Centre for Forensic Behavioural Science, Swinburne University of Technology, Melbourne, Victoria, Australia.
Victorian Institute of Forensic Mental Health, Forensicare, Clifton Hill, Victoria, Australia.
Int J Ment Health Nurs. 2024 Dec;33(6):2336-2342. doi: 10.1111/inm.13406. Epub 2024 Aug 20.
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.
青少年版(DASA:YV)是一种简短的工具,通常由护士使用,专门用于评估青少年环境中即将发生暴力的风险。迄今为止,已经建议以线性方式解释 DASA:YV 分数,高分表示风险水平更高,因此需要更果断和即时的干预。本研究使用当代稳健数据分析程序重新分析了现有数据集,以检验 DASA:YV 的预测有效性,并确定适当的风险带。混合效应逻辑回归模型用于确定当观察结果相关时,DASA:YV 是否可以预测攻击。采用两种方法来确定和测试新的 DASA:YV 风险带,其中 (1) 使用先前为成人 DASA 生成的三个风险带作为起点,考虑将 DASA:YV 重新分类为三个风险带,以及 (2) 使用称为卡方自动交互检测的决策树分析方法来生成风险带。风险带分为四组和三组之间没有统计学上的显著差异。四组和三组选项的 AUC 值分别为 0.85。建议对 DASA:YV 采用三分类方法。新的风险带可以通过更准确地对风险状态进行分类来帮助护理人员。识别风险状态的升级可能会促使早期干预,这也可能防止在年轻人有攻击性行为的风险时依赖限制性行为。