Severe LD in Juvenile Delinquents

未成年人犯罪中的严重LD

• 批准号: 9761329
• 负责人: ELENA L GRIGORENKO
• 金额: $ 47.86万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9761329
• 关键词: Address; Adolescent; Algorithms; Awareness; Behavior Therapy; Big Data; Biological Markers; Child; Cognitive; Communities; Comprehension; Computers; Connecticut; County; DNA Methylation; Data; Data Quality; Data Set; Databases; Development; Developmental Process; Educational Intervention; Elements; Epigenetic Process; Event; Event-Related Potentials; Freezing; Growth; Individual; Intake; Intervention; Learning; Learning Disabilities; Learning Skill; Letters; Link; Literature; Media Intervention; Modality; Modeling; Monitor; Orthography; Outcome; Participant; Pattern; Peripheral; Persons; Pharmacology; Psychological reinforcement; Reading; Research; Research Project Grants; Research Proposals; Saliva; Sampling; Scalp structure; Semantics; Services; Source; Specimen; Statistical Methods; Stimulus; Students; Subgroup; Target Populations; Text; Time; Vocabulary; Youth; austin; base; court; data modeling; design; epigenetic marker; epigenomics; executive function; follow-up; indexing; insight; interest; intervention program; juvenile delinquent; lexical; neurophysiology; novel; person centered; phonological awareness; phonology; probation; programs; recruit; remediation; response; self-directed learning; semantic processing; skills; sound; spelling; therapy design; tool; trend analysis

SUMMARY Pursuant to the Specific Aims (SA) of the OVERALL section for the Hub, the RESEARCH PROJECT focuses on the OVERALL SA2, i.e., (1) identifying a subgroup of juvenile offenders placed in residential settings who are characterized by severe learning disabilities (LD) using big data approaches; (2) carrying out a novel mixed media (person-to-person plus person-to-computer) educational therapy; and (3) differentiating responders from nonresponders to academic therapy by tracking neurophysiological and epigenetic markers. Each of these SAs, in turn, unfolds into a network of research aims. Specifically, using historical (i.e., generated by a freeze from a historical dataset) big data from the Connecticut Court Support Services Division and the Harris County Juvenile Probation Department (estimated n of ~100,000 individual cases), we will initiate three interrelated inquiries: (1) to rigorously evaluate data quality and the statistical assumptions underlying the models that are applicable to big data; (2) to enable valid descriptive and causal inferences from large-scale algorithms applied to heterogeneous samples; and (3) to gain an understanding of the complexities of learning disabilities (LD) among delinquent youth through the statistical methods that capture developmental processes on multiple levels. The last inquiry will result in the development of an algorithm that will be applied to current data maintained on juveniles in the Harris County Juvenile Probation Department, to assist in the identification of, in total, 192 individuals (48 each year of the Hub) with severe LD. These individuals, in a nonconcurrent multiple baseline design, will be offered an educational therapy designed to address severe reading problems in juvenile detainees using a novel mixed media intervention in which the person-to-person intensive 1:1 component is completed while youth are in residential settings (24 sessions, delivered in 90 minute settings 3 times a week) and a “gamified” educational iPad learning tool follow-up completed upon release (with appropriate network fidelity monitoring and participant reinforcement). The person-to-person component is developed specifically for juvenile offenders with severe LD, combining two well-established and highly- regarded intervention programs designed to systematically build students’ repertoire of grapheme-phoneme correspondence rules as well as develop comprehensive reading skills, from beginning reading to proficiency. We anticipate, based on the literature, that the intervention, although its components have been convincingly demonstrated to be effective, will generate a spectrum of outcomes, with youth being more or less responsive to the intervention. To differentiate these outcomes, we will trace two sets of biomarkers thought to be associated with learning, i.e., neurophysiological and epigenetic markers, sampled throughout the intervention delivery. To evaluate the generalizability and durability of the intervention, we will also administer the intervention to 6 children per year in Years 1-4 of the projects (i.e., to 24 youth) in detention and probation centers in the state of Connecticut.

概括 根据枢纽整体部分的特定目标（SA），研究项目重点 在整个SA2上，即（1）确定位于住宅环境中的少年犯罪者的亚组 使用大数据方法以严重的学习障碍（LD）为特征； （2）进行小说混合 媒体（人与人之间的人对计算机）教育疗法； （3）将响应者与 通过跟踪神经生理学和表观遗传标记来对学术疗法进行无反应。每个 反过来，SAS将研究网络展开。具体而言，使用历史（即由冻结产生 来自康涅狄格州法院支持服务部​​和哈里斯县的历史数据集） 青少年缓刑部（估计n个〜100,000个单独的案件中的n），我们将启动三个相互关联 查询：（1）严格评估数据质量和模型基础的统计假设 适用于大数据； （2）从应用的大规模算法中启用有效的描述性和因果推断 进行异质样品； （3）了解学习障碍的复杂性（LD） 通过统计方法捕获多个的发展过程的违法青年 水平。最后一个查询将导致将开发一种将应用于当前数据的算法 维持哈里斯县少年缓刑部的少年，以协助确定 总共有192个患者（每年48个枢纽），患有严重的LD。这些人，以非汇率倍数 基线设计将提供一种教育疗法，旨在解决严重的阅读问题 少年细节使用一种新颖的混合媒体干预措施，其中人对人的密集是1：1 组件已完成，年轻人在居民设置中（24次会议，在90分钟内交付3 一周的时间）和发布后完成的“恋爱”教育iPad学习工具后续（与 适当的网络保真度监视和参与加强）。人与人之间的组成部分是 专为患有严重LD的少年罪犯开发，结合​​了两个良好且高度的 考虑的干预计划旨在系统地构建学生的素谱曲目 从开始阅读到熟练程度，通信规则以及发展全面的阅读技能。 我们预计，根据文献，尽管其组成部分令人信服，但干预 证明是有效的，会产生一系列结果，而青年或多或少地反应迅速 为了区分这些结果，我们将追踪两套被认为是的生物标志物 与学习，即神经生理学和表观遗传标记有关，在整个干预过程中取样 送货。为了评估干预措施的普遍性和耐用性，我们还将管理 在拘留和缓刑的项目1 - 4年中，每年有6个儿童的干预（即24名青年） 康涅狄格州的中心。