Nursing undergraduates’experiences of a simulation-centred educational program in hospice care in Macao:A qualitative research

Objectives:The study aimed to explore the experiences of nursing undergraduates participating in a simulation-centred educational program in hospice care in Macao,China.Methods:This descriptive qualitative study was based on the data collected through semi-structured individual interviews.Seventeen nursing undergraduates in Macao,China who attended the simulation-centred program in hospice care participated in this qualitative from November to December 2020.This program included three parts:introduction to hospice care(2 h),management of terminal symptoms(10 h),and hospice situation simulations(6 h).The interview data were analyzed using qualitative content analysis.Results:This study revealed two themes and six sub-themes.Theme 1 was developing competencies in caring for dying patients and their families,which included four subcategories of sensitivity to patients’needs,knowledge of hospice care,skills of symptom control and comfort supply,and communication skills.Theme 2 was improving the ability to self-care and support colleagues,which included two subcategories of reflection on life and death and sharing and supporting among colleagues.Conclusion:This program improved the competency of nursing undergraduates in hospice care and participants’learning experience was good.

社区居家老年人跌倒经历的体验:质性研究的meta整合

目的系统评价和整合社区居家老年人跌倒经历的看法与体验,为后续发展跌倒预防及管理的干预提供信息,为健康照护实践提供建议。方法依据乔安娜布里格斯研究所(Joanna Briggs Institute,JBI)质性系统评价方法论进行质性研究meta整合,在PubMed、CINAHL、PsycInfo、Embase、Web of science等数据库进行检索,发表时间限定为2010年1月至2023年5月,仅纳入经同行评审的英文文献。使用JBI质性研究质量评价标准评价纳入文献的方法学质量,按照ConQual评分准则检验整合结果的信度。本综述方案已在PROSPERO注册(CRD 42023421789)。结果共纳入10项研究,方法学质量评价得分为60%至90%。提炼59个完好明确的结果,将相似结果归纳组合形成7个类别,并综合成3个整合结果,在ConQual评分上,综合结果2评为中等,综合结果1和3评为低等。整合结果1:跌倒后老年人经历了身体的损伤和疼痛、日常活动受限、社交受限、独立性降低或丧失、并有恐惧和无助感。整合结果2:经历跌倒后,老年人反思跌倒的原因,并识别、解释风险因素。整合结果3:老年人对跌倒原因和后果有正面和负面的看法,其采取的应对跌倒的措施包括使用辅助器具、纠正风险因素、寻求医疗帮助、关注身体和心理状况等。结论医疗卫生提供者应重视老年人跌倒后的感受、经历,以及对跌倒原因和影响的反思,进行特定的干预。今后还需要进行纵向研究,以检测跌倒对老年人的长期影响,深入了解他们的反思、感知、态度和预防行为随时间的改变情况。

Improving innovation and entrepreneurship education in higher institutions: the impact of "Mass Entrepreneurship and Innovation" education on undergraduates' entrepreneurial intentions

The exploration of the way"mass entrepreneurship and innovation"(MEI)education influences students'aspirations to become entrepreneurs has grown into an important area of analysis in studies related to higher education.This research intends to examine the consequences of MEI education on students'tendency towards entrepreneurship,and to put forward methods for augmenting the teaching of innovation and entrepreneurship in private higher educational establishments.To achieve this objective,questionnaires and semi-structured interviews were employed in the study,which involved a total of 197 students and five education experts.The statistical analysis of the questionnaire data revealed that MEI education was positively related to students'entrepreneurial intentions,and that both entrepreneurial experience and family entrepreneurial background played moderating roles in this relationship.The interview findings indicated that private universities could enhance educational reforms by designing talent training programs,developing diversified curricula,and developing more professional entrepreneurial platforms to encourage students'entrepreneurial intentions.This study offers fresh insights for improving and perfecting the mechanism of innovation and entrepreneurship education in private universities.

Nurses’job embeddedness and turnover intention:A systematic review and meta-analysis

