Using Large Language Models to Analyze Factors Influencing Academic Supervision Relationships in Qualitative Interviews With Postgraduate Nursing Students.

Background: Nursing postgraduate supervisors serve as educators, mentors, and research facilitators, ensuring the holistic development of postgraduate students to meet the evolving demands of nursing care.

Purpose: This study explored factors influencing academic supervision relationships (playing a critical role in the academic and professional development of students). It innovatively applied 2 large language models (LLMs) to analyze qualitative interviews with postgraduate nursing students.

Methods: Data were collected through semi-structured interviews with 14 nursing graduate students, and 2 LLMs widely used in China were used for interview transcript analysis.

Results: The themes extracted by 2 LLM models were highly consistent and can be grouped into 4 main categories: (1) academic supervisor-related factors: supervisory style, personal traits, leadership style, and research capabilities/resources; (2) student-related factors: independence, initiative, and career expectations; (3) academic supervisor-student interaction: communication frequency and quality and shared goals; and (4) environmental factors: academic environment and team culture.

Conclusion: Active communication, clear role expectations, and cooperation optimize supervisory relationships, enhancing nursing training and research.