Background: This study examines whether stronger corporate governance is associated with workplace mental health practices and whether this relationship is mediated by a structured sequence of occupational safety and health (OSH) engagement: recognition, goal-setting, and implementation. Methods: We analyzed 134 listed companies in Taiwan that published sustainability reports and received corporate governance evaluations between 2014 and 2023. Governance scores―based on shareholder rights, board functioning, transparency, and sustainability―were dichotomized into high vs. low categories. Workplace mental health practices were measured using 24 binary indicators across planning, provision, and reporting. OSH engagement was conceptualized in three stages based on Global Reporting Initiative 403 guidelines, with composite indicators derived via grouped weighted quantile sum regression. Associations were analyzed using generalized linear mixed-effects and serial mediation models. Results: Compared to companies ranked low in corporate governance, those ranked high had a higher likelihood of adopting mental health practices. OSH goal-setting and implementation showed significant positive associations, whereas recognition did not. Serial mediation analysis showed that 83% of the total effect of corporate governance on mental health practices was direct, while 17% was mediated through structured OSH engagement, primarily via implementation. Recognition showed a nonsignificant effect but initiated significant sequential pathways via goal-setting and implementation.Goal-setting functioned as a bridge within these chains. Conclusion Corporate governance plays a central role in advancing workplace mental health practices.While most effects are direct, structured OSH engagement helps translate governance priorities into sustained organizational actions that embed mental health into routine practice.

Background: Telework adoption in Taiwan has surged because of government guidelines during the COVID-19 pandemic. This study examined the disclosure practices of Taiwanese-listed companies, assessing their adherence to government telework guidelines and their correlation with corporate governance, focusing on occupational health measures. Methods: We conducted a guideline-adherent cohort analysis of the 2020 and 2021 sustainability reports of 295 Taiwanese-listed companies. We assessed their disclosure of corporate measures for teleworking in alignment with two government guidelines, specifically occupational health measures. Using the McNemar test and general estimating equation analysis, we compared the 2020 and 2021 responses and examined their associations with corporate governance rankings. Results: Telework adoption increased significantly from 2020 to 2021, with 68% of companies reporting new work modes. The mentioning of government guidelines also increased to 67% by 2021. Companies with higher governance rankings were more likely to adopt online occupational health measures, including occupational health services (RR = 2.03; 95% CI = 1.41–2.94; p < 0.001) and mental health promotion activities (RR = 2.01; 95% CI = 1.06–3.82; p = 0.032), than those with low rankings. Although on-site and online occupational health services increased, home workspace assessments did not. Conclusion Our findings highlight significant upward trends in the disclosure of telework measures following the issuance of government guidelines. Corporate governance is significantly associated with the implementation of occupational health measures. Amid the evolution of teleworking, both government guidelines and corporate governance have become essential for shaping work arrangements and ensuring workforce well-being.

The increasing prevalence of high body mass index (BMI) emphasizes the need for action. Understanding of BMI factors among military hospital healthcare workers remains limited. This study aims to address this gap by analyzing BMI risk factors and changes pre- and post-coronavirus 2019 pandemic among military hospital healthcare workers in central Taiwan from 2019 to 2021.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

The increasing prevalence of high body mass index (BMI) emphasizes the need for action. Understanding of BMI factors among military hospital healthcare workers remains limited. This study aims to address this gap by analyzing BMI risk factors and changes pre- and post-coronavirus 2019 pandemic among military hospital healthcare workers in central Taiwan from 2019 to 2021.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

The increasing prevalence of high body mass index (BMI) emphasizes the need for action. Understanding of BMI factors among military hospital healthcare workers remains limited. This study aims to address this gap by analyzing BMI risk factors and changes pre- and post-coronavirus 2019 pandemic among military hospital healthcare workers in central Taiwan from 2019 to 2021.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.