Xiuying Hu

Background: Public health nurses play a pivotal role in primary public health services, yet China lacks a systematic, context-specific competency framework to support workforce development. This study aimed to develop a competency framework for public health nurses in China to guide capacity-building initiatives. Methods: A preliminary competency framework was constructed based on a literature review, job analysis, and semi-structured interviews with 22 frontline practitioners and comprised six domains, 16 subdomains, and 52 competencies. A two-round Delphi survey was then conducted with 21 experts from multiple regions with expertise in community nursing, public health, primary care medicine, and nursing education and related specialties. Consensus was defined as a mean importance rating ≥4.0 (5-point Likert scale) and a coefficient of variation ≤0.25. The Delphi consultation process was characterized by expert engagement, the expert authority coefficient, and the coordination of expert opinions. Finally, the Analytic Hierarchy Process was applied to determine the relative weights of the agreed-upon items at each level. Results: Across the two Delphi rounds, the response rates were 95.45% and 100%, the expert authority coefficient was 0.90, and Kendall’s coefficients of concordance were 0.156 and 0.171, respectively (both P < 0.001). The final competency framework comprised six domains, 16 subdomains, and 46 competencies. All Analytic Hierarchy Process judgment matrices showed acceptable consistency (CR < 0.10). Health education and health promotion was assigned the highest weight (0.280) among the domains. Conclusion: The resulting competency framework demonstrates methodological rigor, a comprehensive structure, and contextual relevance. It provides theoretical and practical implications for competency-based education, workforce development, and the professionalization of public health nursing in China.

Background: Cognitive impairment and depressive symptoms are common among nursing home residents. The indirect association between sunlight exposure and cognitive function through depressive symptoms in this setting remains unclear. Methods: This cross-sectional study included 244 older adults from six nursing homes in Chengdu, China. Sunlight exposure, depressive symptoms, and cognitive function were assessed using the Chinese version of the Sunlight Exposure Questionnaire, the 15-item Geriatric Depression Scale (GDS-15), and the Montreal Cognitive Assessment–Basic, respectively. GDS-15 total scores were used for correlations and regressions; three item parcels represented depressive symptoms in the structural model. Correlation analysis, multivariable linear regression, and structural equation modeling with 5000 bootstrap resamples were performed. Results: The mean age was 82.42 years, and 132 participants (54.1%) were women. Mean sunlight exposure, depressive symptom, and cognitive function scores were 1.93 (SD 0.43), 4.07 (SD 3.22), and 20.27 (SD 2.02), respectively. Sunlight exposure correlated negatively with depressive symptoms (r = − 0.297) and positively with cognitive function (r = 0.221); depressive symptoms correlated negatively with cognitive function (r = − 0.195; all P < 0.01). In the structural equation model, sunlight exposure was associated with fewer depressive symptoms (standardized β = − 0.323), and depressive symptoms were associated with lower cognitive function (β = − 0.155). The standardized indirect association through depressive symptoms was 0.050 (bias-corrected 95% bootstrap CI: 0.004– 0.114; bootstrap P = 0.042), representing 24.0% of the total modeled association. The direct association was 0.158 (bias-corrected 95% bootstrap CI: 0.002– 0.308). Model fit was acceptable (χ²/df = 2.091, CFI = 0.957, RMSEA = 0.067). Conclusion: Greater sunlight exposure was modestly associated with fewer depressive symptoms and better cognitive function. Depressive symptoms represented a partial statistical indirect pathway; the cross-sectional design cannot establish temporal order or causality. Longitudinal studies are needed to clarify these relationships.

1. Population aging represents one of the most significant global demographic shifts of the 21st century. Driven by increasing life expectancy and declining fertility rates, this trend is particula...

Purpose: Nightshift nurses commonly suffer from sleep disturbances and occupational stress due to irregular work-sleep rhythms. This Bayesian network meta-analysis (NMA) compared the efficacy of non-pharmacological therapies to identify optimal interventions for improving sleep and reducing occupational stress in night/rotating-shift nurses. Methods: Cochrane, Embase, PubMed, Web of Science, and CNKI were searched up to October 24, 2025. Studies were assessed for bias via ROB2. Primary outcomes were total sleep score and sleep duration; secondary outcomes included anxiety, depression, and fatigue scores. Bayesian NMA (R 4.5.2) calculated SMD (95% CrI) and SUCRA for intervention ranking. Results: Eighteen studies involving 1,572 nurses were included. Sleep intervention program (SIP) appeared to be effective for optimizing total sleep score (SMD −0.80, 95% CrI [−1.2, −0.41]; SUCRA = 80.71%). Mindfulness training intervention (MTI) showed relatively good effectiveness in regulating sleep duration (SMD −1.0, 95% CrI [−1.4, −0.57]; SUCRA = 95.97%). Auricular plaster therapy (APT) appeared to be effective in alleviating depression (SMD −0.60, 95% CrI [−1.0, −0.17]; SUCRA = 84.60%). Rational emotive behavior therapy (REBT) showed superior efficacy for alleviating anxiety (SMD −3.0, 95% CrI [−3.6, −2.4]; SUCRA = 100%). Pure pelargonium essential oil (PPEO) demonstrated relatively good efficacy in relieving fatigue (SMD −0.70, 95% CrI [−1.2, −0.25]; SUCRA = 94.92%). Conclusion: SIP, MTI, APT, REBT, and PPEO appeared to be effective non-pharmacological interventions for optimizing nightshift nurses' total sleep score, regulating their sleep duration, and alleviating their depression, anxiety, and fatigue, respectively. Future well-designed studies are warranted to further validate the role of non-pharmacological therapies, particularly SIP and MTI, in regulating sleep among nightshift nurses. However, all findings were based on indirect evidence, which formed a star-shaped network with no closed loops. Therefore, the findings should be considered exploratory and hypothesis-generating, which are intended to provide preliminary guidance for future head-to-head comparative trials rather than establishing definitive clinical evidence. Clinical decisions should be made based on a comprehensive assessment that takes into account local resources, cultural context, and individual preferences of nurses. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251180760

