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Article: The mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey

TitleThe mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey
Authors
KeywordsChina
cross-sectional study
human-machine
ICU
mental workload
nurse
tasks
Issue Date30-Apr-2024
PublisherWiley
Citation
Journal of Advanced Nursing, 2024 How to Cite?
Abstract

Aims: To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL. Design: A cross-sectional questionnaire study. Methods: Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used. Results: ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0–100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks. Conclusion: ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL. Impact: This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better. Patient or Public Contribution: No patient or public contribution.


Persistent Identifierhttp://hdl.handle.net/10722/350897
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 1.218

 

DC FieldValueLanguage
dc.contributor.authorYan, Yan-
dc.contributor.authorZhao, Chenglei-
dc.contributor.authorBi, Xuanyi-
dc.contributor.authorOr, Calvin Kalun-
dc.contributor.authorYe, Xuchun-
dc.date.accessioned2024-11-06T00:30:31Z-
dc.date.available2024-11-06T00:30:31Z-
dc.date.issued2024-04-30-
dc.identifier.citationJournal of Advanced Nursing, 2024-
dc.identifier.issn0309-2402-
dc.identifier.urihttp://hdl.handle.net/10722/350897-
dc.description.abstract<p>Aims: To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL. Design: A cross-sectional questionnaire study. Methods: Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used. Results: ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0–100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks. Conclusion: ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL. Impact: This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better. Patient or Public Contribution: No patient or public contribution.</p>-
dc.languageeng-
dc.publisherWiley-
dc.relation.ispartofJournal of Advanced Nursing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectChina-
dc.subjectcross-sectional study-
dc.subjecthuman-machine-
dc.subjectICU-
dc.subjectmental workload-
dc.subjectnurse-
dc.subjecttasks-
dc.titleThe mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey -
dc.typeArticle-
dc.identifier.doi10.1111/jan.16199-
dc.identifier.scopuseid_2-s2.0-85192082415-
dc.identifier.eissn1365-2648-
dc.identifier.issnl0309-2402-

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