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将基于证据的自杀预防模型嵌入国民保健服务体系:一项服务改进计划。

Embedding an Evidence-Based Model for Suicide Prevention in the National Health Service: A Service Improvement Initiative.

机构信息

Department of Psychology, Faculty of Health Sciences, University of Hull, Hull HU6 7RX, UK.

NAViGO Health and Social Care Community Interest Company, Grimsby DN32 0QE, UK.

出版信息

Int J Environ Res Public Health. 2020 Jul 8;17(14):4920. doi: 10.3390/ijerph17144920.

Abstract

Despite the improved understanding of the determinants of suicide over recent decades, the mean suicide rate within the United Kingdom (UK) has remained at 10 per 100,000 per annum, with about 28% accessing mental health services in the 12 months prior to death. In this paper, we outlined a novel systems-level approach to tackling this problem through objectively differentiating the level of severity for each suicide risk presentation and providing fast-track pathways to care for all, including life-threatening cases. An additional operational challenge addressed within the proposed model was the saturation of local crisis mental health services with approximately 150 suicidality referrals per month, including non-mental health cases. This paper discussed a service improvement initiative undertaken within a National Health Service (NHS) secondary care mental health provider's open-access 24/7 crisis and home treatment service. An organisation-wide bespoke "suicide risk triage" system utilising the Collaborative Assessment and Management of Suicidality (CAMS) was implemented across all services. The preliminary impacts on suicidality, suicide rates and service user outcomes were described.

摘要

尽管近几十年来人们对自杀决定因素的理解有所提高,但英国(UK)的平均自杀率仍保持在每年每 10 万人中有 10 人,约有 28%的人在死前 12 个月内接受了心理健康服务。在本文中,我们概述了一种通过客观区分每个自杀风险表现的严重程度并为所有人提供快速通道护理的全新系统层面的方法,包括有生命危险的病例。在提出的模型中解决的另一个运营挑战是当地危机心理健康服务的饱和,每月有大约 150 例自杀倾向转诊,包括非心理健康病例。本文讨论了在国家卫生服务(NHS)二级保健精神卫生提供者的开放访问 24/7 危机和家庭治疗服务中进行的一项服务改进举措。在所有服务中都实施了一种利用协作评估和自杀管理(CAMS)的全组织定制“自杀风险分诊”系统。描述了对自杀倾向、自杀率和服务用户结果的初步影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/7399800/0e49189bb311/ijerph-17-04920-g001.jpg

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