...
【24h】

Age 4 Predictors of Oppositional Defiant Disorder in Early Grammar School

机译:早期语法学校的反对缺陷症的4岁预测因素

获取原文
获取原文并翻译 | 示例

摘要

Our ability to predict which children will exhibit oppositional defiant disorder (ODD) at the time of entry into grammar school at age 6 lags behind our understanding of the risk factors for ODD. This study examined how well a set of multidomain risk factors for ODD assessed in 4-year-old children predicted age 6 ODD diagnostic status. Participants were a diverse sample of 796 4-year-old children (391 boys). The sample was 54% White, non-Hispanic; 16.8% African American, 20.4% Hispanic; 2.4% Asian; and 4.4% Other or mixed race. The classification accuracy of two models of multidomain risk factors, using either a measure of overall ODD symptoms or dimensions of ODD obtained at age 4, were compared to one another, to chance, and to a parsimonious model based solely on parent-reported ODD using Automated Classification Tree Analysis. Effect Strength for Sensitivity (ESS), a measure of classification accuracy, indicated a multidomain model including a general measure of ODD symptoms at age 4 yielded a large effect (56.29%), a 13.7% increase over the ESS for the parsimonious model (ESS = 42.9%). The ESS (51.23%) for a model including two ODD dimensions (behavior and negative affect) was smaller than that for the model including a measure of overall ODD symptoms. The Classification Tree Analysis approach showed a small but distinct advantage that would be useful in screening for which children would most likely meet criteria for age 6 ODD.
机译:我们预测哪些儿童将在进入语法学校时将其预测哪些儿童在6岁时展示了对语法学校的理解,了解奇数的风险因素。本研究检测了4岁儿童奇数评估的一套多域风险因素如何预测6岁的奇数奇数诊断地位。参与者是796名4岁儿童(391名男孩)的不同样本。样品为54%白色,非西班牙裔; 16.8%非洲裔美国人,西班牙裔20.4%; 2.4%亚洲; 4.4%的其他或混合种族。两种模型的多麦田风险因素的分类准确性,使用奇数奇数症状或奇数奇数的尺寸,彼此相互比较,并且仅基于父母报告的奇数使用的解析模型自动分类树分析。灵敏度的效果强度(ESS),分类准确度的衡量标准,表明了包括4岁奇数症状的一般衡量标准的多畴模型产生了很大的效果(56.29%),对灾略模型的ESS增加了13.7%(ESS = 42.9%)。包括两个奇数尺寸(行为和负面影响)的模型的ESS(51.23%)小于模型的模型,包括奇数症状的量度。分类树分析方法表明了一个小而明显的优势,可用于筛选哪些孩子最有可能符合6岁奇数的标准。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号