Re-Examining the Predictive Validity and Establishing Risk Levels for the Dynamic Appraisal of Situational Aggression: Youth Version.

Tessa Maguire, Steven Bowe, John Kasinathan, Michael Daffern
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Abstract

The Dynamic Appraisal of Situational Aggression: Youth Version (DASA:YV) is a brief instrument, most often used by nurses and was specifically designed to assess risk of imminent violence in youth settings. To date, it has been recommended that DASA:YV scores are interpreted in a linear manner, with high scores indicating a greater level of risk and therefore need more assertive and immediate intervention. This study re-analyses an existing data set using contemporary robust data analytic procedures to examine the predictive validity of the DASA:YV, and to determine appropriate risk bands. Mixed effect logistic regression models were used to determine whether the DASA:YV predicted aggression when the observations are correlated. Two approaches were employed to identify and test novel DASA:YV risk bands, where (1) three risk bands as previously generated for the adult DASA were used as a starting point to consider recategorising the DASA:YV into three risk bands, and (2) using a decision tree analysis method known as Chi-square automated interaction detection to produce risk bands. There was no statistically significant difference between a four and three category of risk band. AUC values were 0.85 for the four- and three-category options. A three-category approach is recommended for the DASA:YV. The new risk bands may assist nursing staff by providing more accurate categorisation of risk state. Identification of escalation in risk state may prompt early intervention, which may also prevent reliance on the use of restrictive practices when young people are at risk of acting aggressively.

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重新评估情境攻击动态评估的预测有效性并确定风险等级:青少年版。
情境攻击动态评估:情境攻击动态评估:青年版》(DASA:YV)是一种简短的工具,最常用于护士,专门用于评估青年环境中即将发生的暴力风险。迄今为止,人们一直建议以线性方式解释 DASA:YV 分数,高分表示风险程度较高,因此需要更加果断和及时的干预。本研究使用现代稳健的数据分析程序重新分析了现有的数据集,以检验 DASA:YV 的预测有效性,并确定适当的风险等级。研究采用混合效应逻辑回归模型来确定 DASA:YV 是否能在观察结果相关的情况下预测攻击行为。我们采用了两种方法来识别和测试新的 DASA:YV 风险带,其中(1)以之前为成人 DASA 生成的三个风险带为起点,考虑将 DASA:YV 重新归类为三个风险带;(2)使用称为 "Chi-square 自动交互检测 "的决策树分析方法来生成风险带。在统计学上,四类和三类风险带之间没有显著差异。四类和三类选项的 AUC 值均为 0.85。建议 DASA:YV 采用三类方法。新的风险带可对风险状态进行更准确的分类,从而为护理人员提供帮助。识别风险状态的升级可促使护理人员及早进行干预,从而避免在青少年有攻击行为风险时依赖使用限制性措施。
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