ScholarWorks@숙명여자대학교

초록

This study compares text mining analysis with consensual qualitative research (CQR) to gain a multidimensional understanding of the psychotherapy experiences of youth transitioning out of care and to explore the complementary perspectives offered by human-driven qualitative analysis and machine-based text analysis. In-depth interview data were collected from 10 care-leavers (three males and seven females) who had participated in psychotherapy after being discharged from out-of-home care. TF-IDF analysis and Latent Dirichlet Allocation(LDA) topic modeling were conducted on the transcripts and systematically compared with findings from a previous CQR study. A high degree of convergence was revealed between the two methods in identifying core themes that characterized clients’ experiences. Both approaches highlight emotional difficulties, relational and daily-life stressors, interactions with therapists, and experiences of emotional support. Notably, the comparative CQR-text mining analysis demonstrated the differential contributions of the two methodologies to the same dataset. While CQR captured participants’ subjective meanings and depth of experience, revealing nuanced qualitative changes in therapeutic relationships and internal reflection processes, text mining approaches objectively identified structural conditions, such as the number of counseling sessions and support modalities, as well as contextual differences related to the elapsed time since care termination, based on systematic patterns in the textual data. This suggests that interpretation-centered approaches and data-driven pattern analysis can complement and cross-validate each other, thereby expanding the integrated understanding of the same phenomenon. These findings indicate that qualitative and text-based pattern analyses information at different levels and can function complementarily.

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