Firearm violence is a pressing public health issue, yet research into survivors' lived experiences remains underfunded and difficult to scale. Qualitative research, including in-depth interviews, is a valuable tool for understanding the personal and societal consequences of community firearm violence and designing effective interventions. However, manually analyzing these narratives through thematic analysis and inductive coding is time-consuming and labor-intensive. Recent advancements in large language models (LLMs) have opened the door to automating this process, though concerns remain about whether these models can accurately and ethically capture the experiences of vulnerable populations. In this study, we assess the use of open-source LLMs to inductively code interviews with 21 Black men who have survived community firearm violence. Our results demonstrate that while some configurations of LLMs can identify important codes, overall relevance remains low and is highly sensitive to data processing. Furthermore, LLM guardrails lead to substantial narrative erasure. These findings highlight both the potential and limitations of LLM-assisted qualitative coding and underscore the ethical challenges of applying AI in research involving marginalized communities.

翻译：枪支暴力是一个紧迫的公共卫生问题，然而对幸存者亲身经历的研究仍面临资金不足且难以规模化。定性研究（包括深度访谈）是理解社区枪支暴力对个人及社会后果并设计有效干预措施的重要工具。然而，通过主题分析和归纳编码手工分析这些叙事既耗时又费力。大语言模型的最新进展为自动化这一流程开辟了可能性，但人们对这些模型能否准确且合乎伦理地捕捉弱势群体经历仍存疑虑。本研究评估了使用开源大语言模型对21名经历过社区枪支暴力的黑人男性访谈进行归纳编码的效果。结果表明，尽管某些配置的LLMs能够识别重要编码，但其整体相关性仍然较低，且高度依赖数据处理方式。此外，LLM的安全护栏机制导致了大量叙事内容的缺失。这些发现既揭示了LLM辅助定性编码的潜力与局限，也凸显了将AI应用于涉及边缘化社区研究中的伦理挑战。