中西医结合型公共卫生人才培养是应对复杂公共卫生挑战的重要方向，但国内外普遍存在整合医学与群体健康脱节、实践薄弱及标准缺失等问题。大连医科大学依托其国家重点学科及国家中西医协同“旗舰”医院，探索构建了以问题导向模块化课程、“临床—社区”双轨实践体系及量化评价追踪机制为核心的系统化培养模式。数据显示，新模式培养后学生“治未病”应用认知优秀率从28%提升至65%；社区老年高血压管理项目中，患者血压达标率提高25%；毕业1-3年的学生中93%认为该模式对处理复杂公共卫生问题“至关重要”。该模式直面国内外双重困境，实现了从知识拼接向能力融合、从理论讲授向实践闭环、从主观评价向数据追踪的三大创新，在系统性、实操性和可评估性上形成独特优势，为我国新医科背景下中西医结合型公共卫生人才培养提供了可复制、可量化的范式参考。

       目的   基于结构方程模型（SEM）验证早产儿母亲育儿胜任感的多路径作用机制。方法  采用便利抽样法选取2024年6月—2025年6月在莆田学院附属医院分娩的早产儿母亲250例作为研究对象。采用一般资料调查表、中文版育儿胜任感量表（C-PSOC）、婴儿母亲育儿支持问卷（PSM）、角色适应问卷、简式亲职压力量表收集数据。通过单因素分析及多元线性回归分析母亲育儿胜任感的影响因素，使用AMOS软件构建结构方程模型，分析早产儿分娩后母亲育儿胜任感的作用路径。结果  250例早产儿母亲的C-PSOC得分为（61.93±6.02）分，多元线性回归分析结果显示，早产儿母亲育儿胜任感的影响因素包括产次、育儿支持、角色适应、亲职压力（均P＜0.05）。结构方程模型拟合良好（χ ² /df=1.026，GFI=0.987，AGFI=0.978，NFI=0.987，CFI=1.000，RMSEA=0.010），其中角色适应正向预测育儿胜任感（β=0.344），育儿支持（β=-0.477）与亲职压力（β=-0.283）负向预测（均P＜0.05），并且角色适应通过育儿支持、亲职压力间接提升育儿胜任感（效应值0.467）；产次经角色适应间接降低压力源影响（效应值0.529）。结论  早产儿母亲育儿胜任感受多路径机制调控，临床需针对角色适应、育儿支持及亲职压力设计级联干预策略。

       Objective  To verify the multi-pathway mechanism of parenting competence of premature infant mothers based on structural equation modeling（SEM）．Methods  A convenience sampling method was used to select 250 mothers of preterm infants who delivered in Affiliated Hospital of Putian University between June 2024 and June 2025 as the study subjects．Data was collected using a general information survey，the Chinese version of the Parenting Sence of Competence Scale（C-PSOC），the Parenting Support Questionnaire for Infant Mothers（PSM），the Role Adaptation Questionnaire，and the Simplified Parenting Stress Scale．By conducting  single factor analysis and multiple linear  regression analysis on the influencing factors of maternal parenting competence，a structural equation model was constructed using AMOS software to analyze the pathway of maternal parenting competence after premature birth．Results  The C-PSOC score of 250 mothers of premature infants was（61.93±6.02）．Multiple linear  regression analysis showed that the influencing factors of parenting competence among mothers of premature infants included parity，parenting support，role adaptation，and parental pressure（all P＜0.05）．The structural equation model fits well（2/df=1.026，GFI=0.987，AGFI=0.978，NFI=0.987，CFI=1.000，RMSEA=0.010），which  role adaptation  positively  predicted parenting competence（β=0.344），parenting support（β=-0.477）and parenting stress（β=-0.283）negatively predicted（all P＜0.05），and role adaptation indirectly enhanced parenting competence through parenting support and parenting stress（effect value 0.467）．The adaptation of roles during childbirth indirectly reduced the impact of stressors（effect value 0.529）．Conclusions  The multi-pathway mechanism of parental competence perception regulation in premature infant mothers requires the design of cascading intervention strategies targeting role adaptation，parenting support，and parental stress in clinical practice．

