COMMUNITY ENGAGEMENT RESEARCH CORE

社区参与研究核心

• 批准号: 7415276
• 负责人: Angela Francine FORD
• 金额: $ 4.59万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7415276
• 关键词: Address; African American; Area; Clinical; Collaborations; Common Data Element; Communicable Diseases; Communities; Community Outreach; Consultations; Data; Data Analyses; Data Collection; Data Quality; Databases; Development; Effectiveness; End Point; Ensure; Evaluation Studies; Faculty; Goals; Health; Individual; Link; Measurement; Mental Depression; Mentors; Methods; Mission; Obesity; Outcome; Outcome Measure; Pathway interactions; Pilot Projects; Policies; Population; Postdoctoral Fellow; Principal Investigator; Property; Protocols documentation; Psychometrics; Publications; Records; Research; Research Design; Research Infrastructure; Research Personnel; Resources; Sample Size; Site; Statistical Methods; Students; Supervision; Testing; Translations; Trust; Work; comparative; data management; design; experience; health disparity; innovation; instrument; interest; member; novel strategies; outreach program; programs; quality assurance; research study

This core is predicated on the assumption that the Center's scientific mission and practical efficiency would be significantly enhanced by centralizing expertise and resources in these areas. Although the research studies proposed within the Center are diverse with respect to the populations being studied, clinical contexts, and study designs, they are unified by an underlying theoretical framework, an overlapping measurement strategy, and a commitment to the collection of data of the highest quality possible. In order to fully take advantage of the common features across studies and avoid duplication of effort, centralized data management, measurement, and statistical resources are essential. The ability to carry out Center-wide analyses is another important value added feature of our approach. Both pooled analysis in which identical data from multiple studies are combined and comparative analysis can be carried out. For example, pooled analysis of the psychometric properties of instruments in the core data base will enable us to apply statistical methods that would not be possible by the limited sample size of individual studies. Similarly, only through a coordinated theoretically driven measurement and analytic approach will we be able to test hypotheses about common pathways across diverse health endpoints, including depression, obesity, and infectious disease. This approach will enable us to explore important questions regarding which pathways are uniquely linked to particular health outcomes and which pathways are common across health outcomes.

这一核心的前提是该中心的科学使命和实际效率将是 通过集中这些领域的专业知识和资源，显着增强。尽管研究表明 该中心提出的建议在所研究的人群、临床背景和 研究设计，它们由一个基本的理论框架、一个重叠的测量策略统一起来， 并致力于收集尽可能最高质量的数据。为了充分利用 跨研究的共同特征，避免重复工作，集中数据管理， 测量和统计资源至关重要。 进行中心范围分析的能力是我们方法的另一个重要增值特征。 合并分析是将多项研究的相同数据进行组合并进行比较分析。例如，对核心数据中工具的心理测量特性进行汇总分析 基础将使我们能够应用统计方法，而这些方法在样本量有限的情况下是不可能实现的。 个人研究。同样，只有通过协调一致的理论驱动的测量和分析 我们将能够测试关于跨不同健康终点的共同途径的假设， 包括抑郁症、肥胖症和传染病。这种方法将使我们能够探索重要的 关于哪些途径与特定健康结果具有独特联系以及哪些途径与特定健康结果相关的问题 在各种健康结果中都很常见。