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

使用自然语言处理 (NLP) 模型预测治疗师文化能力

• 批准号: 9906653
• 负责人: Patty Beyrong Kuo
• 金额: $ 4.5万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-12 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906653
• 关键词: Address; Alcohol or Other Drugs use; Anxiety; Awareness; Behavior; Caring; Characteristics; Client; Clinical; Code; Complex; Computerized Medical Record; Data; Discrimination; Empathy; Evidence based treatment; Face; Feedback; Foundations; Funding; Goals; Grant; Health Personnel; Healthcare; Individual; Judgment; Label; Language; Lesbian Gay Bisexual Transgender Queer; Machine Learning; Measures; Mental Depression; Mental Health; Methods; Modeling; National Institute on Alcohol Abuse and Alcoholism; Natural Language Processing; Outcome; Patients; Pattern; Perception; Performance; Process; Provider; Psychotherapy; Quality of Care; Reporting; Research; Research Personnel; Suicide; Technology; Text; Therapeutic; Training; Treatment outcome; Work; base; commercial application; community setting; cultural competence; cultural values; disparity reduction; effective intervention; ethnic minority population; experience; health care disparity; improved; psychologic; psychological distress; racial and ethnic; sexual identity; showing emotion; substance abuse treatment; symptomatic improvement; tool; treatment disparity; university student; willingness

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.

项目总结 少数族裔(REM)和女同性恋、男同性恋、双性恋、变性人和同性恋者(LGBTQ) 经历高度的心理痛苦。心理治疗可以有效地解决 心理健康问题，但护理质量方面的差距仍然存在。虽然体制和体制因素 在造成护理不平等的情况下，精神卫生提供者也是审查的关键。努力的主要焦点 为了了解和减少提供者对精神卫生保健差异的贡献，一直以来都在审查文化 能力(CC)，它涉及提供者在临床互动的文化方面的导航能力。 CC的患者评分通常与治疗结果和治疗过程有关。而当 患者对提供商CC的看法很重要，对回顾患者评级的依赖限制了我们 了解文化认同是如何被讨论的，以及构成文化敏感关怀的语言。 许多对提供者CC的研究也要求观察者或患者根据内部 不可靠地观察到的提供商特征(例如，评级提供商对其自身文化的认识 值)。需要更多的研究来检查治疗中的患者与提供者的互动，以便评估 特定提供商行为的影响，以及它们与提供商CC的认知之间的关系。最近，自然 语言处理(NLP)模型已被应用于心理治疗对话，以自动 掌握循证治疗、谈话话题、同理心和情感表达的使用。 先前的研究证明了在心理治疗中自动识别谈话话题的可行性 建议对NLP模型进行培训，以自动识别会话中的特定时刻 患者和医疗服务提供者都在谈论文化问题。自然语言处理模型可以让研究人员不仅 研究特定的提供者-患者互动模式如何推动CC，但也可能提供快速反馈 向提供者提供服务，进而帮助解决护理方面的差距。目前这项研究的目的是为了 开发和评估NLP工具以捕捉提供者-患者的文化内容的基础工作 REM和LGBTQ患者之间的相互作用。首先，利用200次心理治疗中的32,436次标记谈话 我们将评估NLP模型在识别关于文化主题的讨论方面的准确性 心理治疗。其次，我们将使用NLP模型来探索1235项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。