Sleep disorders and anxiety-depression symptoms can significantly impair the quality of life and treatment adherence among breast cancer patients undergoing chemotherapy, while also increasing the risk of disease recurrence and mortality. Early identification of the progression trajectory and underlying causes of these symptoms is essential for providing personalized interventions for deteriorating cases. This study aims to analyze the trajectories of sleep disorders and anxiety-depression symptoms in breast cancer patients during chemotherapy using a group-based Multi-Trajectory Model (GBMTM) and to identify influencing factors through machine learning. Prospective data from 150 breast cancer patients undergoing four cycles of chemotherapy were collected. A GBMTM was employed to identify distinct trajectories, with 70 predictive variables included in the analysis. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the optimal subset of variables. Seven machine learning models were compared, and the optimal model was chosen for result interpretation using SHAP (SHapley Additive exPlanations) analysis. Among breast cancer patients undergoing chemotherapy, two distinct trajectories of sleep disorders and anxiety-depression symptoms were identified: the "stable group" (74.7%) and the "deteriorating group" (25.3%). SHAP analysis revealed that high levels of C-reactive protein, high uric acid, high hemoglobin, smoking, presence of invasive cancer with ductal carcinoma in situ, prior chemotherapy, being married, regular menstrual cycles in the past, and lack of family history were significant risk factors associated with the "deteriorating group." Our findings indicate two distinct trajectories of sleep disorders and anxiety-depression symptoms in breast cancer patients undergoing chemotherapy, with the majority falling into the "stable group". The identified risk factors associated with the "deteriorating group" should be closely monitored to facilitate early intervention and improve patient outcomes. The trial was registered on Chinese Clinical Trial Registry (Trial ID: ChiCTR2300077667 on 15 November 2023; available at https://www.chictr.org.cn/).

Recurrent in-stent restenosis (R-ISR) after percutaneous coronary intervention remains a clinically important failure mode, yet reported rates and predictors vary widely. We aimed to quantify the pooled incidence of R-ISR and synthesize multivariable risk factors. A comprehensive literature search of the electronic database was updated on May 3, 2025. Studies reporting the incidence or analyzing the risk factors of R-ISR were eligible. Study quality was assessed using the Newcastle-Ottawa Scale. Data synthesis was achieved via random-effects meta-analysis using the R studio (version 4.4.3). Subgroup analyses considered region, treatment, and follow-up, with leave-one-out sensitivity analyses. The systematic review is reported in accordance with the PRISMA 2020 checklist. Twenty-three studies were included. The overall pooled incidence of R-ISR was 21.7% (95%CI 17.1%-27.2%), with variations by region (highest in Europe), treatment type (lowest with DES), and follow-up duration (highest within 6 months). Significant risk factors included renal disease (OR 3.90; 95%CI 1.87-8.11), non-focal restenosis (OR 3.29; 95%CI 2.41-4.49), lesion length (OR 1.15; 95%CI 1.06-1.24), heterogeneous tissue pattern (OR 3.36; 95%CI 1.69-6.67), stent fracture (OR 2.21; 95%CI 1.29-3.79), and higher percentage diameter stenosis (OR 1.05; 95%CI 1.03-1.06). Age showed non-significant association (OR 0.96; 95%CI 0.93-1.00). To our knowledge, this is among the first systematic reviews and meta-analyses specifically focused on recurrent ISR after treatment of ISR lesions. Renal disease, non-focal morphology, lesion length, heterogeneous tissue pattern, stent fracture and greater post-procedural percentage diameter stenosis were consistently associated with higher risk of R-ISR. These findings support individualized treatment selection with intensified secondary prevention and closer surveillance during the first six months after the recent percutaneous coronary intervention performed for ISR. CRD420251043520.

Nursing students experience high prevalence of depression, anxiety, and stress, but traditional variable‑centered approaches may overlook distinct su

Objectives: Early and accurate identification of frailty is essential for preventing adverse outcomes in older adults. However, existing frailty prediction models often lack reliability, interpretability, and generalizability. Methods: Participants aged 60 years and older between 2011 and 2015 (n = 3419) from the CHARLS were used to develop models, and participants from the CLHLS-HF between 2014 and 2018 (n = 1017) were used for external validation. The frailty was assessed 4 years after baseline in both cohorts by Fried’s Frailty Phenotype (FFP). Six machine learning models were applied to develop prediction models. The SHapley Additive exPlanations (SHAP) method was utilized to explain the final model. Clinical outcomes were evaluated between participants predicted as frail and non-frail by the final model. Results: The XGBoost (AUC = 0.934, 95% CI: 0.921–0.948; F1 = 0.712, 95% CI: 0.686–0.736 in internal validation; AUC = 0.792, 95% CI: 0.750–0.830; F1 = 0.702, 95% CI: 0.652–0.753 in external validation) performed best among six models. Key predictors included lifestyle factors (e.g., instrumental daily living activities, BMI, and self-rated health) and psychological traits (e.g., depression). Participants predicted as frail had significantly elevated risks of falls (OR = 2.11), hospitalization (OR = 1.75), and disability (OR = 1.42). Conclusions: The proposed model provided a robust and interpretable digital tool for predicting frailty among older adults and associated adverse outcomes.