Advancing qualitative analysis in nursing research: Comparing text analysis rigor and efficiency of large language models with human-coded analysis for interview data

BACKGROUND: Qualitative data analysis in nursing research remains labor-intensive and vulnerable to researcher bias. While large language models offer transformative potential for automating thematic extraction and improving analytical consistency, their methodological rigor, alignment with human analysis, and applicability to nursing contexts remain underexplored. AIM: This study examined whether large language models can assist qualitative descriptive analysis by generating preliminary, data-near summaries of participants' accounts and whether these AI-generated outputs align with human-generated descriptive syntheses. Using kinesiophobia in postoperative bone tumor patients as a case study, we propose a triangulated framework that combines large language models and human coding to enhance analytical rigor and efficiency. METHODS: Semi-structured interviews (N = 15) with postoperative bone tumor patients were analyzed using two approaches: (1) large language model analysis via ChatGPT and DeepSeek; and (2) human-coded analysis by an experienced qualitative researcher. Methodological trustworthiness was assessed through coding consistency and time-efficiency metrics. RESULTS: Both large language models, aligned with the human analyst, identified four common themes: (1) Disease and treatment experiences; (2) Mind-body dynamics in rehabilitation; (3) Utilization of health education; and (4) Roles of family support. The thematic output of the large language models showed strong overlap with the human-coded analysis (Cohen's κ = 0.89) while substantially reducing coding time. Remaining discrepancies may reflect differences in interpreting implicit emotional cues, variation in analytic focus and scope between human and model outputs, and the potential illusion of model understanding. CONCLUSION: Large language models hold promise as valuable supplementary tools in qualitative nursing research, improving efficiency and reducing potential bias of human-coded analysis. Yet, human expertise remains essential for interpreting psychosocial nuances and ensuring contextual relevance. This study introduces a hybrid large language model-human methodology, enhancing qualitative rigor while maintaining the patient-centered ethos of nursing. Future research should assess the scalability of this approach across diverse study populations.