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

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.

Psychometric evaluation of the Chinese version of the supportive care needs survey for caregivers of children with Paediatric cancer (SCNS-C-Ped-C).

AIM: To revise the Supportive Care Needs Survey for Partners and Caregivers of Cancer Patients (SCNS-P&C) and evaluate the psychometric properties of the Chinese Version of the Supportive Care Needs Survey for Caregivers of Children with Paediatric Cancer (SCNS-C-Ped-C) in caregivers of children with paediatric cancer. DESIGN: A cross sectional design was used. METHODS: In this methodological research, the reliability and validity of the SCNS-C-Ped-C were measured by a questionnaire survey among 336 caregivers of children with paediatric cancer in China. The construct validity was evaluated by exploratory factor analysis and internal consistency was examined by Cronbach's alpha, split-half reliability, and corrected item-to-total correlation coefficients. RESULTS: The exploratory factor analysis revealed six factors consist of: Healthcare and Informational Needs, Daily Care and Communication Needs, Psychological and Spiritual Needs, Medical Service Needs, Economic Needs, and Emotional Needs, explaining 65.615% of the variance. The Cronbach's alpha was 0.968 at full scale and 0.603-0.952 on the six domains. The split-half reliability coefficient was 0.883 at full scale and 0.659-0.931 on the six domains. CONCLUSIONS: The SCNS-C-Ped-C demonstrated both reliability and validity. It can be used to evaluate multi-dimensional supportive care needs for caregivers of children with paediatric cancer in China.

Chinese parents' caregiving ability for children with haematological malignancies: A latent class analysis.

AIM: The aim of this study was to identify unobserved subgroups of Chinese parents' caregiving ability for children with haematological malignancies and examine the associations of the latent class membership with individual characteristics. DESIGN: A multicentre cross-sectional survey study was conducted. METHODS: A total of 392 parents of children with haematological malignancies in China were surveyed with the Hematologic Malignancies' Family Caregiver Skills Scale and a study-specific demographic information questionnaire. Latent class analysis (LCA) and multinomial logistic regression model were applied in data analysis. RESULTS: LCA results suggested that there existed three distinct a priori unknown classes of parents of children with haematological malignancies in regard to caregiving ability: Class 1-"high caregiving ability" class (n = 131, 33.4%), Class 2-"medium caregiving ability" class (n = 170, 43.4%) and Class 3-"low caregiving ability" class (n = 91, 23.2%). Socio-demographics and clinical characteristics had significant associations with the latent class membership.

Heterogeneity of Attitudes toward Robots in Healthcare among the Chinese Public: A Latent Profile Analysis.

Attitudes are deemed critical psychological variables that can determine end users' acceptance and adoption of robots. This study explored the heterogeneity of the Chinese public's attitudes toward robots in healthcare and examined demographic characteristics associated with the derived profile membership. The data were collected from a sample of 428 Chinese who participated in an online survey. Latent profile analysis identified three distinct subgroups regarding attitudes toward robots-optimistic (36.9%), neutral (47.2%), and ambivalent (15.9%). Interestingly, although participants in the ambivalent attitude profile held more negative attitudes toward interaction with or social influence of healthcare robots, their attitudes tended to be positive when it came to emotional interactions with healthcare robots. All the respondents reported negative attitudes toward the social influence of healthcare robots. Multivariable regression analysis results showed that there were significant differences in age, education level, monthly income, experience with computers, experience with wearable devices, and whether to follow robot-related news or not. This study confirmed the heterogeneity of the Chinese public's attitudes toward robots in healthcare and highlighted the importance of emotional interaction with and social influence of healthcare robots, which might facilitate a better understanding of the needs and expectations of potential end users for robots in healthcare to make them more acceptable in different situations.