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一种使用多元化团队招募多样化参与者的新型临床研究模式。

A Novel Clinical Research Modality for Enrolling Diverse Participants Using a Diverse Team.

作者信息

Lay Phoebe, Paralkar Tapasvini, Ahmed Syed Hadi, Ghani Minha, Muneer Sara, Jinnah Ramsha, Chen Carolyn, Zeitz Jack, Nitsch Alejandra, Osier Nico

机构信息

College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA.

College of Liberal Arts, The University of Texas at Austin, Austin, TX 78712, USA.

出版信息

Brain Sci. 2020 Jul 8;10(7):434. doi: 10.3390/brainsci10070434.

Abstract

The advancement of the pediatric traumatic brain injury (TBI) knowledge base requires biospecimens and data from large samples. This study seeks to describe a novel clinical research modality to establish best practices for enrolling a diverse pediatric TBI population and quantifying key information on enrollment into biobanks. Screening form responses were standardized and cleaned through Google Sheets. Data were used to analyze total individuals at each enrollment stage. R was utilized for final analysis, including logistic model and proportion statistical tests, to determine further significance and relationships. Issues throughout data cleaning shed light on limitations of the consent modality. The results suggest that through a diverse research team, the recruited sample exceeds traditional measures of representation (e.g., sex, race, ethnicity). Sex demographics of the study are representative of the local population. Screening for candidates is critical to the success of the consent modality. The consent modality may be modified to increase the diversity of the study population and accept bilingual candidates. Researchers must implement best practices, including increasing inclusivity of bilingual populations, utilizing technology, and improving participant follow-up, to improve health disparities for understudied clinical populations.

摘要

儿科创伤性脑损伤(TBI)知识库的发展需要来自大样本的生物标本和数据。本研究旨在描述一种新型临床研究模式,以确立纳入多样化儿科TBI人群并量化生物样本库纳入关键信息的最佳实践。筛查表的回复通过谷歌表格进行标准化和清理。数据用于分析每个纳入阶段的个体总数。使用R进行最终分析,包括逻辑模型和比例统计测试,以确定进一步的显著性和关系。数据清理过程中的问题揭示了同意模式的局限性。结果表明,通过多元化的研究团队,招募的样本超过了传统的代表性指标(如性别、种族、民族)。该研究的性别人口统计学特征代表了当地人口。筛选候选人对于同意模式的成功至关重要。可以修改同意模式以增加研究人群的多样性并接受双语候选人。研究人员必须实施最佳实践,包括提高双语人群的包容性、利用技术以及改善参与者随访,以改善对研究不足的临床人群的健康差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e357/7408300/84fbd0f5badd/brainsci-10-00434-g001.jpg

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