Suchergebnisse

In the context of the Corona Virus Disease 2019 (COVID-19) pandemic, medical staff have experienced traumatic stress events. Research on post-traumatic growth (PTG) was limited. We aimed to assess the relationship between perceived social support and PTG among medical staff 3 weeks after the policy opening of COVID-19 pandemic in Nanjing, China and explore the mediating roles of deliberate rumination. A multicenter study including a total of 842 medical staff was conducted at two general hospitals from December 10, 2022, to December 25, 2022, in Nanjing, China. SPSS was used for statistical description and univariate analysis, and AMOS was used for structural equation modeling (SEM) to analyze the medicating effect of deliberate social support on PTG. In the SEM, the total effect of perceived social support on PTG was significant (β = .332, 95% CI [0.259, 0.408], p < .001). In addition, deliberate rumination played a mediating role between perceived social support and PTG, and the indirect effect of deliberate rumination between perceived social support and PTG was −.036 (95 % CI [−0.059, −0.018], p < .001). Perceived social support has significant direct and indirect effects on PTG. In the context of major public health emergencies, providing medical staff with more social support and guiding them to reflect positively could help medical staff achieve PTG after experiencing a traumatic event.

AbstractResearch in communication and other social science disciplines that relies on measuring each member of a dyad on putative causes and effects can require complex analyses to illuminate how members of the dyad influence one another. Dyadic mediation analysis is a branch of mediation analysis that focuses on establishing the mechanism(s) by which mutual influence operates. Relying on the similarity between dyadic mediation analysis using structural equation modeling and mediation analysis with ordinary least squares regression, we developed MEDYAD, an easy-to-use computational tool for SPSS, SAS, and R that conducts dyadic mediation analysis with distinguishable dyadic data. MEDYAD implements the Actor-Partner Interdependence Model Extended to Mediation (APIMeM), as well as simpler and more complex dyadic mediation models. Bootstrapping methods are implemented for inferences about indirect effects. Additional features include methods for conducting all possible pairwise comparisons between indirect effects, heteroskedasticity-robust inference, and saving bootstrap estimates of parameters for further analysis.