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.

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.

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.

Background::Given that seizures may be triggered by vaccination, this study aimed to evaluate the risk and correlative factors of seizures in patients with epilepsy (PWE) after being vaccinated against coronavirus disease 2019 (COVID-19).Methods::This study retrospectively enrolled PWE who were vaccinated against COVID-19 in the epilepsy centers of 11 hospitals in China. We divided the PWE into two groups as follows: (1) patients who developed seizures within 14 days of vaccination were assigned to the SAV (with seizures after vaccination) group; (2) patients who were seizure-free within 14 days of vaccination were assigned to the SFAV (seizure-free after vaccination) group. To identify potential risk factors for seizure reccurence, the binary logistic regression analysis was performed. Besides, 67 PWE who had not been vaccinated were also included for elucidating the effects of vaccination on seizures recurrence, and binary logistic regression analysis was performed to determine whether vaccination would affect the recurrence rate of PWE who had drug reduction or withdrawal.Results::The study included a total of 407 patients; of which, 48 (11.8%) developed seizures within 14 days after vaccination (SAV group), whereas 359 (88.2%) remained seizure-free (SFAV group). The binary logistic regression analysis revealed that duration of seizure freedom ( P < 0.001) and withdrawal from anti-seizure medications (ASMs) or reduction in their dosage during the peri-vaccination period were significantly associated with the recurrence of seizures (odds ratio= 7.384, 95% confidence interval = 1.732–31.488, P = 0.007). In addition, 32 of 33 patients (97.0%) who were seizure-free for more than three months before vaccination and had a normal electroencephalogram before vaccination did not have any seizures within 14 days of vaccination. A total of 92 (22.6%) patients experienced non-epileptic adverse reactions after vaccination. Binary logistic regression analysis results showed that vaccine did not significantly affect the recurrence rate of PWE who had the behavior of ASMs dose reduction or withdrawal ( P = 0.143). Conclusions::PWE need protection from the COVID-19 vaccine. PWE who are seizure-free for >3 months before vaccination should be vaccinated. Whether the remaining PWE should be vaccinated depends on the local prevalence of COVID-19. Finally, PWE should avoid discontinuing ASMs or reducing their dosage during the peri-vaccination period.

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.