The Mental Workload of Intensive Care Unit Nurses in Performing Human-Machine Tasks: A Cross-Sectional Survey

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Care Unit Nurses in Performing Human-Machine Tasks: A Cross-Sectional Survey</title> <meta name="description" content="The Mental Workload of Intensive Care Unit Nurses in Performing Human-Machine Tasks: A Cross-Sectional Survey"> <meta name="keywords" content="mental workload, nurse, ICU, human-machine, tasks, cross-sectional study, linear mixed model, China"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a 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87404</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">The Mental Workload of Intensive Care Unit Nurses in Performing Human-Machine Tasks: A Cross-Sectional Survey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Yan">Yan Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=Erhong%20Sun"> Erhong Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Lin%20Peng"> Lin Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuchun%20Ye"> Xuchun Ye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aims: The present study aimed to explore Intensive Care Unit (ICU) nurses’ mental workload (MWL) and associated factors with it in performing human-machine tasks. Background: A wide range of emerging technologies have penetrated widely in the field of health care, and ICU nurses are facing a dramatic increase in nursing human-machine tasks. However, there is still a paucity of literature reporting on the general MWL of ICU nurses performing human-machine tasks and the associated influencing factors. Methods: A cross-sectional survey was employed. The data was collected from January to February 2021 from 9 tertiary hospitals in 6 provinces (Shanghai, Gansu, Guangdong, Liaoning, Shandong, and Hubei). Two-stage sampling was used to recruit eligible ICU nurses (n=427). The data were collected with an electronic questionnaire comprising sociodemographic characteristics and the measures of MWL, self-efficacy, system usability, and task difficulty. The univariate analysis, two-way analysis of variance (ANOVA), and a linear mixed model were used for data analysis. Results: Overall, the mental workload of ICU nurses in performing human-machine tasks was medium (score 52.04 on a 0-100 scale). Among the typical nursing human-machine tasks selected, the MWL of ICU nurses in completing first aid and life support tasks (‘Using a defibrillator to defibrillate’ and ‘Use of ventilator’) was significantly higher than others (p < .001). And ICU nurses’ MWL in performing human-machine tasks was also associated with age (p = .001), professional title (p = .002), years of working in ICU (p < .001), willingness to study emerging technology actively (p = .006), task difficulty (p < .001), and system usability (p < .001). Conclusion: The MWL of ICU nurses is at a moderate level in the context of a rapid increase in nursing human-machine tasks. However, there are significant differences in MWL when performing different types of human-machine tasks, and MWL can be influenced by a combination of factors. Nursing managers need to develop intervention strategies in multiple ways. Implications for practice: Multidimensional approaches are required to perform human-machine tasks better, including enhancing nurses' willingness to learn emerging technologies actively, developing training strategies that vary with tasks, and identifying obstacles in the process of human-machine system interaction. <iframe src="https://publications.waset.org/abstracts/160977.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mental%20workload" title="mental workload">mental workload</a>, <a href="https://publications.waset.org/abstracts/search?q=nurse" title=" nurse"> nurse</a>, <a href="https://publications.waset.org/abstracts/search?q=ICU" title=" ICU"> ICU</a>, <a href="https://publications.waset.org/abstracts/search?q=human-machine" title=" human-machine"> human-machine</a>, <a href="https://publications.waset.org/abstracts/search?q=tasks" title=" tasks"> tasks</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-sectional%20study" title=" cross-sectional study"> cross-sectional study</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=China" title=" China"> China</a> </p> <a href="https://publications.waset.org/abstracts/160977/the-mental-workload-of-intensive-care-unit-nurses-in-performing-human-machine-tasks-a-cross-sectional-survey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160977.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div 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The Mental Workload of Intensive Care Unit Nurses in Performing Human-Machine Tasks: A Cross-Sectional Survey