Context Matters in LLM-Assisted Qualitative Data Analysis: Workflow Development and Multidimensional Evaluation in Health Research

Large language models (LLMs) are increasingly used for qualitative analysis, but strong language performance does not guarantee strong interpretation when meaning depends on method and context. We developed a context-specific workflow that translated framework analysis into bounded, sequential LLM-supported tasks and applied it to 20 Chinese-language interviews on fear of dementia (approximately 150,000 characters). Four multilingual LLM-generated outputs and a previous researcher-only analysis were blindly evaluated by 16 relevant experts across different analytical domains and qualitative quality dimensions (496 expert-item records). All LLMs completed the workflow, but rankings varied markedly. Qwen-output showed the most consistently favourable overall profile, while Claude-output was most often ranked first in individual domain–dimension evaluations. The researcher analysis received both highly favourable and highly unfavourable rankings; no output was consistently preferred. LLM-assisted qualitative analysis should be evaluated within its research contexts, with transparent workflows, multidimensional assessment, and continued researcher oversight.