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Using Social Judgment Theory Method to examine how experienced occupational therapy driver assessors use information to make fitness-to-drive recommendations

机译:使用社会判断理论方法检查经验丰富的职业治疗驾驶员评估者如何使用信息提出适合驾驶的建议

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摘要

Introduction: As people with a range of disabilities strive to increase their community mobility, occupational therapy driver assessors are increasingly required to make complex recommendations regarding fitness-to-drive. However, very little is knownabout how therapists use information to make decisions. The aim of this study was to model how experienced occupational therapy driver assessors weight and combine information when making fitness-to-drive recommendations and establish their level of decision agreement. Method: Using Social Judgment Theory method, this study examined how 45 experienced occupational therapy driver assessors from the UK, Australia and New Zealand made fitness-to-drive recommendations for a series of 64 case scenarios. Participants completed the task on a dedicated website, and data were analysed using discriminant function analysis and an intraclass correlation coefficient. Results: Accounting for 87% of the variance, the cues central to the fitness-to-drive recommendations made by assessors are the client’s physical skills, cognitive and perceptual skills, road law craft skills, vehicle handling skills and the number of driving instructor interventions. Agreement (consensus) between fitness-to-drive recommendations was very high: intraclass correlation coefficient¼.97, 95% confidence interval .96–.98).Conclusion: Findings can be used by both experienced and novice driver assessors to reflect on and strengthen the fitness-to-drive recommendations made to clients.
机译:简介:随着一系列残障人士努力提高社区流动性,越来越需要职业治疗驾驶员评估师针对驾驶健康提出复杂的建议。但是,对于治疗师如何利用信息来做出决定的了解很少。这项研究的目的是模拟经验丰富的职业治疗驾驶员评估者如何在提出适合驾驶的建议时权衡并结合信息,并建立他们的决策同意水平。方法:本研究使用社会判断理论方法,研究了来自英国,澳大利亚和新西兰的45位经验丰富的职业治疗驾驶员评估员如何针对一系列64种情况提出适合驾驶的建议。参与者在专用网站上完成了任务,并使用判别函数分析和类内相关系数对数据进行了分析。结果:占评估结果差异的87%,评估者提出的适合驾驶的建议的核心内容是客户的身体技能,认知和感知技能,道路法律技巧,车辆操纵技能以及驾驶教练干预的次数。适合驾驶的建议之间的一致性(共识)非常高:类内相关系数¼.97,95%置信区间.96-.98)。结论:有经验的驾驶员和新手驾驶员都可以使用研究结果来反思和加强向客户提出的适合驾驶的建议。

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