Objective:This study aimed to review the relationship between job embeddedness and turnover intentions among nurses and explore the effects of the Job Embeddedness Scale,number of years in the career,education,and marital status on this relationship.Methods:The review was conducted by searching the China Knowledge Resource Integrated Database(CNKI),Weipu Database(CQVIP),China Biology Medicine(CBM),Wanfang Database,PubMed,Web of Science,Embase,CINAHL,and APA-PsycNet for articles on nurses’job embeddedness and turnover from intention up to March 2024.The research quality was evaluated using the Agency for Healthcare Research and Quality(AHRQ)assessment criteria.The review protocol has been registered on PROSPERO[CRD42023483947].Results:The results of this review included 47 studies consisting of 15,742 nurses from seven countries worldwide.A moderate negative correlation was found between job embeddedness and turnover intention(r=0.487).Furthermore,on-the-job embeddedness(r=0.527)was more negatively associated with turnover intention than off-the-job embeddedness(r=0.234).The highest negative correlation was found between sacrifice and turnover intention(r=0.460),while the lowest was for the link(r=0.185).Furthermore,the relationship between job embeddedness and its dimensions with turnover intention was affected by different job embeddedness scales,number of years in the career,education,and marital status(P<0.05).Conclusion:This systematic review and meta-analysis analyzed the relationships between nurses’job embeddedness,dimensions,and turnover intention.Meanwhile,subgroup analysis and meta-regression explored the factors influencing these relationships.It is an important reference for nurse managers to promote nurse retention.

Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.

Structural characterization and mechanisms of macrophage immunomodulatory activity of a novel polysaccharide with a galactose backbone from the processed Polygonati Rhizoma

A purified polysaccharide with a galactose backbone(SPR-1,Mw 3,622 Da)was isolated from processed Polygonati Rhizoma with black beans(PRWB)and characterized its chemical properties.The backbone of SPR-1 consisted of[(4)-b-D-Galp-(1]9/4,6)-b-D-Galp-(1/4)-a-D-GalpA-(1/4)-a-D-GalpA-(1/4)-aD-Glcp-(1/4,6)-a-D-Glcp-(1/4)-a/b-D-Glcp,with a branch chain of R1:b-D-Galp-(1/3)-b-D-Galp-(1/connected to the/4,6)-b-D-Galp-(1/via O-6,and a branch chain of R2:a-D-Glcp-(1/6)-a-D-Glcp-(1/connected to the/4,6)-a-D-Glcp-(1/via O-6.Immunomodulatory assays showed that the SPR-1 significantly activated macrophages,and increased secretion of NO and cytokines(i.e.,IL-1b and TNF-a),as well as promoted the phagocytic activities of cells.Furthermore,isothermal titration calorimetry(ITC)analysis and molecular docking results indicated high-affinity binding between SPR-1 and MD2 with the equilibrium dissociation constant(KD)of 18.8 mM.It was suggested that SPR-1 activated the immune response through Toll-like receptor 4(TLR4)signaling and downstream responses.Our research demonstrated that the SPR-1 has a promising candidate from PRWB for the TLR4 agonist to induce immune response,and also provided an easily accessible way that can be used for PR deep processing。

Neuroimaging features in a patient with non-ketotic hyperglycaemic seizures: A case report

BACKGROUND Non-ketotic hyperglycaemic(NKH)seizures are a rare neurological complication of diabetes caused by hyperglycaemia in non-ketotic and non-hyperosmotic states.The clinical characteristics of NKH seizures are atypical and lack unified diagnostic criteria,leading to potential misdiagnoses in the early stages of the disease.CASE SUMMARY This report presents a rare case of NKH seizures in a 52-year-old male patient with a history of type 2 diabetes mellitus.We performed comprehensive magnetic resonance imaging(MRI)studies at admission,12 d post-admission,and 20 d post-discharge.The imaging techniques included contrast-enhanced head MRI,T2-weighted imaging(T2WI),fluid-attenuated inversion recovery(FLAIR),diffusion-weighted imaging,susceptibility-weighted imaging,magnetic reso-nance spectroscopy(MRS),and magnetic resonance venography.At the time of admission,T2WI and FLAIR of the cranial MRI showed that the left parieto-occipital cortex had gyrus-like swelling and high signal,and subcortical stripes had low signal.MRS showed a reduced N-acetylaspartate peak and increased creatine and choline peaks in the affected areas.A follow-up MRI 20 d later showed that the swelling and high signal of the left parieto-occipital cortex had disappeared,and the low signal of the subcortex had disappeared.CONCLUSION This case study provides valuable insights into the potential pathogenesis,diagnosis,and treatment of NKH seizures.The comprehensive MRI findings highlight the potential utility of various MRI sequences in diagnosing and characterizing NKH seizures.

Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS^(2) library: Sishen formula as a case

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges.Conventional strategies always exhibit the insufficiencies in overall coverage,analytical efficiency,and degree of automation,and the results highly rely on the personal knowledge and experience.The goal of this work was to establish a software-aided approach,by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry(UHPLC/IM-QTOF-MS)and in-house high-definition MS^(2) library,to enhance the identification of prototypes and metabolites of the compound formulae in vivo,taking Sishen formula(SSF)as a template.Seven different MS2 acquisition methods were compared,which demonstrated the potency of a hybrid scan approach(namely high-definition data-independent/data-dependent acquisition(HDDIDDA))in the identification precision,MS1 coverage,and MS^(2) spectra quality.The HDDIDDA data for 55 reference compounds,four component drugs,and SSF,together with the rat bio-samples(e.g.,plasma,urine,feces,liver,and kidney),were acquired.Based on the UNIFI™platform(Waters),the efficient data processing workflows were established by combining mass defect filtering(MDF)-induced classification,diagnostic product ions(DPIs),and neutral loss filtering(NLF)-dominated structural confirmation.The high-definition MS^(2) spectral libraries,dubbed in vitro-SSF and in vivo-SSF,were elaborated,enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples.Consequently,118 prototypes and 206 metabolites of SSF were identified,with the identification rate reaching 80.51%and 79.61%,respectively.The metabolic pathways mainly involved the oxidation,reduction,hydrolysis,sulfation,methylation,demethylation,acetylation,glucuronidation,and the combined reactions.Conclusively,the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer

Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final features.The diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value.Calibration and decision-curve analyses were also performed.Results The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer.The performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,respectively.The validation set of the model was assessed using clinical decision curves,calibration curves,and confusion matrices,which collectively demonstrated the model's stability and accuracy.Conclusion In this study,information on the deep radiomics of Vgg16 was obtained from computed tomography images,and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.

Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.

ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Return to Reality:Speculative Design of Interaction Ritual Chains and Exploration of Alone Together

“Alone together”is an ever-changing“wicked problem.”In this research and practice,the author tries to combine experience design,referring to the theory of“interaction ritual chain theory,”and creatively add interactive experience to discursive works,so that the audience can think between the real and the virtual.

Side Effects of Gamifying Environmental Education Activities in Chinese Kindergartens

Environmental education is an effective approach to addressing environmental issues,and incorporating environmental education into kindergarten through gamified activities aligns with the concept of gamifying teaching and provides the optimal pathway for implementing environmental education.The purpose of this study is to investigate the specific processes involved in determining the objectives,themes,and content of gamified environmental education activities,as well as the organization,implementation,and evaluation of these activities in kindergarten settings.Five classes from Class G in Xining City Kindergarten were selected as the observational subjects for this study.Interviews were conducted with the teaching staff and the head of the kindergarten.The data obtained from observations and interviews served as the primary data for this research.The results indicate that the activity objectives formulated by teachers lack scientific basis and operability,with limited incorporation of gaming elements.The activity themes and content are narrow in scope and primarily determined by teachers and kindergarten administrators.The organization and implementation of activities often neglect the playful experiences of children,and activity evaluation is not given sufficient attention.

Chinese High School Students' Global Competence: Development and Assessment

With the advancement of globalization and digital technology,students'ability to live in an interconnected world has become increasingly important.Global competence has gradually become an important indicator for assessing students'progress.In the study of global competence,Western countries took the lead,followed by China.China has gradually transitioned from learning from the ideas of other countries to the construction of global competence education with Chinese characteristics,demonstrating a catch-up trend.So,based on the PISA Global Competence Framework,this paper aims to interpret what global competence is,how to develop students'global competence in the context of Chinese school practices,and how to assess students'global competence using the PISA 2018 Global Competence Framework.As a result,it can substantially improve global competency research and development.

Understanding the pregnant women’s experiences during the COVID-19 pandemic:A qualitative study

Objective To explore and interpret the experiences of pregnant women in Macao region,China during the COVID-19 pandemic.Methods Recruitment advertisements were published through multiple social platforms in Macao region,China.A purposive snowball sampling method was adopted to select interviewees.Eighteen women who were confirmed as pregnant from January to May 2020 participated in this qualitative study.Data was collected from November to December 2020 using in-depth personal interviews.One-to-one interviews were conducted by telephone to avoid personal contact.Thematic analysis was used to perform the data analysis and identify emergent themes.Results Five themes emerged from the data analysis:changes in daily life,psychological distress,unique experiences of pregnancy follow-up,trying to pay attention to health information but also feeling overwhelmed,and change in hygiene behaviors due to fear of infection.Six sub-themes were identified:being confined at home but understanding the reasons,financial pressures and timely support from the government,perceived risk of catching the infection,retaining optimism with various help and support,adequate personal protections,and obsessive hygiene behaviors.Conclusion During a pandemic,there is a risk of greater individual isolation,particularly for vulnerable groups such as women in pregnancy.The humanized attention to and support for the residents from the government buffered the adverse impact on the study participants.Preplanning for such events is needed to focus on psychological distress,financial constraints,and prenatal health services.Alternative service delivery,such as telemedicine,online counseling,and virtual reality(VR)technology,should be applied to offer pregnant women timely support and avoid a crisis.

