The impact of telecom industry employees' stress perception on job burnout: moderated mediation model.

BACKGROUND: The rapid development of the telecommunications industry in the post-COVID-19 era has brought tremendous pressure to employees making them a high-risk group for job burnout. However, prior research paid less attention to the burnout of employees. Furthermore, social support and gender have separate effects on job burnout. This study explores the mechanism of stress perception on job burnout and examines the roles of social support and gender amid it. METHOD: This cross-sectional study was conducted from June 2023 to August 2023 in mainland China. A total of 39,507 were recruited by random sampling and online questionnaires, and 28,204 valid questionnaires were retained. SPSS (version 26.0) and PROCESS (Model 4 & 7) were used for correlation analysis, mediation analysis, and mediated moderation analysis. RESULT: Stress perception can positively predict the level of job burnout of employees in the telecommunications industry, and social support plays a partial mediating role, accounts for 8.01% of the total effect, gender moderates the first half of the path in this mediation model. At the same pressure level, female can perceive more social support than male. CONCLUSIONS: Under high pressure background, employees' job burnout varies depending on gender and the perception of social support. Therefore, telecommunications industry managers should adopt decompression measures and targeted social support resources for different groups.

Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer.

PURPOSE: To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis. METHODS: In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram. RESULTS: Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram. CONCLUSIONS: Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.