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To develop a predictive model for high-burnout of nurses.
A cross-sectional study.
This study was conducted using an online survey. Data were collected by the Chinese Maslach Burnout Inventory-General Survey (CMBI-GS) and self-administered questionnaires that included demographic, behavioural, health-related, and occupational variables. Participants were randomly divided into a development set and a validation set. In the development set, multivariate logistic regression analysis was conducted to identify factors associated with high-burnout risk, and a nomogram was constructed based on significant contributing factors. The discrimination, calibration, and clinical practicability of the nomogram were evaluated in both the development and validation sets using receiver operating characteristic (ROC) curve analysis, Hosmer–Lemeshow test, and decision curve analysis, respectively. Data analysis was performed using Stata 16.0 software.
A total of 2750 nurses from 23 provinces of mainland China responded, with 1925 participants (70%) in a development set and 825 participants (30%) in a validation set. Workplace violence, shift work, working time per week, depression, stress, self-reported health, and drinking were significant contributors to high-burnout risk and a nomogram was developed using these factors. The ROC curve analysis demonstrated that the area under the curve of the model was 0.808 in the development set and 0.790 in the validation set. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets.
This study has developed and validated a predictive nomogram for identifying high-burnout in nurses.
The nomogram conducted by our study will assist nursing managers in identifying at-high-risk nurses and understanding related factors, helping them implement interventions early and purposefully.
The study adhered to the relevant EQUATOR reporting guidelines: TRIPOD Checklist for Prediction Model Development and Validation.
No patient or public contribution.
Chronic obstructive pulmonary disease (COPD) causes airflow blockage and breathing-related issues. This chronic disease impacts people worldwide. Substantial evidence supports the use of cognitive behavioral therapy (CBT) to help patients with chronic illnesses cope with worrisome and painful symptoms. However, the impact of CBT on COPD outcomes is less understood.
In this study, we systematically summarized the effects of CBT on lung function, anxiety and depressive symptoms, and quality of life of patients with COPD.
Six English-language and four Chinese-language databases were systematically searched for relevant randomized controlled trials published through April 15, 2023. Studies in which CBT was the only difference in treatment administered to experimental and control groups were included in the review. The studies' risk of bias was evaluated using the Cochrane Criteria.
Sixteen studies (1887 participants) were included. The meta-analysis showed that CBT improved the percent-predicted forced expiratory volume in 1 second (FEV1%), forced vital capacity (FVC), FEV1/FVC ratio, maximal voluntary ventilation, peak expiratory flow, treatment compliance, and World Health Organization abbreviated quality of life, Self-rating Anxiety and Depression Scale, and St George's Respiratory Questionnaire scores compared with the control (all p < .05).
This review demonstrated that CBT improves the lung function, anxiety and depressive symptoms, treatment compliance, and quality of life of patients with COPD and can be used widely in the clinical treatment of this disease.
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