       目的  探讨结肠镜下息肉切除术后复发的危险因素，并基于机器学习算法构建复发风险预警模型，为防治对策提供依据。方法  回顾性收集2018年9月—2023年9月六安市人民医院1 058例初次行无痛结肠镜下息肉切除术患者的临床资料，使用单因素和多因素Logistic回归分析筛选复发危险因素。采用7∶3随机抽样分为训练集和验证集，分别通过决策树、贝叶斯及Logistic回归算法构建预测模型，并以受试者工作特征曲线（ROC）曲线下面积（AUC）、灵敏度、特异度等指标来评估模型效能。结果  单因素分析显示，性别、吸烟、代谢综合征、息肉数量、息肉位置、山田分型、组织病理学类型、切除方式、复查时间、肠息肉直径、手术时间是复发的危险因素（P＜0.05）。多因素分析显示，性别、代谢综合征、息肉数量、息肉直径、肠息肉位置、山田分型、组织学病理类型、切除方式、手术时间均是结肠息肉内镜下切除术后复发的危险因素。模型评估显示，决策树算法、贝叶斯算法、Logistic回归算法的ROC曲线下面积（AUC）分别为0.849、0.818、0.811；灵敏度分别为85.14%、81.62%、79.43%；特异度分别为81.69%、79.45%、74.18%；约登指数分别为0.534、0.551、0.573；95%CI分别为0.810~0.876、0.794~0.860、0.782~0.850；决策树算法模型效能最佳，Logistic回归算法的性能最差。结论  性别、代谢综合征、肠息肉特征（数量、直径、位置等）是术后复发的关键危险因素。决策树模型在风险预测中表现最优，可为临床制定个体化随访策略提供参考。

       Objective  To explore the  risk factors for  recurrence after painless colonoscopic polypectomy and construct a recurrence risk warning model based on machine learning algorithms to provide evidence for prevention and treatment strategies．Methods  A retrospective analysis was conducted on clinical data from 1 058 patients who underwent their first painless colonoscopy-guided polypectomy at our hospital between September 2018 and September 2023．Univariate and multivariate Logistic  regression analyses were performed to identify recurrence risk factors．The dataset was randomly divided into training and validation sets using a 7∶3 ratio．Prediction models were constructed using decision tree，Bayesian，and Logistic regression algorithms，and their performance was evaluated using metrics such as the area under the receiver operating characteristic curve（AUC），sensitivity，specificity，and others．Results  Univariate analysis revealed that gender，smoking，metabolic syndrome，number of polyps，polyp location，Yamada classification，histopathological type，resection method，follow-up time，polyp diameter，and operation duration were risk factors for recurrence（P＜0.05）．Multivariate analysis identified gender，metabolic syndrome，number of polyps，polyp diameter，polyp location，Yamada classification，histopathological type，resection method，and operation duration as independent risk factors for recurrence after endoscopic polypectomy．Model evaluation showed AUC values of 0.849，0.818，and 0.811 for the decision tree，Bayesian，and Logistic regression algorithms，respectively．Sensitivity values were 85.14%，81.62%，and 79.43%；specificity values were 81.69%，79.45%，and 74.18%；Youden’s indices were 0.534，0.551，and 0.573；and 95% confidence intervals（CIs）were 0.810–0.876，0.794–0.860，and 0.782–0.850，respectively．The  decision tree algorithm demonstrated the best predictive performance，while the Logistic regression algorithm performed the least favorably．Conclusions  Gender，metabolic syndrome，and polyp characteristics（number，diameter，location，etc．）are key  risk factors for recurrence after polypectomy．The decision tree algorithm exhibited optimal predictive efficacy，offering valuable insights for developing individualized follow-up strategies in clinical practice．

      目的   基于决策树构建老年患者吞咽障碍预警模型。方法  采用便利取样法对宁夏银川市宁夏回族自治区人民医院老年科住院的200例老年患者进行调查。结果  200例老年患者中，吞咽障碍发生率为40.5%。依据是否发生吞咽障碍将其患者分为两组，两组患者在性别、年龄、文化程度、职业、医保类型、家庭年收入、日常生活能力、衰弱、抑郁、营养、体质指数（BMI）比较（χ ² 值分别为13.321、4.064、31.944、36.695、18.230、19.681、52.509、10.253、20.456、9.070、9.483），差异均有统计学意义（均P＜0.05）。决策树模型筛选出老年患者吞咽障碍的影响因素主要有自理能力、职业、文化程度和抑郁，决策树模型受试者工作特征曲线下面积为0.862，灵敏度为79.8%，特异度为79.0%，P＜0.001。结论  基于自理能力、职业、文化程度和抑郁构建的决策树模型，能有效预测老年患者吞咽障碍风险。

