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Background Hierarchical diagnosis and treatment system is crucial for deepening the medical and health system reform and establishing the basic medical and health system with Chinese characteristics. Primary care institutions play an essential role in hierarchical diagnosis and treatment system by assuming the role of "gatekeepers" of residents health. The post competency of general practitioners, who are the core of primary care institutions, significantly influences the service capacity of these institutions. Objective To construct an indicator system for the evaluation of post competency of general practitioners in primary care institutions. Methods From September to December 2022, 13 medical professionals were consulted through two-round Delphi expert consultation method; the hierarchical analysis and entropy method was used to calculate the weights of the indicators. Results The indicator system was optimized and sifted through two rounds of Delphi expert consultation, the evaluation system of primary general practitioners was finally constructed, consisting of 5 primary indicators and 27 secondary indicators. The weights of the indicators at all levels were clarified through the hierarchical analysis combined with the entropy method, in which the basic public health service capacity indicator had the highest weight and was the core indicator. The indicator system was tested for reliability and validity by small samples, and all of them were at an acceptable level. Conclusion An indicator system for evaluation the post competency of general practitioners in primary care institutions was constructed, contributing to the selection and appointment of general practitioners, which will provide an objective reference for the evaluation of post competence of general practitioners in primary care institutions.

Aim: This cross‐sectional study investigates the factors that contribute to academic resilience among nursing students during COVID‐19 pandemic. Design: A cross‐sectional study. Methods: A survey was conducted in a general hospital between November and December 2022. The Nursing Student Academic Resilience Inventory (NSARI) model was used to assess the academic resilience of 96 nursing students. The Boruta method was then used to identify the core factors influencing overall academic resilience, and rough set analysis was used to analyse the behavioural patterns associated with these factors. Results: Attributes were categorised into three importance levels. Three statistically significant attributes were identified ("I earn my patient's trust by making suitable communication," "I receive support from my instructors," and "I try to endure academic hardship") based on comparison with shadow attributes. The rough set analysis showed nine main behavioural patterns. Random forest, support vector machines, and backpropagation artificial neural networks were used to test the performance of the model, with accuracies ranging from 73.0% to 76.9%. Conclusion: Our results provide possible strategies for improving academic resilience and competence of nursing students. [ABSTRACT FROM AUTHOR]

Background: Improving overall and individual health literacy is a major focus of national initiatives in China and similar initiatives globally. However, few studies have examined the identification and improvement of individual health literacy levels, especially among patients. Purpose: To develop an interpretable method with decision rules to assess the health literacy levels of male patients and identify key factors influencing health literacy levels. Methods: Using a convenience sampling method, we conducted on-site surveys with 212 male patients of a hospital in China from July 2020 to September 2020. The questionnaire was developed by the Ministry of Health of the People's Republic of China. A total of 206 of the completed surveys were ultimately included for analyses in this study. The rough set theory was used to identify conditional attributes (ie, key factors) and decision attributes (ie, levels of health literacy) and to establish decision rules between them. These rules specifically describe how different combinations of conditional attributes can affect health literacy levels among men. Results: Basic knowledge, concepts, and health skills are important in identifying whether male patients have health literacy. Health skills, scientific health concepts, healthy lifestyles and behaviors, infectious disease prevention and control literacy, basic medical literacy, and health information literacy can be identified as cognitive behaviors with varying degrees of health literacy among patients. Conclusion: This model can effectively identify the key factors and decision rules for male patients' health literacy. Simultaneously, it can be applied to clinical nursing practice, making it easier for hospitals to guide male patients to improve their health literacy. [ABSTRACT FROM AUTHOR]

Hemodialysis is an important part of nosocomial infection prevention and control (IPC). This study aimed to identify the key potential risk areas and failure modes in hemodialysis rooms in hospitals and put forward a series of improvement measures to prevent and control the spread of the coronavirus disease 2019 (COVID‐19). Hemodialysis patients are highly susceptible to COVID‐19 and usually have a high incidence of severe illness and mortality after infection with COVID‐19. Therefore, IPC in hemodialysis patients is of crucial strategic significance. Based on 30 domain experts' interviews and careful analysis of prevention and control documents, we constructed a comprehensive failure system for a model that identifies the potential risks for nosocomial COVID‐19 infection in the hemodialysis room. Subsequently, a thorough risk assessment of the potential failure factors identified in our model was conducted. The failure key factors corresponding to the human element in medical waste (garbage) disposal (C2) are verified to be the highest risk factors. They are as follows: The cleaning staff did not dispose of different types of medical waste (garbage) (C21), did not wear masks according to the regulations (C22), and lacked knowledge and norms of nosocomial IPC (C23). This study provides valuable insights for hospital decision‐makers on the potential failure factors related to COVID‐19 infections in hemodialysis rooms. By working with hospital infection specialists, the suggested improvement measures can help reduce the risk of virus exposure among hospital medical staff, patients, and cleaning staff. [ABSTRACT FROM AUTHOR]

Purpose: To understand key factors for shared decision‐making (SDM) and the quality improvement of nursing decisions in the orthopaedic clinical environment. Method: This study applied the consistent consistent fuzzy preference relations (CFPRs) and importance‐performance analysis (IPA) methods to explore the attribute weights and SDM performance, respectively. The dataset was collected from 16 orthopaedic clinical nurses' experiences in a third‐grade and first‐level general hospital in Taizhou, China. This study was performed according to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines. Results: The results showed that "I made clear to my patient or patient's family that a nursing care decision needs to be made", "I explained the advantages and disadvantages of the nursing care options to my patient or patient's family" and "I told my patient or patient's family that there are different nursing care options for caring his/her medical condition" were key factors for affecting SDM. The statistical significance confidence and difference error of weight results were 98.321% and 1.679%, respectively. In addition, "I asked my patient or patient's family which nursing care option he/she prefers" was the key factor for improving orthopaedic clinical nursing in the case hospital. Conclusion: The hybrid CFPRs‐IPA model can help hospital managers effectively understand the key factors of SDM quality and improve the orthopaedic clinical nursing performance from nurses' perspectives. Relevance to Clinical Practice: A quantitative decision‐making model can help nurses understand the key factors affecting the quality of SDM in nursing decision‐making and promote nursing decision‐making and patient‐centred nursing service quality. A series of corresponding SDM training courses (i.e. concepts, knowledge and skills) can be provided for hospital and nursing department managers to maximise the potentially available resources. Patient or Public Contribution: The clinical care process should be committed to involving patients in their care decisions and also provide an opportunity for patients to gain a comprehensive understanding of the care decision‐making process in order to inform future patient contributions to care decisions. [ABSTRACT FROM AUTHOR]