Human Resource Development, The University of Texas at Tyler, Tyler, Texas, 75799, USA.
F1000Res. 2024 Sep 26;13:664. doi: 10.12688/f1000research.151493.1. eCollection 2024.
An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Data extraction activities associated with evidence synthesis have been described as time-consuming to the point of critically limiting the usefulness of research. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. The goal of the present study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists.
We report the baseline results of a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. This review follows PRISMA standards for reporting systematic reviews.
The baseline review of social science research yielded 23 relevant studies.
When considering the process of automating systematic review and meta-analysis information extraction, social science research falls short as compared to clinical research that focuses on automatic processing of information related to the PICO framework. With a few exceptions, most tools were either in the infancy stage and not accessible to applied researchers, were domain specific, or required substantial manual coding of articles before automation could occur. Additionally, few solutions considered extraction of data from tables which is where key data elements reside that social and behavioral scientists analyze.
大量快速积累的科学证据为研究人员和从业者提供了新的机会，但这些优势常常因寻找和整合不断扩展的科学信息所需的资源而黯然失色。与证据综合相关的数据提取活动被描述为非常耗时，以至于严重限制了研究的有用性。在社会科学学科中，自动化技术的使用可以及时、准确地进行知识综合，从而提高研究转化的价值，更好地为关键政策制定提供信息，并扩展人类互动、组织和系统的现有理解。围绕自动化的持续发展主要集中在循证医学的研究中，而针对临床研究界以外应用的工具和技术的证据有限。本研究的目的是通过综合当前提取关键数据元素的提取技术在社会科学家应用中的趋势，扩展自动化知识库。
我们报告了一项针对自动化数据提取技术在社会科学系统评价和荟萃分析中应用的实时系统评价的基线结果。该综述符合系统评价报告的 PRISMA 标准。
对社会科学研究的基线综述产生了 23 项相关研究。
在考虑自动化系统评价和荟萃分析信息提取的过程中，与专注于自动处理与 PICO 框架相关信息的临床研究相比，社会科学研究落后了。除了少数例外，大多数工具要么处于起步阶段，无法为应用研究人员所用，要么是特定于某个领域的，要么在自动化发生之前需要对文章进行大量手动编码。此外，很少有解决方案考虑从表格中提取数据，而表格是社会和行为科学家分析的关键数据元素所在的位置。