       Objective  To construct a swallowing disorder warning model for elderly patients based on decision tree．Methods  Convenience  sampling was  used to  study  200  elderly  patients  admitted to the  geriatric  department  of  a tertiary comprehensive hospital in Yinchuan，Ningxia．Results  Among 200 elderly patients，the incidence of swallowing disorders was 40.5%．The two groups of patients were compared in terms of gender，age，education level，occupation，medical insurance type，annual family income，daily living ability，frailty，depression，nutrition，and BMI（χ ²  values were 13.321，4.064，31.944，36.695，18.230，19.681，52.509，10.253，20.456，9.070，9.483，respectively），and the  differences were  statistically significant（all P＜0.05）．The decision tree model identified the main influencing factors of swallowing disorders in elderly patients as self-care ability，occupation，education level，and depression．The Receiver Operating Characteristic curve of the decision tree model had an area under the curve of 0.862，sensitivity of 79.8%，and specificity of 79.0%，P＜0.001．Conclusions  A decision tree model based on self-care ability，occupation，education level，and depression can effectively predict the  risk of swallowing disorders in elderly patients．

       目的 汇总分析肝硬化患者消化道出血风险预测模型，为今后模型的建立和优化提供参考。方法   系统检索中国知网、维普、PubMed数据库在2025年4月22日前公开发表的所有肝硬化患者消化道出血风险预测模型，按纳入标准筛选文献，对最终纳入文章分析摘录并系统汇总，包括模型特征、危险因素及模型预测评估效果等信息。结果   共检索3 603篇预测模型相关研究论文，最终纳入30篇，其中中国27篇、韩国1篇、印度1篇、埃及1篇。22项研究收集了肝硬化病因，其中病毒性肝病最多（72.94%，2 922/4 006），药物性肝病及非酒精性脂肪性肝病最少（均为0.02%，1/4 006）。在研究类型上，有28篇单中心研究，2篇为多中心研究，其中有12个模型未进行验证，只有1个模型进行了外部验证，其余模型只进行了内部验证，曲线下面积（AUC）范围0.680~0.994。根据模型纳入因素特点，分为血常规指标、凝血指标、生化指标、影像学指标、复合指标、其他指标共6种，其中纳入因素最多为影像学指标，最少为凝血指标。在纳入危险因素中，第1位为门静脉直径，第2位为血小板计数，第3位为血红蛋白水平及脾脏硬度，所有因素中与脾脏相关的指标最多。结论   肝硬化患者消化道出血风险预测模型研究质量有待提升，影像学指标应用最广，脾脏相关指标重要性突出，门静脉直径、血小板计数、血红蛋白水平及脾脏硬度为最常用的危险预测因素。

       Objective  To  summarize and analyze the  prediction models for gastrointestinal  bleeding  risk in  patients with cirrhosis，providing references for the establishment and optimization of future models．Methods  A systematic search was conducted in CNKI，VIP，and PubMed for all published prediction models for gastrointestinal bleeding risk in patients with cirrhosis before April 22，2025．Articles were screened according to the inclusion criteria，and the finally included articles were analyzed and summarized，including model characteristics，risk factors，and model prediction evaluation effects．Results  A total of 3 603 related research papers on prediction models were initially retrieved，and 30 were finally included，with 27 from China，one from South Korea，one from India，and one from Egypt．Among the 22 studies that collected the etiology of cirrhosis，viral hepatitis was the most common（72.94%，2 922/4 006），while drug-induced liver disease and non-alcoholic fatty liver disease were the least common（0.02%，1/4 006）．In terms of study type，28 were single-center studies and two were multicenter studies．Among them，12 models were not validated，only one model was externally validated，and the rest were only internally validated，with an area under the curve range of 0.680-0.994．According to the characteristics of the factors included in the models，they were divided into six types of indicators：blood routine，coagulation，biochemistry，imaging，composite，and others，among which imaging indicators were the most common and coagulation indicators were the least．In the included risk factors，the first was portal vein diameter，the second was platelets count，and the third was hemoglobin level and spleen stiffness，with the most factors related to the spleen．Conclusions  The quality of studies on prediction models for gastrointestinal bleeding risk in cirrhosis patients needs to be improved．Imaging indicators are the most widely used，and spleen-related indicators are of prominent importance，with portal vein diameter，platelets count，hemoglobin level，and spleen stiffness being the most commonly used risk prediction factors．

