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