Crisis Responders (CRs) rapidly assess thousands of youth SMS conversations each year to identify mental health concerns and guide support. Yet youth distress is increasingly expressed through evolving and context-specific language that often does not fit fixed-label taxonomies. This work analyzed 703,975 de-identified Kids Help Phone conversations (2018-2023) and expanded KHP's 19-label issue taxonomy into a 39-label hierarchical schema. We then introduce Keyphrase Generative Representation (KGR), a constrained LLM generating concise, conversation-specific keyphrases, evaluated across 129 conversations and 387 expert annotations. The expanded taxonomy achieved expert consensus reliability, with an accuracy of 0.96, and expert review found that 81% of keyphrases accurately reflected content and 74% improved clarity. KGR surfaced identity-linked themes absent from the fixed taxonomy, including immigration problems and caregiver burden, and supported a topic-retrieval workflow that increased accuracy from 0.25 to 0.70 (+0.45) over the manual analyst process. KGR marks a shift toward hybrid, interpretable generative representations that extend crisis response beyond static taxonomies to surface emerging and culturally grounded patterns of youth distress.

翻译：危机响应者每年需快速评估数千条青少年短信对话，以识别心理健康问题并指导支持。然而，青少年困扰日益通过不断演变且依赖具体情境的语言表达，这通常不符合固定标签分类体系。本研究分析了2018-2023年间703,975条经过去标识化处理的"儿童帮助热线"对话，将原有的19标签问题分类法扩展为39标签层级架构。我们进一步提出关键词生成式表示——一种受约束的大语言模型，可生成简洁且针对对话的关键词，并在129条对话与387份专家注释中进行了评估。扩展后的分类法达到专家共识可靠性（准确率0.96），专家评审发现81%的关键词准确反映对话内容，74%提升了表述清晰度。KGR揭示了固定分类法未涉及的认同关联主题（包括移民问题与看护者负担），并支持主题检索流程，使准确率相比人工分析过程从0.25提升至0.70（提升+0.45）。KGR标志着向混合型、可解释生成式表示的转变，将危机响应延伸至静态分类法之外，以揭示新兴且植根于文化的青少年困扰模式。