• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一项在线机器学习驱动的风险评估和干预平台的随机对照试验,旨在增加危机服务的使用。

Randomized controlled trial of an online machine learning-driven risk assessment and intervention platform for increasing the use of crisis services.

机构信息

Department of Psychology, Harvard University.

Koko.

出版信息

J Consult Clin Psychol. 2019 Apr;87(4):370-379. doi: 10.1037/ccp0000389.

DOI:10.1037/ccp0000389
PMID:30883164
Abstract

OBJECTIVE

Mental illness is a leading cause of disease burden; however, many barriers prevent people from seeking mental health services. Technological innovations may improve our ability to reach underserved populations by overcoming many existing barriers. We evaluated a brief, automated risk assessment and intervention platform designed to increase the use of crisis resources provided to those online and in crisis.

METHOD

Participants, users of the digital mental health app Koko, were randomly assigned to treatment or control conditions upon accessing the app and were included in the study after their posts were identified by machine learning classifiers as signaling a current mental health crisis. Participants in the treatment condition received a brief Barrier Reduction Intervention (BRI) designed to increase the use of crisis service referrals provided on the app. Participants were followed up several hours later to assess the use of crisis services.

RESULTS

Only about one quarter of participants in a crisis (21.8%) reported being "very likely" to use clinical referrals provided to them, with the most commonly endorsed barriers being they "just want to chat" or their "thoughts are too intense." Among participants providing follow-up data (41.3%), receipt of the BRI was associated with a 23% increase in the use of crisis services.

CONCLUSION

These findings suggest that a brief, automated BRI can be efficacious on digital platforms, even among individuals experiencing acute psychological distress. The potential to increase help seeking and service utilization with such procedures holds promise for those in need of psychiatric services.

TRIAL REGISTRATION

clinicaltrials.gov identifier: NCT03633825. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

摘要

目的

精神疾病是导致疾病负担的主要原因之一;然而,许多障碍阻止人们寻求心理健康服务。技术创新可以通过克服许多现有的障碍来提高我们为服务不足的人群提供服务的能力。我们评估了一个简短的自动化风险评估和干预平台,旨在增加向在线和处于危机中的人提供的危机资源的使用。

方法

参与者是数字心理健康应用程序 Koko 的用户,他们在访问应用程序时被随机分配到治疗或对照组,并在通过机器学习分类器确定他们的帖子表示当前心理健康危机后被纳入研究。治疗组的参与者接受了一个简短的减少障碍干预(BRI),旨在增加对应用程序上提供的危机服务推荐的使用。几个小时后,对参与者进行了随访,以评估危机服务的使用情况。

结果

只有大约四分之一的处于危机中的参与者(21.8%)表示“非常有可能”使用提供给他们的临床推荐,最常被认可的障碍是他们“只是想聊天”或他们的“想法太强烈”。在提供后续数据的参与者中(41.3%),接受 BRI 与危机服务使用增加 23%相关。

结论

这些发现表明,即使在经历急性心理困扰的个体中,简短的自动化 BRI 在数字平台上也可能有效。这种程序增加寻求帮助和服务利用的潜力为那些需要精神病服务的人带来了希望。

