Stewart Shannon L, Poss Jeff W, Thornley Elizabeth, Hirdes John P
Western University, Faculty of Education, London, ON, Canada.
University of Waterloo, Faculty of Applied Health Sciences, Waterloo, ON, Canada.
Health Serv Insights. 2019 Feb 24;12:1178632919827930. doi: 10.1177/1178632919827930. eCollection 2019.
Children's mental health care plays a vital role in many social, health care, and education systems, but there is evidence that appropriate targeting strategies are needed to allocate limited mental health care resources effectively. The aim of this study was to develop and validate a methodology for identifying children who require access to more intense facility-based or community resources. Ontario data based on the interRAI Child and Youth Mental Health instruments were analysed to identify predictors of service complexity in children's mental health. The Resource Intensity for Children and Youth (RIChY) algorithm was a good predictor of service complexity in the derivation sample. The algorithm was validated with additional data from 61 agencies. The RIChY algorithm provides a psychometrically sound decision-support tool that may be used to inform the choices related to allocation of children's mental health resources and prioritisation of clients needing community- and facility-based resources.
儿童心理健康护理在许多社会、医疗保健和教育系统中发挥着至关重要的作用,但有证据表明,需要适当的目标定位策略来有效分配有限的心理健康护理资源。本研究的目的是开发并验证一种方法,以识别那些需要获得更强化的机构或社区资源的儿童。分析了基于interRAI儿童和青少年心理健康工具的安大略省数据,以确定儿童心理健康服务复杂性的预测因素。儿童和青少年资源强度(RIChY)算法在推导样本中是服务复杂性的良好预测指标。该算法通过来自61个机构的额外数据进行了验证。RIChY算法提供了一种心理测量学上合理的决策支持工具,可用于为儿童心理健康资源分配以及需要社区和机构资源的客户优先级排序相关的选择提供信息。