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Leximancer半自动内容分析与人工内容分析的比较:以COVID-19医院工作人员互动网络直播的情感记录为例的医疗保健研究

A Comparison of Leximancer Semi-automated Content Analysis to Manual Content Analysis: A Healthcare Exemplar Using Emotive Transcripts of COVID-19 Hospital Staff Interactive Webcasts.

作者信息

Engstrom Teyl, Strong Jenny, Sullivan Clair, Pole Jason D

机构信息

Centre for Health Services Research, The University of Queensland, Herston, QLD, Australia.

Metro North Hospital and Health Services, Brisbane Australia, The University of Queensland, School of Health and Rehabilitation Sciences, Professor Occupational Therapy, University of Southern Queensland.

出版信息

Int J Qual Methods. 2022 Aug 18;21:16094069221118993. doi: 10.1177/16094069221118993. eCollection 2022 Jan-Dec.

Abstract

Effective consumer centred healthcare incorporates consumer and clinician perspectives into decision making, in addition to traditional quantitative measures. This information is usually captured in qualitative data that requires manual analysis. Healthcare systems often lack resources to systematically incorporate qualitative feedback into decision making. Semi-automated content analysis tools, such as Leximancer, provide an efficient and objective alternative to time consuming manual content analysis (MCA). Literature on the validity of Leximancer in healthcare is sparse. This study seeks to validate Leximancer against MCA on a broad emotive conversational dataset gathered in a healthcare setting. At the outset of the COVID-19 pandemic, a large Australian hospital and health service conducted interactive webcasts with staff to provide updates and answer questions. A manual thematic analysis and a Leximancer content analysis were conducted independently on 20 webcast transcripts. The findings were compared, along with the time required to the complete each analysis. The Leximancer analysis identified nine concepts, while the manual analysis identified 12 concepts. The Leximancer concepts mapped to five of the concepts identified in the manual analysis, which accounted for 74% of mentions tagged in the text through the manual analysis. Leximancer missed concepts which required an emotional or contextual interpretation. The Leximancer analysis took 21 hours (excluding time to learn the program), compared to 73 hours for the manual analysis. Semi-automated content analysis provides an efficient alternative to manual qualitative data analysis, shifting it from a small-scale research activity to a more routine operational activity, albeit with some limitations. This is critical to be able to utilise at scale the rich narratives from consumers and clinicians in healthcare decision making.

摘要

以消费者为中心的有效医疗保健除了采用传统的定量指标外,还将消费者和临床医生的观点纳入决策过程。这些信息通常包含在需要人工分析的定性数据中。医疗保健系统往往缺乏资源,无法将定性反馈系统地纳入决策过程。诸如Leximancer之类的半自动内容分析工具,为耗时的人工内容分析(MCA)提供了一种高效且客观的替代方法。关于Leximancer在医疗保健领域有效性的文献很少。本研究旨在针对在医疗环境中收集的广泛情感对话数据集,将Leximancer与MCA进行验证对比。在COVID-19大流行开始时,澳大利亚一家大型医院和医疗服务机构与工作人员进行了互动网络直播,以提供最新信息并回答问题。对20份网络直播文字记录独立进行了人工主题分析和Leximancer内容分析。比较了分析结果以及完成每次分析所需的时间。Leximancer分析识别出9个概念,而人工分析识别出12个概念。Leximancer识别出的概念与人工分析识别出的12个概念中的5个相匹配,这5个概念占人工分析在文本中标记提及次数的74%。Leximancer遗漏了需要情感或上下文解释的概念。Leximancer分析耗时21小时(不包括学习该程序的时间),而人工分析耗时73小时。半自动内容分析为人工定性数据分析提供了一种有效的替代方法,将其从小规模研究活动转变为更常规的操作活动,尽管存在一些局限性。这对于能够在医疗保健决策中大规模利用消费者和临床医生的丰富叙述至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e170/9393405/0771649abaa2/10.1177_16094069221118993-fig1.jpg

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