Exploring Recovery through Life Narratives in Psychiatric Home-Visit Nursing: A Natural Language Processing Approach Using BERTopic

Background: In mental health, recovery is emphasized, and qualitative analyses of service users’ narratives have accumulated; however, while qualitative approaches excel at capturing rich context and generating new concepts, they are limited in generalizability and feasible data volume. This study aimed to quantify the subjective life history narratives of users of psychiatric home-visit nursing using natural language processing (NLP) and to clarify the relationships between linguistic features and recovery-related indicators. Methods: We conducted audio-recorded and transcribed semi-structured interviews on daily life verbatim and collected self-report questionnaires (Recovery Assessment Scale [RAS]) and clinician ratings (Global Assessment of Functioning [GAF]) from Japanese users of psychiatric home-visit nursing. Using the artificial intelligence-based topic-modeling method BERTopic, we extracted topics from the interview texts and calculated each participant’s topic proportions, and then examined associations between topic proportions and recovery-related indicators using Pearson correlation analyses. Results: “School” showed a significant positive correlation with RAS (r = 0.39, p = 0.05), whereas “Family” showed a significant negative correlation (r = –0.46, p = 0.02). GAF was positively correlated with word count (r = 0.44, p = 0.02) and “Hospital” (r = 0.42, p = 0.03), and negatively correlated with “Backchannels” (aizuchi) (r = –0.41, p = 0.03). Conclusion: The present results suggest that the quantity, quality, and content of narratives can serve as useful indicators of mental health and recovery, and that objective NLP-based analysis of service users’ narratives can complement traditional self-report scales and clinician ratings to inform the design of recovery-oriented care in psychiatric home-visit nursing.