Department of Psychological Assessment, SCHUHFRIED GmbH, Mödling, Austria.
Arch Clin Neuropsychol. 2010 Mar;25(2):99-117. doi: 10.1093/arclin/acp109. Epub 2010 Jan 15.
Increasingly often, practitioners in neuropsychological rehabilitation centers are called upon to assess patients' fitness to drive after brain injury. There is, therefore, a need for valid and reliable psychometric test batteries that enable unsafe drivers to be identified. This article investigates the contribution of five driving-related personality traits to the prediction of fitness to drive in patients suffering from traumatic brain injuries (TBI) or strokes over and above cognitive ability traits that have already shown to be related to safe driving. A total of 178 patients suffering from either strokes or TBI participated in this study. All the participants completed a standardized psychometric test battery and subsequently took a standardized driving test. The contribution of the driving-related ability and personality traits to the prediction of fitness to drive was investigated by means of a logistic regression analysis and an artificial neural network. The results indicate that both cognitive ability and personality factors are important in predicting fitness to drive, although cognitive ability factors contribute slightly more to the prediction of patients' actual fitness to drive than personality factors. Furthermore, even though there are subtle differences in the predictive models obtained for the two subsamples (stroke and TBI patients), these differences are adequately accounted for by a more unitary model calculated by means of an artificial neural network that is capable of taking account of moderating effects between the predictor variables.
越来越多的神经心理康复中心的从业者被要求评估脑损伤后患者的驾驶能力。因此,需要有效的和可靠的心理计量测试组合,以便识别不安全的驾驶员。本文探讨了五种与驾驶相关的人格特质对预测创伤性脑损伤(TBI)或中风患者驾驶能力的贡献,这些人格特质除了已经显示与安全驾驶相关的认知能力特质外。共有 178 名患有中风或 TBI 的患者参与了这项研究。所有参与者都完成了标准化的心理计量测试组合,随后进行了标准化的驾驶测试。通过逻辑回归分析和人工神经网络,研究了与驾驶相关的能力和人格特质对驾驶能力预测的贡献。结果表明,认知能力和人格因素对预测驾驶能力都很重要,尽管认知能力因素对预测患者实际驾驶能力的贡献略高于人格因素。此外,尽管为两个子样本(中风和 TBI 患者)获得的预测模型存在细微差异,但通过人工神经网络计算的更统一的模型可以充分考虑预测变量之间的调节效应,从而充分考虑这些差异。