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This study aims to explore the trajectories and co-occurrence of perceived control and caregiver self-efficacy among patients with heart failure (HF) and their caregivers within 3 months post-discharge and identify associated risk factors.
A prospective cohort design.
A prospective cohort study was conducted from March to June 2024 in Tianjin, China. Information on perceived control and caregiver self-efficacy was collected 24 h before discharge, 2 weeks, 1 month, and 3 months after discharge. Group-Based Dual Trajectory Modelling (GBDTM) and logistic regression were used for analysis.
The study included 203 dyads of patients with HF and their caregivers (HF dyads). Perceived control identified three trajectories: low curve (15.3%), middle curve (57.1%) and high curve (27.6%). Caregiver self-efficacy demonstrated three trajectories: low curve (17.2%), middle curve (56.7%) and high stable (26.1%). GBDTM revealed nine co-occurrence patterns, with the highest proportion (36.7%) being ‘middle-curve group for perceived control and middle-curve group for caregiver self-efficacy’, and 16.7% being ‘high-curve group for perceived control and high-stable group for caregiver self-efficacy’. Age, gender, household income, NYHA class, symptom burden and psychological resilience were identified as risk factors for perceived control trajectories; marital status, regular exercise and psychological resilience were identified as risk factors for caregiver self-efficacy trajectories.
We identified distinct trajectories, co-occurrence patterns and risk factors of perceived control and caregiver self-efficacy among HF dyads. These findings help clinical nurses to better design and implement interventions, strengthening the comprehensive management and care outcomes for HF dyads.
These findings highlighted the interactive relationship between perceived control and caregiver self-efficacy trajectories, suggesting that interventions should boost both to improve personalised treatment plans and outcomes for HF dyads.
This study adhered to the STROBE checklist.
Patients and their caregivers contributed by participating in the study and completing the questionnaire.
To develop a comprehensive and psychometrically validated scale for evaluating the core competencies of community nurses for public health emergencies.
A study of instrument development and validation was conducted.
A total of 1057 community nurses provided valid responses for this study conducted in Shanghai, China. Building upon previous study findings of the adapted core competency model and integrating the World Health Organisation's Framework for Action, this study was conducted in two phases. First, scale items were developed through systematic review, qualitative research, stakeholder meeting, and Delphi survey, refined with cognitive interviews to establish version 1.0 of the scale. Second, item analysis was performed with item-total correlations, Cronbach's alpha, and exploratory factor analysis, resulting in version 2.0. The final scale was produced after assessing the validity (content validity, confirmatory factor analysis, known-groups validity) and reliability (internal consistency, test–retest reliability).
The final scale consisted of 47 items categorised into four competency factors: prevention, preparation, response, and recovery competency. Factor analysis results indicated adequate factor loadings, excellent model fit, and well-established construct validity. The overall scale and its sub-factors exhibited high internal consistency and good test–retest reliability.
The study presents a theoretically grounded and scientifically validated scale measuring the competencies that community nurses need for public health emergency response.
This study enhances the theoretical framework of community nurses' core competencies in public health emergencies, provides a validated assessment tool, and clarifies their role in enhancing preparedness and effectiveness.
The study addressed the need for a standardised tool for assessing community nurse core competency for public health emergencies and will impact policy initiatives to enhance early prevention, emergency response, and integrated recovery practices in crisis management.
Strengthening the Reporting of Observational studies in Epidemiology checklist.
No Patient or Public Contribution.
The study examines the associations between nursing competence, work environment, and health system resilience. It also analyzes how nursing competence and work environment relate to different patterns of health system resilience.
A multiple center cross-sectional study was conducted between December 2023 and January 2024 across 33 hospitals in eastern China, involving 2435 nurses.
Questionnaires measuring nursing competence, work environment resources, nurse disaster resilience, and organizational commitment to resilience were utilised, along with the collection of additional personal demographic data. Structural equation modelling and cluster analysis were performed to explore the underlying mechanisms within the overall model and across multiple groups. Multivariable regression was conducted to identify variables associated with resilience in different subgroups.
Structural equation modelling demonstrated significant influences of nursing competence and work environment support on system resilience. Cluster analysis identified four resilience patterns: strong, marginal, low, and critical vulnerability. Strong resilience correlated with balanced individual-organizational resources, while vulnerable systems relied heavily on environmental support.
Our findings support policymakers and managers in developing systematic strategies with distinct focal points—targeting nurse workforce investment and optimised work environment—to enhance health system resilience across varying levels of public health emergencies.
This study validated the framework connecting individual and organizational resilience, offering evidence-based insights for nurse training and resource allocation to enhance healthcare systems' adaptability during disasters.
The study addressed how nursing competence and work environment significantly influenced resilience during public health emergencies, identified four resilience patterns, and provided insights to guide policymakers and healthcare managers in developing targeted, effective strategies.
Strengthening the Reporting of Observational studies in Epidemiology checklist.
No patient or public contribution.
To develop and validate a machine learning-based risk prediction model for delirium in older inpatients.
A prospective cohort study.
A prospective cohort study was conducted. Eighteen clinical features were prospectively collected from electronic medical records during hospitalisation to inform the model. Four machine learning algorithms were employed to develop and validate risk prediction models. The performance of all models in the training and test sets was evaluated using a combination of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, Brier score, and other metrics before selecting the best model for SHAP interpretation.
A total of 973 older inpatient data were utilised for model construction and validation. The AUC of four machine learning models in the training and test sets ranged from 0.869 to 0.992; the accuracy ranged from 0.931 to 0.962; and the sensitivity ranged from 0.564 to 0.997. Compared to other models, the Random Forest model exhibited the best overall performance with an AUC of 0.908 (95% CI, 0.848, 0.968), an accuracy of 0.935, a sensitivity of 0.992, and a Brier score of 0.053.
The machine learning model we developed and validated for predicting delirium in older inpatients demonstrated excellent predictive performance. This model has the potential to assist healthcare professionals in early diagnosis and support informed clinical decision-making.
By identifying patients at risk of delirium early, healthcare professionals can implement preventive measures and timely interventions, potentially reducing the incidence and severity of delirium. The model's ability to support informed clinical decision-making can lead to more personalised and effective care strategies, ultimately benefiting both patients and healthcare providers.
This study was reported in accordance with the TRIPOD statement.
No patient or public contribution.
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