IMPROVING THE PREDICTION OF DANGEROUSNESS

临床医生继续需要做出法律上的危险判定 和常规的临床用途。因此，研究需要集中在开发 更好地完成这项任务的方法。拟议的研究通过以下方式做到这一点 对两组关系进行建模：1)案例特征和 临床医生对危险的预测和2)病例之间 精神病人的特点和暴力行为。这些型号需要一种 通过以下方式改进危险预测的重要步骤 为临床医生做了什么以及应该做什么提供了丰富的图景 在评估危险性时进行考虑。 这项研究利用了使用以前收集的数据的机会 来自调查人员收集的两个数据集。它们有重叠， 丰富的案件特征信息集，将用作 潜在的预测者。第一项研究有临床预测 对大约400个病例的样本进行了危险分析。第二种兼而有之。 临床预测和详细的自我报告和官方描述 患者对大约800名患者的暴力行为。入院时的预测模型 评估(关于社区和单位暴力)和在退伍时 将使用各种标准统计方法来构建，例如 以及分类树方法。模型将在内部和 外部交叉验证。该项目将建造一个初步的 用于构建危险的临床评估的方法 临床医生实际如何改变这些精算模型的模型。 这一决策援助将是切实的、在方法上合理的一步， 使危险性预测系统化。

Clinicians continue to need to make dangerousness determinations for legal and routine clinical purposes. Thus research needs to focus on developing methods for doing this task better. The proposed study does this by modeling two sets of relationships: 1) between case characteristics and clinicians' predictions of dangerousness and 2) between case characteristics and violence in mental patients. These models take an important step toward improving the prediction of dangerousness by providing a rich picture of what clinicians do, and should, take into account when assessing dangerousness. This study capitalizes on an opportunity to use previously collected data from two data sets gathered by the investigators. They have overlapping, rich sets of case characteristic information that will serve as a pool of potential predictors. The first study has clinical predictions of dangerousness on a sample of approximately 400 cases. The second has both clinical predictions and detailed self report and official accounts of patient violence on about 800 patients. Models for prediction at admission assessment (regarding both community and unit violence) and at discharge will be constructed using a variety of standard statistical approaches as well as classifications tree methodology. Models will be internally and externally cross-validated. The project will construct a preliminary method for structuring clinical assessments of dangerousness using the models of what clinicians actually do to alter these actuarial models. This decision aid will be a tangible and methodologically sound step toward systematizing the prediction of dangerousness.