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Mitigating Trauma in Qualitative Research Infrastructure: Roles for Machine Assistance and Trauma-Informed Design

Emily Tseng, Thomas Ristenpart, Nicola Dell

TL;DR

The paper addresses how qualitative research can mitigate researchers' traumatic exposure by applying trauma-informed computing (TIC) to create TIQA, a mixed-initiative qualitative coding system. TIQA enables personalized definitions of traumatic concepts and self-management of exposure through embeddings, semantic search, and exposure-tracking interfaces, while prioritizing safety as enablement rather than mere exposure reduction. A formative provocation study with 15 researchers reveals that machine assistance should support reflective practices, peer collaboration, and mindful workload distribution, rather than enforce fast coding. The work argues for evaluating trauma-informedness of the design process itself and emphasizes privacy-preserving, user-controlled approaches to balance well-being with rigorous qualitative analysis, providing a roadmap for trauma-aware infrastructure in qualitative research.

Abstract

Researchers increasingly look to understand experiences of pain, harm, and marginalization via qualitative analysis. Such work is needed to understand and address social ills, but poses risks to researchers' well-being: sifting through volumes of data on painful human experiences risks incurring traumatic exposure in the researcher. In this paper, we explore how the principles of trauma-informed computing (TIC) can be applied to reimagine healthier tools and workflows for qualitative analysis. We apply TIC to create a design provocation called TIQA, a system for qualitative coding that leverages language modeling, semantic search, and recommendation systems to measure and mitigate an analyst's exposure to concepts they find traumatic. Through a formative study of TIQA with 15 participants, we illuminate the complexities of enacting TIC in qualitative knowledge infrastructure, and potential roles for machine assistance in mitigating researchers' trauma. To assist scholars in translating the high-level principles of TIC into sociotechnical system design, we argue for: (a) a conceptual shift from safety as exposure reduction towards safety as enablement; and (b) renewed attention to evaluating the trauma-informedness of design processes, in tandem with the outcomes of designed objects on users' well-being.

Mitigating Trauma in Qualitative Research Infrastructure: Roles for Machine Assistance and Trauma-Informed Design

TL;DR

The paper addresses how qualitative research can mitigate researchers' traumatic exposure by applying trauma-informed computing (TIC) to create TIQA, a mixed-initiative qualitative coding system. TIQA enables personalized definitions of traumatic concepts and self-management of exposure through embeddings, semantic search, and exposure-tracking interfaces, while prioritizing safety as enablement rather than mere exposure reduction. A formative provocation study with 15 researchers reveals that machine assistance should support reflective practices, peer collaboration, and mindful workload distribution, rather than enforce fast coding. The work argues for evaluating trauma-informedness of the design process itself and emphasizes privacy-preserving, user-controlled approaches to balance well-being with rigorous qualitative analysis, providing a roadmap for trauma-aware infrastructure in qualitative research.

Abstract

Researchers increasingly look to understand experiences of pain, harm, and marginalization via qualitative analysis. Such work is needed to understand and address social ills, but poses risks to researchers' well-being: sifting through volumes of data on painful human experiences risks incurring traumatic exposure in the researcher. In this paper, we explore how the principles of trauma-informed computing (TIC) can be applied to reimagine healthier tools and workflows for qualitative analysis. We apply TIC to create a design provocation called TIQA, a system for qualitative coding that leverages language modeling, semantic search, and recommendation systems to measure and mitigate an analyst's exposure to concepts they find traumatic. Through a formative study of TIQA with 15 participants, we illuminate the complexities of enacting TIC in qualitative knowledge infrastructure, and potential roles for machine assistance in mitigating researchers' trauma. To assist scholars in translating the high-level principles of TIC into sociotechnical system design, we argue for: (a) a conceptual shift from safety as exposure reduction towards safety as enablement; and (b) renewed attention to evaluating the trauma-informedness of design processes, in tandem with the outcomes of designed objects on users' well-being.

Paper Structure

This paper contains 46 sections, 3 figures, 4 tables.

Figures (3)

  • Figure 1: Design science research process, following peffers2007design as demonstrated in reinecke2013knowing.
  • Figure 2: Architectural diagram of how TIQA enables users to develop personalized models of their codes using an underlying ML workflow (shaded blue box). See Section \ref{['sec:system-personalize']} for a step-by-step walkthrough, and Table \ref{['tab:tiqa-modules']} for detail on each module. Steps are labeled A1-5, corresponding with their label in the Section \ref{['sec:system-personalize']} walkthrough.
  • Figure 3: Architectural diagrams of how TIQA enables users to self-manage their traumatic exposure using the same underlying ML workflow as in part A (blue box). See Section \ref{['sec:system-trauma']} for a step-by-step walkthrough, and Table \ref{['tab:tiqa-modules']} for detail on each module. Steps are labeled B1-5, corresponding with their label in the Section \ref{['sec:system-trauma']} walkthrough.