Focus-RCNet:a lightweight recyclable waste classification algorithm based on focus and knowledge distillation

Waste pollution is a significant environmental problem worldwide.With the continuous improvement in the living standards of the population and increasing richness of the consumption structure,the amount of domestic waste generated has increased dramatically,and there is an urgent need for further treatment.The rapid development of artificial intelligence has provided an effective solution for automated waste classification.However,the high computational power and complexity of algorithms make convolutional neural networks unsuitable for real-time embedded applications.In this paper,we propose a lightweight network architecture called Focus-RCNet,designed with reference to the sandglass structure of MobileNetV2,which uses deeply separable convolution to extract features from images.The Focus module is introduced to the field of recyclable waste image classification to reduce the dimensionality of features while retaining relevant information.To make the model focus more on waste image features while keeping the number of parameters small,we introduce the SimAM attention mechanism.In addition,knowledge distillation was used to further compress the number of parameters in the model.By training and testing on the TrashNet dataset,the Focus-RCNet model not only achieved an accuracy of 92%but also showed high deployment mobility.

Multi-Objective Optimization of Traffic Signal Timing at Typical Junctions Based on Genetic Algorithms

With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.

Psychological experience of infected individuals during the pandemic: A qualitative meta-synthesis

The purpose of this review is to integrate the psychological experience of infected individuals during the pandemic.The spread of the pandemic has led to psychological and emotional impacts on patients.A qualitative synthesis is needed to identify,appraise,and integrate the available qualitative findings to provide an evidence for the development of interventions.A meta-aggregation approach was used to analyze studies published in English from January 2020 to August 2021.The JBI Qualitative Assessment and Review Instrument was used to assess the methodological quality of included studies.The ConQual system was used to establish the dependability and credibility in the synthesized findings.Six phenomenological studies and one narrative inquiry with an overall quality score of 70%-100% were included.The research findings from 87 participants in eligible studies were aggregated into three categories based on similarity of meaning.Two synthesized findings were generated and rated as moderate based on the ConQual score.The synthesized findings suggested that participants had psychological distress such as fear,anxiety,loneliness,and uncertainty,but they accepted the fact of being infected and tried to adjust themselves using some self-coping strategies,such as diverting attention from disease,seeking help from professional counselors,relying on religious beliefs,and participating in religious activities.They would like to learn more about infections and diseases and needed more informational support from health-care professionals.They were looking forward to recovery from the disease.

Marketing Model Analysis of Fashion Communication Based on the Visual Analysis of Neutrosophic Systems

In order to Improvement the Neutrosophic sets as effective tools to deal with uncertain and inconsistent information.The research takes method-ology of combined single-valued neutrosophic rough set and multi-scale deci-sion systems.This paper proposes the optimal scale selection and reduction algorithms based on multi-scale single-valued neutrosophic dominance rough set model.User requirements were analyzed using KJ method to construct a hierarchical model.According to the statistics of representative studies from China and the West,we found that,on the one hand,classical theory has been expanded and supplemented in fashion culture communication and market-ing.The topics are more micro-diverse,and the research methods are inspired by other disciplines;on the other hand,Chinese practice and Chinese cultural perspective need to fill the gap.The fashion content in the new fashion,however,needs to broaden its boundaries,and in addition to integrating with cultural theory and sociology,it needs to be integrated with fashion products,including product design,visual communication,image design and so on.Aesthetic communication needs to be taken into account as an important connotation,with visual communication and the communication of images as important research elements.On the whole,this research abroad inspires the development of domestic fashion culture communication and marketing research.

Advance care planning readiness for Chinese older people:An integrative review and conceptual framework

Advance care planning is a process of discussion in which patients can communicate their end-of-life care preferences to family members and health care providers for consideration.Readiness for advance care planning is a patient's preparedness to engage in advance care planning.This review aims to develop the conceptual framework for advance care planning readiness for Chinese older people.The current knowledge from the published studies was identified and synthesized by an integrative review.The conceptual framework was developed based on the social-ecological model and the theory of planned behavior.The factors from the social environment/community,health care professionals,and individual/family layers were defined.These factors may influence an individual's medical decision-making,which in turn triggers individual behavioral mechanisms that arise from interactions between motivations,attitudes,and beliefs.Relevant factors should be considered when assessing the behavior of personnel engaged in advance care planning or formulating appropriate intervention measures to improve advance care planning par ticipation in China.This framework can be used to guide studies that explore how the social/familial/individual factors predict the readiness for advance care planning among Chinese older people,and to design intervention studies to test the effect of family function on the readiness for advance care planning.