Objective To explore the impact of dyadic coping on fear of progression in patients with hematological tumors and their spouses based on the Actor-Partner Interdependence Model(APIM),and to provide references for clinical intervention.Methods By the convenient sampling method,136 pairs of hematological tumor patients and their spouses were selected from October 2023 to January 2024 in 5 tertiary hospitals in Shandong Province.A general information questionnaire,Dyadic Coping Inventory,Fear of Progression Questionnaire Short Form,and Fear of Progression Questionnaire Short Form/Partner Version were used to conduct the investigation.Amos 24.0 software was used to establish the APIM for dyadic coping with fear of progression.Results A total of 280 survey questionnaires were distributed,and 272 valid questionnaires were collected,including 136 from patients and their spouses,with an effective rate of 97.14％.The dyadic coping scores of hematological tumors patients and their spouses were(129.50±20.58)and(132.71±15.70),respectively,while the fear of progression scores were(31.71±3.13)and(29.01±3.05),respectively.Regarding the actors'effects,the level of dyadic coping strategies of patients and their spouses can both predict their own fear of progression,and are negatively correlated(β1=-0.52,β2=-0.41;P＜0.001).Regarding the partners'effects,the degree of fear of progression in patients is negatively correlated with the dyadic coping level of their spouses(β=-0.19,P＜0.001).Conclusion There is an interactive effect between the fear of progression and the level of dyadic coping between hematological tumor patients and their spouses.This suggests that clinical nursing staff should pay attention to the evaluation of fear of progression in patients with hematological tumors and their spouses,and effectively reduce the level of fear of progression on both sides.

Objective To systematically evaluate the risk prediction models for neonatal early-onset sepsis(EOS),aiming to provide reference for the construction and optimization of models,as well as for clinical selection of appro-priate prediction models.Methods PubMed,Web of Science,Embase,Cochrane Library,China National Know-ledge Infrastructure(CNKI),Wanfang Data,China Biology Medicine disc(CBM),and VIP databases were re-trieved,and studies relevant to neonatal EOS risk prediction models were collected.The retrieval period was from the inception of the database to January 18,2025.Two researchers independently screened literatures,extracted da-ta,and evaluated the quality of the included literatures using PROBAST tool.Any disagreements were resolved through consultation with a third reviewer.Results A total of 14 literatures were included in analysis,containing 19 risk prediction models.The area under receiver operating characteristic(ROC)curve(AUC)of the included model ranged 0.71-0.999.The number of prediction factors ranged 3-21.Common prediction factors included young gestational age,low birth weight,1-minute Apgar score,abnormal neonatal temperature,prolonged prema-ture rupture of membranes,amniotic fluid turbidity,maternal Group B streptococcal infection,maternal chorioam-nionitis,as well as elevated levels of procalcitonin and C-reactive protein in neonates.The risk of model overall bias was high,mainly due to insufficient number of outcome variable events in the analysis field,improper processing of missing data,screening of prediction factors based on univariate analysis,lacking model performance evaluation,and overfitting of model.Conclusion The neonatal EOS risk prediction model is still at the development stage.Al-though the current prediction models have better overall predictive performance,the overall quality needs to be im-proved.Future modeling can follow the PROBAST and TRIPOD specifications to reduce bias risk,explore the com-bination of multiple modeling methods,and focus on strengthening external validation and localized application to enhance the clinical applicability and promotion value of the model.

Objective:To systematically review the prediction models for extubation failure in critically ill patients undergoing mechanical ventilation, providing a reference for healthcare professionals in selecting appropriate models to identify high-risk populations.Methods:Literature on the construction of prediction models for extubation failure risk in critically ill patients undergoing mechanical ventilation was retrieved from China National Knowledge Infrastructure, Wanfang Database, VIP, SinoMed, PubMed, Web of Science, Embase, and Cochrane Library. The search was limited from database inception to February 2024. Two researchers independently screened the literature and extracted data, using bias risk assessment tools to evaluate the bias risk and applicability of the prediction models.Results:A total of nine studies were included, with the most common predictive factors being mechanical ventilation duration, Glasgow Coma Scale score, cough reflex strength, age, and 24-hour input/output volume. The area under the receiver operating characteristic curve for the models ranged from 0.689 to 0.926, indicating good predictive performance. However, the risk of bias was high, mainly due to small sample sizes, the selection of predictive factors based on univariate analysis, and lack of proper internal validation.Conclusions:Existing prediction models show good predictive performance, but they carry high bias risk. Future studies should improve research design, adhere to model development and reporting guidelines, and develop well-performing, user-friendly prediction models to more accurately identify high-risk populations for extubation failure.

