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Abstract Objective To construct a nutrition support program for middle-aged and elderly patients with acute decompensated heart failure (ADHF) during hospitalization. Methods Based on the JBI Evidence-Based Health Care Model as the theoretical framework, the best evidence was extracted through literature analysis and a preliminary nutrition support plan for middle-aged and elderly ADHF patients during hospitalization was formed. Two rounds of expert opinion consultation were conducted using the Delphi method. The indicators were modified, supplemented and reduced according to the expert’s scoring and feedback, and the expert scoring was calculated. Results The response rates of the experts in the two rounds of consultation were 86.7% and 100%, respectively, and the coefficient of variation (CV) for each round was between 0.00% and 29.67% (all

Abstract Background This study aimed to provide insight into the training load of newly recruited nurses in grade-A tertiary hospitals in Shanghai, China. The lack of nurses in hospitals across China has resulted in newly recruited nurses in grade-A tertiary hospitals in Shanghai having to integrate into the work environment and meet the needs of the job quickly; thus, they undergo several training programs. However, an increase in the number of training programs increases the training load of these nurses, impacting the effectiveness of training. The extent of the training load that newly recruited nurses have to bear in grade-A tertiary hospitals in China remains unknown. Methods This qualitative study was conducted across three hospitals in Shanghai, including one general hospital and two specialized hospitals, in 2020. There were 15 newly recruited nurses who were invited to participate in semi-structured in-depth interviews with the purpose sampling method. A thematic analysis approach was used to analyze the data. The COREQ checklist was used to assess the overall study. Results Three themes emerged: external cognitive overload, internal cognitive overload, and physical and mental overload. Conclusion Through qualitative interviews, this study found that the training of newly recruited nurses in Shanghai’s grade-A tertiary hospitals is in a state of overload, which mainly includes external cognitive overload, internal cognitive overload, physical and mental overload, as reflected in the form of training overload, the time and frequency of training overload, the content capacity of training overload, the content difficulty of training overload, physiological load overload, and psychological load overload. The intensity and form of the training need to be reasonably adjusted. Newly recruited nurses need to not only improve their internal self-ability, but also learn to reduce internal and external load. Simultaneously, an external social support system needs to be established to alleviate their training burden and prevent burnout.

Newly registered nurses' emergency response capability falls short of their employers' expectations. Therefore, they need to develop this ability to cope with various changes in the clinic. Unfortunately, traditional learning has not been good enough in cultivating nursing skills and fostering self-directed learning which plays a key role in the learning process. Therefore, we attempted to integrate virtual simulation into the flipped learning format to optimize the training of newly registered nurses, and investigate its effectiveness. This study employs a pre- and post-test quasi-experimental design. The control and experimental groups each consisted of 43 newly registered nurses recruited in 2019 and 2020, respectively. The control group was trained using the traditional learning while the experimental group used the flipped learning format combined with virtual simulation for a 4-week emergency response training course. The data were collected through The Assessment Questionnaire of Clinical First-aid Capability of Nurses in the Non-Emergency Department and the Rating Scale of Self-directed Learning Competence for Nurses. Multivariate analysis of covariance was used for statistical analysis and the results show that the flipped learning combined with virtual simulation is more suitable and effective in improving nurses' emergency response abilities and self-directed learning compared to traditional learning.