Technology Facilitated Training for Mental Health Counseling

数百万美国人被诊断出患有精神健康或药物滥用问题。虽然像心理治疗和其他形式的咨询这样的对话干预是最有效的治疗选择之一，但只有不到一半的人需要得到照顾。一个问题是，很难根据对治疗师工作的直接观察，为他们提供定期的反馈。因此，培训治疗师既昂贵又耗时，导致专家咨询师短缺。此外，由于难以获得护理，患者转向在线支持来源，而这些来源的质量可能难以确定。这个项目探讨了如何利用技术来提高各级心理健康咨询师的培训这一及时的问题。该项目将开发一种新型的智能辅导系统，以利用自然语言处理的发展，并促进学员之间的合作，以提高学习效果。经过更好培训的治疗师和心理健康论坛上更好的互动应该提高每个人心理健康护理的质量和及时性。此外，这项工作将支持持牌专业心理健康辅导员的终身和协作学习，该项目将开发和评估技术工具，促进培训未来心理健康工作人员的新模式。具体来说，这将涉及创建和研究一个新的基于文本的平台，目标是培训心理健康咨询师。在该平台中，两个广泛的研究问题包括调查（a）自然语言处理驱动的助手的影响，这些助手提供真实的反馈，以及（B）使用接受过最少培训的个人进行众包咨询。为此，将设计和训练几个统计模型，使其在拟议的基于文本的平台内运行，与新手治疗师互动。两种反馈的有效性（自动化和基于人群的）在培训不同类型的学员方面（在线论坛中的外行支持提供者，培训中的新手治疗师）将与学习者自己练习的模型进行比较，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

Millions of Americans have been diagnosed with mental health or substance abuse problems. While conversational interventions like psychotherapy and other forms of counseling are among the most effective treatment options available, less than half of those in need receive care. One problem is that it is difficult to provide therapists in training with regular feedback based on direct observation of their work. Accordingly, training therapists is expensive and time consuming, leading to a shortage of expert counselors. Furthermore, due to difficulties in obtaining care, patients are turning to online sources of support, where quality may be difficult to ascertain. This project examines this timely question of how to use technology to improve the training of mental health counselors at all levels. This project will develop a novel intelligent tutoring system to capitalize on developments in natural language processing and also facilitate collaboration among trainees to enhance learning. Better trained therapists and improved interactions on mental health forums should improve the quality and timeliness of mental health care for everyone. Furthermore, this work will support lifelong and collaborative learning for licensed professional mental health counselors.This project will develop and evaluate technological tools that facilitate new models for training tomorrow's mental health workforce. Specifically, this will involve creating and studying a novel text-based platform with the goal of training mental health counselors. Within this platform, two broad research questions include investigating the impact of (a) natural language processing driven helpers that provide feedback in real time, and, (b) crowd-sourced counseling using individuals with minimal training. To this end, several statistical models will be designed and trained to operate within the proposed text- based platform to interact with novice therapists. The efficacy of the two kinds of feedback (automatic and crowd-based) in terms of how well they can train different kinds of trainees (lay support providers in online forums, novice therapists in training) will be compared to models where the learner practices on their own and/or without specific feedback.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.