背景:全世界迫切需要合格的卫生专业人员。卫生专业人员的流失率很高,再加上预期寿命的增长,进一步强调需要更多的卫生专业人员。与工作有关的压力是卫生专业人员的主要关注点,影响卫生专业人员的福祉和病人护理质量。
目的:本范围审查旨在确定使用自然语言处理(NLP)和文本挖掘技术在卫生专业人员中自动检测与工作相关的压力的过程和方法。
方法:本综述遵循JoannaBriggs研究所方法学和PRISMA-ScR(系统评价的首选报告项目和范围评价的Meta分析扩展)指南。本范围审查的纳入标准包括涉及卫生专业人员使用NLP进行与工作相关的压力检测的研究,而不包括涉及其他专业或儿童的研究。审查的重点是各个方面,包括用于压力检测的NLP应用,应力识别标准,NLP的技术方面,以及通过NLP进行压力检测的含义。考虑使用多种NLP技术在医疗保健环境中进行的研究,包括实验和观察设计,旨在全面了解NLP在检测卫生专业人员压力方面的作用。研究发表在英文,德语,或法国从2013年至今将被考虑。要搜索的数据库包括MEDLINE(通过PubMed),CINAHL,PubMed,科克伦,ACM数字图书馆,和IEEEXplore。要搜索的未发表的研究和灰色文献的来源将包括ProQuest论文和论文以及OpenGrey。两名审稿人将独立检索全文研究并提取数据。收集的数据将组织在表格中,graphs,和定性的叙述性总结。本综述将使用表格和图表来展示按年份分列的研究分布数据,国家,活动场,和研究方法。结果综合涉及识别,分组,和分类。最终的范围审查将包括详细说明搜索和研究选择过程的叙述性书面报告,使用PRISMA-ScR流程图的视觉表示,并讨论了对实践和研究的影响。
结果:我们预计结果将在2024年6月之前在系统范围审查中呈现。
结论:这篇综述通过使用NLP和文本挖掘在卫生专业人员中识别与工作相关的自动压力检测来填补文献空白,提供创新方法的见解,并确定进一步系统审查的研究需求。尽管有希望的结果,承认审查研究的局限性,包括方法上的限制,样本偏见,和潜在的监督,对于完善方法和推进卫生专业人员的自动压力检测至关重要。
■PRR1-10.2196/56267。
BACKGROUND: There is an urgent need worldwide for qualified health professionals. High attrition rates among health professionals, combined with a predicted rise in life expectancy, further emphasize the need for additional health professionals. Work-related stress is a major concern among health professionals, affecting both the well-being of health professionals and the quality of patient care.
OBJECTIVE: This scoping review aims to identify processes and methods for the automatic detection of work-related stress among health professionals using natural language processing (NLP) and text mining techniques.
METHODS: This review follows Joanna Briggs Institute Methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The inclusion criteria for this scoping review encompass studies involving health professionals using NLP for work-related stress detection while excluding studies involving other professions or children. The review focuses on various aspects, including NLP applications for stress detection, criteria for stress identification, technical aspects of NLP, and implications of stress detection through NLP. Studies within health care settings using diverse NLP techniques are considered, including experimental and observational designs, aiming to provide a comprehensive understanding of NLP\'s role in detecting stress among health professionals. Studies published in English, German, or French from 2013 to present will be considered. The databases to be searched include MEDLINE (via PubMed), CINAHL, PubMed, Cochrane, ACM Digital Library, and IEEE Xplore. Sources of unpublished studies and gray literature to be searched will include ProQuest Dissertations & Theses and OpenGrey. Two reviewers will independently retrieve full-text studies and extract data. The collected data will be organized in tables, graphs, and a qualitative narrative summary. This review will use tables and graphs to present data on studies\' distribution by year, country, activity field, and research methods. Results synthesis involves identifying, grouping, and categorizing. The final scoping review will include a narrative written report detailing the search and study selection process, a visual representation using a PRISMA-ScR flow diagram, and a discussion of implications for practice and research.
RESULTS: We anticipate the outcomes will be presented in a systematic scoping review by June 2024.
CONCLUSIONS: This review fills a literature gap by identifying automated work-related stress detection among health professionals using NLP and text mining, providing insights on an innovative approach, and identifying research needs for further systematic reviews. Despite promising outcomes, acknowledging limitations in the reviewed studies, including methodological constraints, sample biases, and potential oversight, is crucial to refining methodologies and advancing automatic stress detection among health professionals.
UNASSIGNED: PRR1-10.2196/56267.