Quality of life in multiple scenarios: The impact of work mode and social contact quantity.

Introduction: During the COVID-19 pandemic, many employees were encouraged to temporarily work from home as an attempt to decrease social contact with others. However, the employees' quality of life (QoL) may have been threatened by this mode of working. This study, therefore, aims to explore the employees' QoL given the new mode of working from home (WFH) as a result of the pandemic vs. working in the office (WIO), the amount of social contact that they were exposed to, and the ratio of face-to-face contact that they had. Methods: A total of 803 WFH employees and 588 WIO employees' QoL was assessed during the same time period using the WHOQOL-BREF, which contains four domains: physical health, psychological health, social relationship, and the environment. We then divided the participants into 16 groups in accordance with the levels of work mode, social contact quantity, and face-to-face contact ratio-forming a case-control study. A differential item functioning (DIF) analysis was used to analyze the responses on the WHOQOL-BREF under the 4-dimensional rating scale model fitting. Results: The results indicated that WFH employees' QoL was superior to that of WIO employees. The relationship between the WFH mode and the employees' QoL was specifically moderated by the amount of social contact and the ratio of face-to-face contact that was experienced. The results further demonstrated that the increased amount of non-face-to-face contact was better for WFH employees' QoL than that of WIO employees. Discussion: In conclusion, the WFH mode was practical during the COVID-19 pandemic, as our findings indicated that WFH employees' QoL was better than WIO employees' QoL. However, maintaining social connections is equally important as this allows employees to perform better at their jobs and maintain such performance. The employees with a higher number of social support had a better QoL. Additionally, the facets detected as DIF items provided implications for the QoL with regard to the research methodology and insight into factors affecting the employees' QoL.

Determinants of personal vaccination hesitancy before and after the mid-2021 COVID-19 outbreak in Taiwan.

BACKGROUND: Using a 10 week nationwide online survey performed during a time period containing the time ahead, the start, and the peak of a COVID-19 outbreak in Taiwan, we investigated aspects that could affect participants' vaccination intentions. METHODS: From March to May 2021, we surveyed 1,773 people in Taiwan, aged from 20 to 75 years, to determine potential acceptance rates and factors influencing the acceptance of a COVID-19 vaccine. We used an ordinal logistic regression with a backward selection method to identify factors that affected vaccination intention. RESULTS: Several factors could increase individuals' vaccination intentions including: being male, older, with an openness personality, having a better quality of life in the physical health domain, having better knowledge and personal health behavior, having more trust in the government, and being worried about misinformation. Perceived risks played a crucial role in the vaccine decision-making process. When the pandemic intensified, people's vaccination intentions increased significantly. CONCLUSION: The findings of the present study could highlight individuals' vaccination attitudes and provide governments with an empirical and dynamic base to design tailored strategies to increase vaccination rates.

A Social Desirability Item Response Theory Model: Retrieve-Deceive-Transfer.

In this study, a new item response theory model is developed to account for situations in which respondents overreport or underreport their actual opinions on a positive or negative issue. Such behavior is supposed to be a result of deception and transfer mechanisms. In the proposed model, this behavior is simulated by incorporating a deception term into a multidimensional rating scale model, followed by multiplication by a transfer term, with the two operations performed by an indicator function and a transition matrix separately. The proposed model is presented in a Bayesian framework approximated by Markov chain Monte Carlo algorithms. Through a series of simulations, the parameters of the proposed model are recovered accurately. The methodology is also implemented within an online experimental study to demonstrate the methodology's application.