Suchergebnisse

The novel coronavirus disease 2019 (COVID-19) rapidly escalated to a global pandemic. To control the rate of transmission, governments advocated that the public practice social distancing, which included staying at home. However, compliance with stay-at-home orders has varied between countries such as China and the United States, and little is known about the mechanisms underlying the national differences. Based on the health belief model, the theory of reasoned action, and the technology acceptance model, health beliefs and behavioral intention are suggested as possible explanations. A total of 498 Chinese and 292 American college students were recruited to complete an online survey. The structural equation modeling results showed that health beliefs (i.e., perceived susceptibility, severity, and barriers) and behavioral intention played multiple mediating roles in the association between nationality and actual stay-at-home behaviors. Notably, the effect via perceived barriers → behavioral intention was stronger than the effects via perceived susceptibility and severity → behavioral intention. That is, American participants perceived high levels of susceptibility whereas Chinese participants perceived high levels of severity, especially few barriers, which further led to increased behavioral intention and more frequent stay-at-home behaviors. These findings not only facilitate a comprehensive understanding of cross-country differences in compliance with stay-at-home orders during peaks in the COVID-19 pandemic but also lend support for mitigation of the current global crisis and future disease prevention and health promotion efforts.

The paper examined the relations among problem solving, automaticity, and working memory load (WML) by changing the difficulty level of task characteristics through two applications. In Study 1, involving 68 engineering students, a 2 (automaticity) × 2 (WML) design was utilized for arithmetic problems. In Study 2, involving 76 engineering students, a 2 (automaticity) × 2 (WML) design was used for linear algebra tasks. In both studies, there were statistically significant main effects and interaction effects of automaticity and WML on the variable of response time, concurring with the cognitive load theory. The simple effect of WML rendered a larger effect size under the conditions with low automaticity. When the testing condition was easy but contained more steps, the students were more accurate, and response times were faster. When the testing condition was difficult but contained fewer steps, the students were less accurate, and response times were slower. The findings underscore the important role of automaticity in helping engineering students bypass the limits of working memory.