Stress response and experiential avoidance among firefighters: Preliminary insights from network analyses.

Firefighters are at increased risk of developing posttraumatic stress disorder (PTSD) due to exposure to potentially traumatic events during their careers. However, little is known about the prevalence of PTSD among this population, particularly when taking moderating variables into account. Using Gaussian Graphical Models and Directed Acyclic Graphs, we conducted network analyses to examine the interactions between clusters of PTSD symptoms, perceived stress, hardiness, and experiential avoidance among 187 firefighters. The data and code are published with the paper. Experiential avoidance, as part of psychological inflexibility, was found to be the only variable that interacted with PTSD symptomatology. Strong positive associations were observed between experiential avoidance and the "negative mood and cognitions" subscale of the PTSD Checklist for DSM-5 (PCL-5). Through this association, other PTSD symptoms were activated, particularly avoidance and arousal. Our findings suggest that experiential avoidance and negative mood and cognition symptoms are particularly important in the expression of PTSD symptomatology in firefighters. In addition, experiential avoidance may be used as a coping strategy to reduce perceived stress during potentially traumatic events. Therefore, experiential avoidance may be a prime target for future interventions and training focused on flexible self-regulation strategies in this population.

A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting.

Multistep-ahead forecasts can either be produced recursively by iterating a one-step-ahead time series model or directly by estimating a separate model for each forecast horizon. In addition, there are other strategies; some of them combine aspects of both aforementioned concepts. In this paper, we present a comprehensive investigation into the bias and variance behavior of multistep-ahead forecasting strategies. We provide a detailed review of the different multistep-ahead strategies. Subsequently, we perform a theoretical study that derives the bias and variance for a number of forecasting strategies. Finally, we conduct a Monte Carlo experimental study that compares and evaluates the bias and variance performance of the different strategies. From the theoretical and the simulation studies, we analyze the effect of different factors, such as the forecast horizon and the time series length, on the bias and variance components, and on the different multistep-ahead strategies. Several lessons are learned, and recommendations are given concerning the advantages, disadvantages, and best conditions of use of each strategy.