Prediction of therapist cultural competency using Natural Language Processing (NLP) models

项目摘要 种族少数群体（REM）和女同性恋，男同性恋，双性恋，变性人和酷儿（LGBTQ）个人 经历了严重的心理困扰心理治疗可以有效地解决 心理健康问题，但在护理质量方面仍然存在差距。虽然制度和体制因素 有助于照顾的差距，心理健康提供者也是至关重要的检查。工作的主要重点 为了了解和减少提供者对精神卫生保健差异的贡献， 能力（CC），涉及提供者在临床互动的文化方面导航的能力。 CC的患者评级通常与治疗结果和治疗过程相关。而 患者对提供者CC的看法很重要，依赖回顾性患者评级限制了我们 了解如何讨论文化特性，以及构成文化敏感护理的语言。 许多关于提供者CC的研究还要求观察者或患者根据内部判断做出复杂的判断。 无法可靠观察到的供应商特征（例如， 价值观）。需要更多的研究来检查患者与提供者在治疗中的互动，以评估 具体的供应商行为的影响，以及它们如何与供应商CC的看法。最近，自然 语言处理（NLP）模型已被应用于心理治疗对话， 捕捉使用循证治疗，谈话的话题，同情，和情感表达。 先前的研究证明了在心理治疗中自动识别谈话主题的可行性 建议NLP模型可以被训练来自动识别会话中的特定时刻， 患者和供应商正在讨论文化问题。NLP模型不仅可以让研究人员 检查提供者-患者交互的特定模式如何驱动CC，但也可能提供快速反馈 提供者，并反过来帮助解决护理中的差异。本研究的目的是为了 开发和评估NLP工具的基础工作，这些工具可以捕捉提供者-患者的文化内容 REM和LGBTQ患者之间的相互作用。首先，利用200个心理治疗中的32，436个标记谈话 我们将评估NLP模型在识别文化主题讨论中的准确性， psychotherapy.其次，我们将使用NLP模型来探索1，235种心理治疗内容的差异 在文化能力方面被评为高度积极或消极的会议。

PROJECT SUMMARY Racial-ethnic minorities (REM) and lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals experience high levels of psychological distress. Psychological treatments can be effective in addressing mental health concerns, but disparities in quality of care still exist. Although systemic and institutional factors contribute to disparities in care, mental health providers are also critical to examine. A primary focus of efforts to understand and reduce provider contributions to mental health care disparities has been to examine cultural competency (CC), which involves a provider’s ability to navigate the cultural aspects of clinical interactions. Patient ratings of CC are generally associated with treatment outcomes and therapeutic processes. While patient perceptions of provider CC are important, a reliance on retrospective patient ratings limits what we know about how cultural identities are discussed, and the language that constitutes culturally sensitive care. Many studies of provider CC also require observers or patients to make complex judgments based on internal provider characteristics that are not reliably observable (e.g. rate provider awareness of their own cultural values). More studies are needed that examine patient-provider interactions in treatment in order to assess the impact of specific provider behaviors, and how they relate to perceptions of provider CC. Recently, Natural Language Processing (NLP) models have been applied to psychotherapy conversations to automatically capture the use of evidence based treatments, topics of conversation, empathy, and emotional expression. Prior research demonstrating the feasibility of automatically identifying topics of conversation in psychotherapy suggest that NLP models could be trained to automatically identify specific moments in sessions where patients and providers are talking about cultural issues. NLP models could allow researchers to not only examine how specific patterns of provider-patient interactions drive CC, but might also provide rapid feedback to providers, and in turn help address disparities in care. The purpose of the current study is to do the foundational work to develop and evaluate NLP tools that capture the cultural content of provider-patient interactions among REM and LGBTQ patients. First, utilizing 32,436 labeled talk turns from 200 psychotherapy sessions we will evaluate the accuracy of NLP models in recognizing the discussion of cultural topics in psychotherapy. Second, we will use NLP models to explore differences in the content of 1,235 psychotherapy sessions that were rated as highly positive or negative on a measure of cultural competence.