      目的 分析儿童大环内酯类耐药重症肺炎支原体肺炎（SMPP）的危险因素,构建列线图预测模型。 方法 回顾性收集2023年1月—2024年9月在广州医科大学附属番禺中心医院儿科住院治疗的1 121例大环内酯类耐药肺炎支原体肺炎患儿入院初期的临床资料。按7∶3比例将患儿资料随机分为训练集（784例）和验证集（337例）。采用R4.4.1软件使用10重交叉验证最小绝对收缩与选择算法（LASSO）回归分析进行单因素变量筛选,采用Logistics回归分析建立预测模型, 绘制可视化列线图。使用受试者操作特征曲线（ROC）, 校准曲线、Hosmer-Lemeshow（HL）检验及临床决策曲线（DCA）分别评估模型的区分度、校准度和临床使用价值。 结果 在训练集中, LASSO回归结合Logistics回归分析结果显示,院前发热时间>5.5 d、谷丙转氨酶>14.5 U/L、乳酸脱氢酶>287.5 U/L、C反应蛋白>18.65 mg/L、肺实变、合并病毒感染是大环内酯类耐药SMPP发生的危险因素（P<0.05）, 根据上述危险因素构建列线图预测模型。训练集和验证集ROC曲线下面积分别为0.847和0.822; 校准曲线和HL检验显示模型具有良好的校准度; DCA显示预测模型在风险阈值为0.05~0.95时预测性能最优。 结论 院前发热时间、谷丙转氨酶、乳酸脱氢酶、C反应蛋白、肺实变、合并病毒感染是大环内酯类耐药SMPP发生的影响因素, 基于以上因素构建的列线图模型具有较好的预测效能, 有利于早期识别耐药重症病例, 及早采取有效干预,改善患者预后。

      Objective To explore the risk factors and to construct a nomogram prediction model for severe macrolide-resistant Mycoplasma pneumoniae pneumonia（MPP）in children．Methods The clinical data during the initial admission period of 1 121 children with macrolide-resistant MPP who were hospitalized in the Department of Pediatrics of the Affiliated Panyu Central Hospital of Guangzhou Medical University from January 2023 to September 2024 were retrospectively collected．The children data were randomly divided into a training set（n=784）and a validation set（n=337）at a ratio of 7∶3．With R language software（version 4.4.1）, least absolute shrinkage and selection operator（LASSO）regression analysis with tenfold cross-validation was used to screen risk factors, Logistics regression analysis was used to establish prediction model, and a visualization of the risk variables was created using a nomogram．The receiver operating characteristic（ROC）curves, calibration curves, Hosmer-Lemeshow（HL）test and clinical decision curve analysis（DCA）were used to evaluate the discrimination, calibration and clinical application value of the model．Results In the training set, LASSO regression analysis combined with Logistics regression analysis showed that prehospital fever duration > 5.5 days, alanine aminotransferase level> 14.5 U/L, lactate dehydrogenase level> 287.5 U/L, C-reactive protein > 18.65 mg/L, lung consolidation, and co-infection with virus were risk factors for severe macrolide-resistant MPP（P<0.05）．A nomogram prediction model was constructed based on the above risk factors．The area under the ROC curves of the training set and the validation set were 0.847 and 0.822, respectively．The calibration curves and HL test showed that the model had good calibration． The DCA curves showed that the prediction model had the best prediction performance when the risk threshold was between 0.05-0.95．Conclusions Prehospital fever duration, alanine aminotransferase level, lactate dehydrogenase level, C-reactive protein level, lung consolidation and co-infection with virus were risk factors for prediction of severe macrolide-resistant MPP．The nomogram model based on the above factors had a good prediction efficiency, which was conducive to early identification of severe cases with macrolide-resistant, and taking early effective interventions to improve the prognosis．

       文章围绕康复治疗学专业创新创业人才培养模式展开研究，讨论了国内外高校创新创业人才培养现状。从教育理念、课程体系、师资力量、资源配套等方面探讨了人才培养所面临的问题。结合广东药科大学康复治疗学专业培养现状，从管理、教学、平台、服务四大体系明确了具体要求，有望为其他高校康复治疗学专业培养模式改革提供思路。

