Clinical Data Mining With the Listening Guide: An Approach to Narrative Big Qual

Abstract

We developed a novel approach to narrative Big Qual research that combines Carol Gilligan and Lyn Mikel Brown’s Listening Guide with Irwin Epstein’s clinical data mining. We adapted the voice-based research methodology of the Listening Guide for use with a corpus of clinical case notes drawn from an integrated data system (IDS) of a social service intervention serving families in an immigrant enclave. This methodological innovation was inspired by the insight that the Listening Guide can be used to trace and name the layering of meaning within any narrative, whether that narrative reflects the experience of an individual person or, as in this case, the community and everyday life of a social service intervention. Critically, this approach pivots on theorizing the subject as the collective of the intervention itself, as narrated by case managers, who can be understood as narrating subjects within the cultural, figured world of the intervention. In the context of a larger process and outcome evaluation, marrying these two approaches provided context, texture, and depth to supplement existing data sources like self-report survey data and participant observation, and offered a glimpse inside the “black box” of the intervention. We adapted the Guide through three readings of the clinical case notes: once for stanza structure, once inspired by the I-Poem technique but modified for these third-person narratives, and once with an eye to the contrapuntal voices of the inner and outer worlds of the intervention. As a methodological innovation this approach represents an advance in Big Qual and a promising approach to conducting narrative research on large qualitative data sets within mixed methods studies.