试验注册

clinicaltrials.gov 标识符:NCT03633825。(PsycINFO 数据库记录(c)2019 APA,保留所有权利)。

相似文献

1
Randomized controlled trial of an online machine learning-driven risk assessment and intervention platform for increasing the use of crisis services.一项在线机器学习驱动的风险评估和干预平台的随机对照试验,旨在增加危机服务的使用。
J Consult Clin Psychol. 2019 Apr;87(4):370-379. doi: 10.1037/ccp0000389.
2
Procedures for risk management and a review of crisis referrals from the MindSpot Clinic, a national service for the remote assessment and treatment of anxiety and depression.风险管理程序以及对MindSpot诊所危机转诊的审查,MindSpot诊所是一家提供焦虑和抑郁远程评估与治疗的全国性服务机构。
BMC Psychiatry. 2015 Dec 1;15:304. doi: 10.1186/s12888-015-0676-6.
3
Admission decisions following contact with an emergency mental health assessment and intervention service.在与紧急心理健康评估和干预服务机构接触后做出的入院决定。
J Clin Nurs. 2007 Jul;16(7):1313-22. doi: 10.1111/j.1365-2702.2007.01302.x.
4
Are long-term psychiatric patients causing more crisis consultations outside office hours in mental health care?长期精神科患者是否会导致精神卫生保健中非办公时间的危机咨询增加?
Int J Soc Psychiatry. 2013 Sep;59(6):555-60. doi: 10.1177/0020764012445259. Epub 2012 Jun 24.
5
[Psychiatric emergency care and crisis intervention--concepts, experiences and results].[精神科急诊护理与危机干预——概念、经验与成果]
Psychiatr Prax. 1986 Nov;13(6):203-12.
6
Implementing street triage: a qualitative study of collaboration between police and mental health services.实施街头分诊:对警察与心理健康服务机构合作的定性研究
BMC Psychiatry. 2016 Sep 7;16(1):313. doi: 10.1186/s12888-016-1026-z.
7
Perceptions of crisis care in populations who self-referred to a telephone-based mental health triage service.自我转介至电话心理分诊服务的人群对危机护理的认知。
Int J Ment Health Nurs. 2016 Apr;25(2):136-43. doi: 10.1111/inm.12177. Epub 2016 Jan 6.
8
The CORE service improvement programme for mental health crisis resolution teams: results from a cluster-randomised trial.精神科危机干预小组的 CORE 服务改进项目:一项群组随机试验的结果。
Br J Psychiatry. 2020 Jun;216(6):314-322. doi: 10.1192/bjp.2019.21.
9
Do We Still Have a Digital Divide in Mental Health? A Five-Year Survey Follow-up.我们在心理健康方面是否仍存在数字鸿沟?一项为期五年的调查随访。
J Med Internet Res. 2016 Nov 22;18(11):e309. doi: 10.2196/jmir.6511.
10
Making in-roads across the youth mental health landscape in Singapore: the Community Health Assessment Team (CHAT).新加坡社区健康评估团队(CHAT)在青少年心理健康领域取得进展
Early Interv Psychiatry. 2016 Apr;10(2):171-7. doi: 10.1111/eip.12192. Epub 2014 Oct 2.

引用本文的文献

1
Identifying therapeutic characteristics of digital social media narratives about suicide: a mixed methods investigation.识别关于自杀的数字社交媒体叙事的治疗特征:一项混合方法研究。
Npj Ment Health Res. 2025 Sep 3;4(1):41. doi: 10.1038/s44184-025-00155-5.
2
Automated Digital Safety Planning Interventions for Young Adults: Qualitative Study Using Online Co-design Methods.针对青年成年人的自动化数字安全规划干预措施:使用在线协同设计方法的定性研究
JMIR Form Res. 2025 Feb 26;9:e69602. doi: 10.2196/69602.
3
Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.
精神卫生保健中的人工智能:对诊断、监测及干预应用的系统评价
Psychol Med. 2025 Feb 6;55:e18. doi: 10.1017/S0033291724003295.
4
Automated Real-Time Tool for Promoting Crisis Resource Use for Suicide Risk (ResourceBot): Development and Usability Study.用于促进自杀风险危机资源使用的自动化实时工具(ResourceBot):开发和可用性研究。
JMIR Ment Health. 2024 Oct 31;11:e58409. doi: 10.2196/58409.
5
Antecedents, reasons for, and consequences of suicide attempts: Results from a qualitative study of 89 suicide attempts among army soldiers.自杀未遂的前因、原因及后果:对89名陆军士兵自杀未遂情况的定性研究结果
J Psychopathol Clin Sci. 2025 Jan;134(1):6-17. doi: 10.1037/abn0000935. Epub 2024 Sep 19.
6
Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data.检测美国军人和退伍军人中的自杀风险:一种使用社交媒体数据的深度学习方法。
Psychol Med. 2024 Sep 9:1-10. doi: 10.1017/S0033291724001557.
7
Breaking Down Barriers to a Suicide Prevention Helpline: Web-Based Randomized Controlled Trial.打破自杀预防热线障碍:基于网络的随机对照试验。
JMIR Ment Health. 2024 Sep 5;11:e56396. doi: 10.2196/56396.
8
Intervening on high-risk responses during ecological momentary assessment of suicidal thoughts: Is there an effect on study data?在自杀意念的生态瞬时评估中干预高危反应:对研究数据有影响吗?
Psychol Assess. 2024 Jan;36(1):66-80. doi: 10.1037/pas0001288. Epub 2023 Nov 2.
9
Why Suicide? Suicide Propinquity and Adolescent Risk for Suicidal Thoughts and Behaviors.为何自杀?自杀倾向与青少年自杀念头及行为风险
Clin Child Fam Psychol Rev. 2023 Dec;26(4):904-918. doi: 10.1007/s10567-023-00456-1. Epub 2023 Oct 6.
10
Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models: Cross-Sectional Study.使用机器学习模型预测网络减肥计划中的退出意向以改善结果:横断面研究。
J Med Internet Res. 2023 Jun 26;25:e43633. doi: 10.2196/43633.