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On the basis of demand survey feedback from individuals with disabilities and caregivers, this study designed two sets of functional garments for long-term bedridden patients, with the primary objective of increasing convenience and reducing the physical workload of caregivers.

Wear trials were conducted by employing 24 subjects to perform 11 different tasks to compare the performance of the two newly developed garments with that of conventional hospital patient apparel. Task operation time, heart rate (HR), electromyography (EMG) signals, and subjective perceptions were evaluated.

The new functional garments reduced the time required to perform tasks by 29–79%, maintained the average HR of caregivers at approximately the resting threshold and resulted in a 37–74% reduction in the root mean square (RMS) of the EMG at the arm muscles in the private and thigh nursing tasks. All the subjective and objective evaluation results of the caregivers demonstrated varying degrees of correlation.

This study has practical implications for the design of functional clothing for long-term bedridden patients and provides guidance for evaluating the ergonomics of garments that can be utilized only with caregiver support.

In contrast to previous studies that focused primarily on individuals with disabilities while overlooking the indispensable role of caregivers in the nursing process, this study shifted its emphasis to long-term bedridden patients who relied exclusively on caregivers for daily activities. Additionally, this study attempted to analyze the correlations between the evaluation parameters to explore the relationships between the evaluation methods.

This study aims to explore the roles of Zhongyong and political efficacy on citizens’ intention to use digital government platforms for e-participation (i.e. e-participation intention). Zhongyong is a dialectical way of thinking that influences Chinese behavioral intentions and approaches. Political efficacy is a predictor of traditional political participation. Both of them have not been adequately investigated in this digital era, particularly regarding e-participation in digital government platforms. Therefore, this study investigates their relationships.

A quantitative model is constructed to examine the relationship between Zhongyong and citizens’ e-participation intention (internal and external) political efficacy serves as a mediator. An online questionnaire gathered 345 responses from three representative provinces of China (i.e. Guangdong, Jiangxi and Shanxi). Partial least square structural equation modeling (PLS-SEM) was adopted and executed with Smart PLS 4.0 to analyze the data.

Zhongyong and (internal and external) political efficacy can positively influence citizens’ e-participation intention. Moreover, (internal and external) political efficacy mediates the relationship between Zhongyong and citizens’ e-participation intention.

This research focuses on Chinese culture Zhongyong and surveyed Chinese citizens, thus is limited to the Chinese context. Second, this study used cross-sectional data. Third, this study only investigated two factors’ effects on e-participation, i.e. Zhongyong and political efficacy.

The findings provide multifaceted strategies for improving citizens’ adoption of digital government platforms for e-participation. Incentive policies to boost citizens’ (internal and external) political efficacy can be launched. To achieve broader citizen participation, a participative culture can be cultivated based on Zhongyong.

This study constructs a novel model that innovatively links Zhongyong thinking, political efficacy and e-participation intention. The results underscore the importance of Zhongyong culture and political efficacy in increasing citizens’ e-participation intention.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.