Objective To explore the impact of dyadic coping on fear of progression in patients with hematological tumors and their spouses based on the Actor-Partner Interdependence Model(APIM),and to provide references for clinical intervention.Methods By the convenient sampling method,136 pairs of hematological tumor patients and their spouses were selected from October 2023 to January 2024 in 5 tertiary hospitals in Shandong Province.A general information questionnaire,Dyadic Coping Inventory,Fear of Progression Questionnaire Short Form,and Fear of Progression Questionnaire Short Form/Partner Version were used to conduct the investigation.Amos 24.0 software was used to establish the APIM for dyadic coping with fear of progression.Results A total of 280 survey questionnaires were distributed,and 272 valid questionnaires were collected,including 136 from patients and their spouses,with an effective rate of 97.14％.The dyadic coping scores of hematological tumors patients and their spouses were(129.50±20.58)and(132.71±15.70),respectively,while the fear of progression scores were(31.71±3.13)and(29.01±3.05),respectively.Regarding the actors'effects,the level of dyadic coping strategies of patients and their spouses can both predict their own fear of progression,and are negatively correlated(β1=-0.52,β2=-0.41;P＜0.001).Regarding the partners'effects,the degree of fear of progression in patients is negatively correlated with the dyadic coping level of their spouses(β=-0.19,P＜0.001).Conclusion There is an interactive effect between the fear of progression and the level of dyadic coping between hematological tumor patients and their spouses.This suggests that clinical nursing staff should pay attention to the evaluation of fear of progression in patients with hematological tumors and their spouses,and effectively reduce the level of fear of progression on both sides.

Objective To systematically evaluate the risk prediction models for neonatal early-onset sepsis(EOS),aiming to provide reference for the construction and optimization of models,as well as for clinical selection of appro-priate prediction models.Methods PubMed,Web of Science,Embase,Cochrane Library,China National Know-ledge Infrastructure(CNKI),Wanfang Data,China Biology Medicine disc(CBM),and VIP databases were re-trieved,and studies relevant to neonatal EOS risk prediction models were collected.The retrieval period was from the inception of the database to January 18,2025.Two researchers independently screened literatures,extracted da-ta,and evaluated the quality of the included literatures using PROBAST tool.Any disagreements were resolved through consultation with a third reviewer.Results A total of 14 literatures were included in analysis,containing 19 risk prediction models.The area under receiver operating characteristic(ROC)curve(AUC)of the included model ranged 0.71-0.999.The number of prediction factors ranged 3-21.Common prediction factors included young gestational age,low birth weight,1-minute Apgar score,abnormal neonatal temperature,prolonged prema-ture rupture of membranes,amniotic fluid turbidity,maternal Group B streptococcal infection,maternal chorioam-nionitis,as well as elevated levels of procalcitonin and C-reactive protein in neonates.The risk of model overall bias was high,mainly due to insufficient number of outcome variable events in the analysis field,improper processing of missing data,screening of prediction factors based on univariate analysis,lacking model performance evaluation,and overfitting of model.Conclusion The neonatal EOS risk prediction model is still at the development stage.Al-though the current prediction models have better overall predictive performance,the overall quality needs to be im-proved.Future modeling can follow the PROBAST and TRIPOD specifications to reduce bias risk,explore the com-bination of multiple modeling methods,and focus on strengthening external validation and localized application to enhance the clinical applicability and promotion value of the model.