  The article focuses on innovative and entrepreneurial talent-cultivation models in the Rehabilitation Therapy specialty，discussing the current status of such cultivation in domestic and international universities．It explores challenges in talent development from perspectives including educational philosophy，curriculum system，faculty resources，and resource allocation．Based on the current training status of Guangdong Pharmaceutical University’s Rehabilitation Therapy Program，the study specifies detailed requirements through four major systems：management，teaching，platform，and service．This  research is expected to provide valuable insights for the  reform of talent cultivation models in  rehabilitation therapy programs at other higher education 

institutions．

       目的   评估ChatGPT 4与Llama 3微调模型在乳腺癌诊断中的应用效果，特别是在超声、钼靶及超声联合钼靶的非结构化报告和影像诊断方面。方法   回顾性收集了689例同时接受乳腺超声和钼靶检查的患者数据，比较两种模型在文本和图像模态下的诊断性能，并探讨乳腺密度对模型表现的影响。结果   在文本模态下，微调Llama 3表现优异，联合诊断准确率达91.7%，优于ChatGPT 4的71.7%。图像模态中两模型准确率均低于70%，但ChatGPT 4灵敏度较高（78.3%），Llama 3特异度突出（98.3%）。分组分析表明，在非致密型乳腺中钼靶表现更佳，而致密型乳腺中超声诊断更具优势。结论   大语言模型在医学图像处理和多模态整合方面仍需进一步优化，医学领域微调的大语言模型在处理非结构化临床文本方面具有潜力。

       Objective  To evaluate the application effectiveness of ChatGPT 4 and the fine-tuned Llama 3 model in breast cancer diagnosis，particularly in processing unstructured reports and diagnostic imaging of ultrasound，mammography，and their combined modalities．Methods  Retrospective data from 689 patients who underwent both breast ultrasound and mammography examinations were collected．The diagnostic performance of the two models was compared across text and image modalities，and the impact of breast density on model performance was explored．Results  In the text modality，the fine-tuned Llama 3 model performed excellently，achieving a combined diagnostic accuracy of 91.7%，outperforming 71.7% of ChatGPT 4．In the image modality，both models had accuracies below 70%，but ChatGPT 4 exhibited higher sensitivity（78.3%），while Llama 3 demonstrated outstanding specificity（98.3%）．Subgroup analysis indicated that mammography performed better in non-dense breasts，whereas ultrasound was more advantageous in dense breasts．Conclusions  The large language models  still  require further optimization in medical image processing and multimodal integration，but fine-tuned large language models in the medical field show potential in handling unstructured clinical texts．

       目的   通过机器学习方法构建脓毒症谵妄患者30 d死亡的预测模型，并识别关键预测因子。方法   采用基于医疗信息集成重症监护数据库（Medical Information Mart for Intensive Care IV）的回顾性队列研究方法，boruta筛选重要特征，并通过决策树，K近邻，LightGBM，随机森林，支持向量机，XGBoost构建模型进行分析，通过ROC曲线下面积进行评估，利用F1分数、召回率、精确率、特异度、灵敏度和阳性预测值比较模型表现。结果  XGBoost模型在训练集和验证集中的ROC曲线下面积分别为0.906和0.762，表明该模型具有良好的预测能力，入院年龄、红细胞分布宽度和白细胞计数是最重要的预测因子。结论   基于机器学习的脓毒症谵妄患者预后预测模型展现出良好的预测效能，为临床早期干预提供了重要参考依据。

       Objective  To construct a  30-day mortality  prediction model for  patients with  sepsis-associated  delirium using machine learning methods and identify key predictive factors．Methods  A  retrospective cohort study was conducted based on the Medical Information Mart for Intensive Care IV database．Important features were selected using the Boruta algorithm，and models including Decision Tree，K-Nearest Neighbors，LightGBM，Random Forest，Support Vector Machine，and XGBoost were constructed and analyzed．Model performance was evaluated using the area under the reciver operater characteristic（ROC）curve（AUC），along with F1 score，recall，precision，specificity，sensitivity，and positive predictive value．Results  The XGBoost model demonstrated strong predictive performance，with AUC values of 0.906 in the training set and 0.762 in the test set．Key predictors identified included admission age，red blood cell distribution width，and white blood cell count．Conclusions  The machine learning-based prediction model for sepsis-associated delirium prognosis exhibits robust predictive efficacy，providing a valuable tool for early clinical intervention．

