Adopting social identity theory, this study examined the process linking the relations between internal corporate social responsibility (InCSR), work engagement, and turnover intention by focusing on the mediating influence of organizational identification and the moderating role of perceived corporate hypocrisy. Data were obtained from 311 medical staff (excluding supervisors and managers) of a public regional teaching hospital in Taiwan. The results revealed that employees are more dedicated to work and less inclined to leave the firm if they perceive that InCSR is implemented within the firm. However, if an employee perceives corporate hypocrisy of inconsistency between communication and actual actions, it may have the opposite effect on employees. Likewise, the higher the level of perceived corporate hypocrisy, the lesser the positive effect of InCSR on employee behavior. Finally, the implications, limitations, and suggestions for future research were discussed.

This study assessed the feasibility of five separate machine learning (ML) classifiers for predicting disease progression in patients with pre-dialysis chronic kidney disease (CKD). The study enrolled 858 patients with CKD treated at a veteran’s hospital in Taiwan. After classification into early and advanced stages, patient demographics and laboratory data were processed and used to predict progression to renal failure and important features for optimal prediction were identified. The random forest (RF) classifier with synthetic minority over-sampling technique (SMOTE) had the best predictive performances among patients with early-stage CKD who progressed within 3 and 5 years and among patients with advanced-stage CKD who progressed within 1 and 3 years. Important features identified for predicting progression from early- and advanced-stage CKD were urine creatinine and serum creatinine levels, respectively. The RF classifier demonstrated the optimal performance, with an area under the receiver operating characteristic curve values of 0.96 for predicting progression within 5 years in patients with early-stage CKD and 0.97 for predicting progression within 1 year in patients with advanced-stage CKD. The proposed method resulted in the optimal prediction of CKD progression, especially within 1 year of advanced-stage CKD. These results will be useful for predicting prognosis among patients with CKD.

The objective of the study was to explore the dynamic effects of socioeconomic status (SES) and lifestyle behaviors on the risks of metabolic syndrome (MS) or cardiovascular disease (CVD) in life course. The data of 12,825 subjects (6616 males and 6209 females) who underwent repeated examinations and answered repeated questionnaires from 2006 to 2014 at the Major Health Screening Center in Taiwan, was collected and analyzed. The trajectory of trends in the subjects’ SES and lifestyle mobility over time was observed, and the effects of factors with potential impacts on health were tested and analyzed using multiple logistic regression and a generalized estimated equation model. A 10% increase in MS prevalence was observed over the nine-year period. The average Framingham CVD score for people with MS was estimated to be about 1.4% (SD = 1.5%). Except for middle-aged women, marriage was found to raise the risk of CVD, whereas increasing education and work promotions independently reduced CVD risk for the majority of subjects. However, the risk of CVD was raised by half for young men who had a job or lost a job in comparison to continuously unemployed young men. Physical activity was only found to be advantageous for disease prevention in those aged less than 40 years; increased exercise levels were useless for reducing CVD risk among older men. Alcohol drinking and betel chewing caused increased CVD risk in the old and young subjects, respectively, whereas vegetarian diets and vitamin C/E intake were helpful in preventing CVD, even if those habits were ceased in later life. For middle-aged women, getting sufficient sleep reduced CVD risk. We concluded that SES and lifestyle behaviors may have different effects on health over time, among various populations. Accordingly, suggestions can be provided to healthcare workers in designing health promotion courses for people at different life stages.

Although many studies have investigated burnout, stress, and mental health issues among health care workers (HCWs) during the COVID-19 pandemic, few have linked these relationships to chronic physiological illnesses such as cardiovascular diseases. This study assessed changes in cardiovascular risk factors in HCWs and other hospital workers during the COVID-19 pandemic and identified vulnerable groups at a higher risk of increased adverse cardiovascular conditions. Five hundred and fourteen hospital employees ≥ 20 years of age underwent physical examinations and laboratory testing once before and once after the first wave of the pandemic in Taiwan during 2020 and 2021. Their sociodemographic characteristics and cardiovascular risk factors, including blood pressure, blood biochemical parameters, and body mass index, were collected. The differences between pre- and post-pandemic measurements of their biophysical and blood biochemical parameters were analyzed using pairwise tests. The post-pandemic increases in their parameter levels and cardiovascular risk as a function of underlying factors were estimated from multivariate regressions. HCWs showed significant increases in levels and abnormal rates of BMI, blood pressure, plasma glucose, and total cholesterol after the pandemic. Post-pandemic increases in BMI, waist circumference, and blood pressure were higher in females than in males. Workers with higher levels of education or longer job tenure had greater increases in BMI, triglyceride, and total cholesterol levels than other workers. Females had a higher incidence of abnormal BMI and hypertension than males (adjusted odds ratios [AORs] of 8.3 and 2.9, respectively). Older workers’ incidence of hypertension was higher than younger workers’ (AOR = 3.5). Preventive strategies should be implemented for HCWs susceptible to cardiovascular diseases during emerging infectious disease outbreaks.

Chronic kidney disease (CKD) poses significant challenges to public health and healthcare systems, demanding a comprehensive understanding of its progressive nature. Prior methods have often fallen short in capturing the dynamic and individual variability of renal function. This study aims to address this gap by introducing a novel approach for the individualized assessment of CKD progression. A cohort of 1042 patients, comprising 700 with stage 3a and 342 with stage 3b to stage 5 CKD, treated at a veteran general hospital in Taiwan from 2006 to 2019, was included in the study. A comprehensive dataset spanning 12 years, consisting of clinical measurements, was collected and analyzed using joint models to predict the progression to hemodialysis treatment. The study reveals that the estimated glomerular filtration rate (eGFR) can be considered an endogenous factor influenced by innate biochemical markers. Serum creatinine, blood pressure, and urinary protein excretion emerged as valuable factors for predicting CKD progression. The joint model, combining longitudinal and survival analyses, demonstrated predictive versatility across various CKD severities. This innovative approach enhances conventional models by concurrently incorporating both longitudinal and survival analyses and provides a nuanced understanding of the variables influencing renal function in CKD patients. This personalized model enables a more precise assessment of renal failure risk, tailored to each patient’s unique clinical profile. The findings contribute to improving the management of CKD patients and provide a foundation for personalized healthcare interventions in the context of renal diseases.