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.

翻译：研究人员日益希望通过质性分析来理解痛苦、伤害与边缘化的经历。此类工作对于理解和解决社会弊病至关重要，但也对研究者的福祉构成风险：梳理大量关于人类痛苦经历的数据，可能导致研究者遭受创伤性暴露。本文探讨了如何应用创伤知情计算的原则，重新构想更健康的质性分析工具与工作流程。我们应用创伤知情计算创建了一个名为TIQA的设计构想，这是一个质性编码系统，利用语言建模、语义搜索和推荐系统来测量并减轻分析员对其认为具有创伤性的概念的暴露。通过对15名参与者进行的TIQA形成性研究，我们阐明了在质性知识基础设施中实施创伤知情计算的复杂性，以及机器辅助在缓解研究者创伤方面的潜在作用。为帮助学者将创伤知情计算的高层原则转化为社会技术系统设计，我们主张：（a）概念转变，从将安全视为减少暴露转向将安全视为赋能；（b）重新关注对设计过程的创伤知情程度评估，同时关注设计成果对用户福祉的影响。