目的 基于Donabedian环节模型构建急诊脑出血患者护理质量评价体系, 并应用于临床,为急诊脑出血患者护理质量管理、监测与评价提供客观、科学的参考依据。方法 通过文献查阅、筛查与评价, 提取可行性资料, 基于Donabedian环节模型构建急诊脑出血患者护理质量评价体系的框架, 并采用德尔菲法完成两轮专家函询,确定最终的指标体系。选择2021年1月—2024年1月本院收治的230例急诊脑出血患者为研究对象, 将2021年1月—2022年6月作为干预前监测节点,该阶段的165例患者为传统组, 实施常规的护理质量管理;将2022年7月—2024年1月作为干预后监测节点,该阶段的165例患者为观察组, 实施以急诊脑出血患者护理质量评价指标进行护理质量监测管理。结果 两轮函询中专家积极系数分别为95%和100%, 意见提出率分别为56.25%和35.54%; 两轮函询专家权威系数为0.945、0.893; 第1轮函询中各项指标变异系数（CV）均值为0~0.136, Kendall’s W协调系数为0.065; 第2轮函询中变异系数（CV）均值为0~0.110, Kendall’s W协调系数为0.186。最终形成的急诊脑出血患者护理质量评价体系共涵盖一级指标3个、二级指标11个、三级指标55个。观察组入院-用药时间合格率、吞咽障碍患者动态评估率、气道管理合格率、早期被动/主动活动落实率高于传统组,差异具有统计学意义（χ²=14.850、12.261、8.183、37.420, P<0.05）, 观察组患者满意度明显高于传统组（χ²=14.049, P<0.001）。结论 本研究构建的急诊脑出血患者护理质量评价体系具有一定的科学性、可靠性和实用性, 可作为临床实现护理质量持续改进的重要评价工具。

Objective Based on the Donabedian model,the nursing quality evaluation system of emergency cerebral hemorrhage patients was constructed, and applied to clinical practice, providing an objective and scientific reference basis for realizing the nursing quality management, monitoring and evaluation of emergency cerebral hemorrhage patients．Methods Through literature review, screening and evaluation, the feasibility data was extracted, and the framework of the nursing quality evaluation system for patients with emergency cerebral hemorrhage was constructed based on the Donabedian model, and the Delphi method was adopted to complete two rounds of expert letter inquiry to determine the final index system．The study selected 230 patients with acute cerebral hemorrhage admitted to our hospital from January 2021 to January 2024 as the research subjects．The period from January 2021 to June 2022 was used as the pre-intervention monitoring period, during which 165 patients were in the traditional group, receiving routine nursing quality management．The period from July 2022 to January 2024 was used as the post-intervention monitoring period, during which 165 patients were in the observation group,implementing nursing quality monitoring and management based on evaluation indicators for the care of patients with acute cerebral hemorrhage．Results In the two rounds of letter inquiry, the positive coefficient of experts was 95% and 100%, respectively, and the rate of suggestions was 56.25% and 35.54%, respectively; the authority coefficient of experts in the two rounds of letter inquiry was 0.945 and 0.893．In the first round the mean value of coefficient of variation（CV）of each index was 0~0.136, and the coordination coefficient of Kendall’s W was 0.065; in the second round the mean value of variation coefficient（CV）was 0-0.110, and the coordination coefficient of Kendall's W was 0.186．The final nursing quality evaluation system for emergency cerebral hemorrhage patients covers 11 first-level indicators, 11 second-level indicators and 55 third-level indicators．The results showed that the pass rate of admission-medication time, dynamic assessment rate of dysphagia patients, airway management rate, and early passive / active activity implementation rate of the observation group were statistically significant different from those in the traditional group（χ²=14.850,12.261, 8.183, 37.420, P<0.05）, and the patient satisfaction in the observation group was significantly higher than that in the traditional group（χ²=14.049, P<0.001）．Conclusions The nursing quality evaluation system for emergency cerebral hemorrhage patients constructed in this study is scientific,reliable and practical, and can be used as an important evaluation tool to achieve continuous improvement of nursing quality in clinical practice．