Examining the Technostress Dimensions and Job Satisfaction in Nursing - A Cross-Sectional Study.

The healthcare system is increasingly being digitized. Besides expected benefits, the transformation can negatively affect nurses with increasing technostress. This study aimed to examine technostress among nurses and its association with job satisfaction. Cross-sectional survey data of 154 nurses working in acute hospitals in Switzerland was analyzed using Welch's ANOVA with the Games-Howell test and multiple linear regression model. Among the technostress dimensions, uncertainty was the most agreed upon by nurses, with a mean score of 2.21 (on a scale ranging from 0 to 4), and it differed significantly from other technostress dimensions. The multiple linear regression showed that the feeling of invasion of private life had the strongest negative association with job satisfaction (ß = -0.34). Nurses experience constant changes or new developments in the technologies in their organization. Therefore, health organizations should carefully plan their digital transformation processes to minimize simultaneous technology implementations and allow adaptation time.

Natural Language Processing for Work-Related Stress Detection Among Health Professionals: Protocol for a Scoping Review.

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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/56267.