心理健康支持热线作为容量有限的服务：需求、评估与未评估呼叫的联合建模

中文摘要 AI 辅助

心理健康支持热线实时记录求助呼叫尝试，然而有限的服务容量仅允许一小部分呼叫被接听和评估。每次尝试的时间均被记录，而问题类型及呼叫者人口统计特征和危机严重程度等标记仅对已评估的呼叫可见。呼叫者的匿名性也阻止了将重复尝试关联到同一人。尽管如此，服务规划和危机监测需要估计未评估的需求、恢复呼叫内容的时变模式，并量化部分观测的标记在多大程度上改善这些估计。我们开发了一种联合标记动态因子模型（JM-DFM），其中共享的低维潜在状态驱动尝试强度、问题构成和混合类型标记分布。我们建立了可识别性，证明了所提出的筛子估计量的一致性，并表明纳入标记可提高渐近估计精度。该框架随后应用于中国国家12356心理健康支持热线的68,371次呼叫尝试记录。我们估计每天约有35次未评估的尝试涉及自杀意念，其比例高于已评估呼叫中的比例。纳入标记将潜在状态不确定性带收窄了18%至49%，在问题标记呼叫较少的时期收窄幅度最大。

英文摘要

Mental health support hotlines log help-seeking call attempts in real time, yet limited service capacity allows only a small proportion of calls to be answered and assessed. The time of every attempt is recorded, whereas issue types and marks such as caller demographics and crisis severity are observed only for assessed calls. Caller anonymity also prevents linking repeated attempts to the same individual. Nevertheless, service planning and crisis monitoring require estimating unassessed demand, recovering temporal patterns in call content, and quantifying how much the partially observed marks improve these estimates. We develop a Joint Marked Dynamic Factor Model (JM-DFM) in which a shared low-dimensional latent state drives attempt intensity, issue composition, and mixed-type mark distributions. We establish identifiability, prove consistency of the proposed sieve estimator, and show that incorporating marks improves the asymptotic estimation precision. The proposed framework is then applied to records of 68,371 call attempts to China's national 12356 mental health support hotline. We estimate that about 35 unassessed attempts per day involve suicidal ideation, representing a higher proportion than among assessed calls. Incorporating the marks narrows the latent-state uncertainty bands by 18% to 49%, most during periods with few issue-labeled calls.

发表机构

• Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University（华东师范大学统计学院）
• Faculty of Business and Economics, The University of Hong Kong（香港大学商学院）

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