Objective:To systematically review the prediction models for extubation failure in critically ill patients undergoing mechanical ventilation, providing a reference for healthcare professionals in selecting appropriate models to identify high-risk populations.Methods:Literature on the construction of prediction models for extubation failure risk in critically ill patients undergoing mechanical ventilation was retrieved from China National Knowledge Infrastructure, Wanfang Database, VIP, SinoMed, PubMed, Web of Science, Embase, and Cochrane Library. The search was limited from database inception to February 2024. Two researchers independently screened the literature and extracted data, using bias risk assessment tools to evaluate the bias risk and applicability of the prediction models.Results:A total of nine studies were included, with the most common predictive factors being mechanical ventilation duration, Glasgow Coma Scale score, cough reflex strength, age, and 24-hour input/output volume. The area under the receiver operating characteristic curve for the models ranged from 0.689 to 0.926, indicating good predictive performance. However, the risk of bias was high, mainly due to small sample sizes, the selection of predictive factors based on univariate analysis, and lack of proper internal validation.Conclusions:Existing prediction models show good predictive performance, but they carry high bias risk. Future studies should improve research design, adhere to model development and reporting guidelines, and develop well-performing, user-friendly prediction models to more accurately identify high-risk populations for extubation failure.

Objective:To systematically synthesize qualitative research on the role expectations of nursing master student's supervisors to provide a reference for improving supervisor team development and optimizing selection and evaluation systems.Methods:A systematic search was conducted in databases such as Web of Science, PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure, Wanfang, VIP, and SinoMed for qualitative studies on the role expectations of nursing master student's supervisors. The search was limited to studies published up to October 31, 2023. The quality of the included studies was evaluated using the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. Meta-integration was employed to synthesize the results.Results:A total of nine studies were included, yielding 39 research findings, which were further categorized into eight new categories. These were integrated into three main themes: expectations of supervisors' comprehensive qualities, clear role positioning and exemplification, and respecting and meeting students' developmental needs.Conclusions:Educational institutions and policymakers involved in graduate education should fully understand the multi-dimensional role expectations of stakeholders regarding nursing master student's supervisors. This understanding is crucial for strengthening the development of the supervisor team, improving selection and evaluation systems, and supporting the training of advanced nursing professionals.

Pregnant women are affected by various biological,psychological and social pressures,and the incidence of perinatal vulnerability is relatively high.The existence of perinatal vulnerability seriously affects the physical and mental health of pregnant women and infants.Attention to perinatal vulnerability can help reduce the risk of adverse matemal and infant outcomes.This paper reviews the concept,classification,assessment tools,influencing factors,intervention measures,limitations and prospects of perinatal vulnerability,providing references for formulating management programs of perinatal vulnerability.

This article reviewed the present situation of the research on the relationship between the number of nursing staff, education level, skill combination and patient safety at home and abroad, as well as the indirect mechanism of nursing manpower factors on patient safety through intermediary factors such as working environment, attendance, nursing lack and so on. In view of the problems existing in domestic research, some suggestions were put forward, such as carrying out longitudinal and intervention research on patient safety, optimizing the allocation of nursing human resources and patient safety indicators, exploring the mechanism of multiple nursing factors and patient safety and conducting empirical analysis. To provide reference for hospital managers to improve nursing quality and ensure patient safety.

Objective:To retrieve and evaluate the evidence on breastfeeding associated nipple pain and trauma management both domestically and internationally, so as to provide reference for clinical practice.Methods:According to the evidence pyramid "6S", all evidence on breastfeeding associated nipple pain and trauma management, including guidelines, evidence summary, best clinical practice manual, systematic review, expert consensus, and randomized controlled trial was retrieved by computer on China National Knowledge Infrastructure (CNKI), WanFang Data, VIP, China Biomedical Literature Database, PubMed, Embase, Web of Science, Cochrane Library, Joanna Briggs Institute (JBI) in Australia, National Institute for Health and Clinical Excellence (NICE), National Guideline Clearinghouse (NGC), Scottish Intercollegiate Guidelines Network (SIGN), Registered Nurses' Association of Ontario (RNAO), medlive and other websites or databases. The retrieval period was from January 1, 2012 to June 1, 2022.Results:A total of 22 articles were included, including 5 guidelines, 3 evidence summaries, 6 systematic reviews, 1 expert consensus and 7 RCTs. 26 pieces of evidence were summarized from 6 aspects: pain assessment, education and training, non-pharmacological interventions, pharmacological interventions, self-management and precautions.Conclusions:This study summarizes the best evidence for managing breastfeeding associated nipple pain and trauma, providing evidence-based evidence for regulating breastfeeding associated nipple pain and trauma management. It is recommended that nurses comprehensively consider the clinical situation when applying evidence, selectively apply the best evidence, extend breastfeeding time, and promote maternal and infant health.