Jinkyung Katie Park, Vivek K Singh, Pamela Wisniewski
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引用次数: 0
摘要
背景:会话代理(ca);聊天机器人(Chatbots)是一种能够使用自然的人类对话与用户交互的系统。它们越来越多地用于支持敏感话题(如心理健康话题)的交互式知识发现。虽然许多关于心理健康辅助治疗的研究都集中在成年人身上,但这些研究的见解可能不适用于青少年的辅助治疗。目的:综合评价青少年心理健康辅助服务的研究现状。方法:遵循PRISMA(系统评价和荟萃分析首选报告项目)指南,我们从ProQuest、Scopus、Web of Science和PubMed等4个数据库中确定了39项针对青少年心理健康ca的同行评议研究。我们对文献进行了范围综述,以评价青少年心理健康评价中心的研究特点、青少年心理健康评价中心的设计和计算考虑以及青少年心理健康评价中心研究报告的评价结果。结果:我们发现许多心理健康ca(11/ 39,28 %)被设计为年长的同伴,以提供治疗或教育内容来促进青少年的心理健康。所有ca都是基于专家知识设计的,其中一些还纳入了年轻人的意见。ca的技术成熟度还处于起步阶段,主要集中在用基于规则的模型构建原型,以交付预先编写的内容,并使用有限的安全特性来响应即将发生的风险。研究结果表明,虽然年轻人喜欢与ca就敏感话题进行全天候友好或同情的对话,但他们发现ca提供的内容有限。最后,我们发现大多数(35/39,90%)的研究没有解决心理健康ca的伦理问题,而年轻人担心他们敏感谈话数据的隐私和机密性。结论:我们的研究强调了研究人员需要继续共同努力,将青少年心理健康ca的循证研究与如何最好地向青少年提供这些技术的经验教训结合起来。我们的回顾揭示了需要进一步发展和评估的心理健康ca。基于大型语言模型的ca的新趋势使这些技术更加可行。但是,应该优先考虑系统的隐私和安全。虽然初步证据显示心理健康ca的积极趋势,但需要更大样本量的长期评估研究和可靠的研究设计来验证其有效性。更重要的是,从早期设计阶段到最终评估,青年和临床专家之间的合作对于开发安全、有效和以青年为中心的心理健康聊天机器人至关重要。最后,需要为青少年和为青少年制定减轻风险和道德发展的最佳做法,以促进他们的心理健康。
Current Landscape and Future Directions for Mental Health Conversational Agents for Youth: Scoping Review.
Background: Conversational agents (CAs; chatbots) are systems with the ability to interact with users using natural human dialogue. They are increasingly used to support interactive knowledge discovery of sensitive topics such as mental health topics. While much of the research on CAs for mental health has focused on adult populations, the insights from such research may not apply to CAs for youth.
Objective: This study aimed to comprehensively evaluate the state-of-the-art research on mental health CAs for youth.
Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we identified 39 peer-reviewed studies specific to mental health CAs designed for youth across 4 databases, including ProQuest, Scopus, Web of Science, and PubMed. We conducted a scoping review of the literature to evaluate the characteristics of research on mental health CAs designed for youth, the design and computational considerations of mental health CAs for youth, and the evaluation outcomes reported in the research on mental health CAs for youth.
Results: We found that many mental health CAs (11/39, 28%) were designed as older peers to provide therapeutic or educational content to promote youth mental well-being. All CAs were designed based on expert knowledge, with a few that incorporated inputs from youth. The technical maturity of CAs was in its infancy, focusing on building prototypes with rule-based models to deliver prewritten content, with limited safety features to respond to imminent risk. Research findings suggest that while youth appreciate the 24/7 availability of friendly or empathetic conversation on sensitive topics with CAs, they found the content provided by CAs to be limited. Finally, we found that most (35/39, 90%) of the reviewed studies did not address the ethical aspects of mental health CAs, while youth were concerned about the privacy and confidentiality of their sensitive conversation data.
Conclusions: Our study highlights the need for researchers to continue to work together to align evidence-based research on mental health CAs for youth with lessons learned on how to best deliver these technologies to youth. Our review brings to light mental health CAs needing further development and evaluation. The new trend of large language model-based CAs can make such technologies more feasible. However, the privacy and safety of the systems should be prioritized. Although preliminary evidence shows positive trends in mental health CAs, long-term evaluative research with larger sample sizes and robust research designs is needed to validate their efficacy. More importantly, collaboration between youth and clinical experts is essential from the early design stages through to the final evaluation to develop safe, effective, and youth-centered mental health chatbots. Finally, best practices for risk mitigation and ethical development of CAs with and for youth are needed to promote their mental well-being.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.