Resilience, coping, and distress among healthcare service personnel during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has a detrimental effect on the health and well-being of health care workers (HCWs). The extent to which HCWs may differ in their experience of depression and anxiety is unclear, and longitudinal studies are lacking. The present study examined theorized differences in distress between resilient and non-resilient HCWs over time, as reported in a national online survey. We also examined possible differences in distress as a function of sex and doctoral-level status. METHODS: A national sample responded to an online survey data that included the study measures. Of the HCWs who responded, 666 had useable data at the two time points. A longitudinal structural equation model tested an a priori model that specified the relationship of a resilient personality prototype to self-reported resilience, coping, depression and anxiety at both measurement occasions. Additional invariance models examined possible differences by sex and doctoral-level status. RESULTS: The final model explained 46.4% of the variance in psychological distress at Time 1 and 69.1% at Time 2. A non-resilient personality prototype predicted greater depression and anxiety. A resilient personality prototype was predictive of and operated through self-reported resilience and less disengaged coping to effect lower distress. No effects were found for active coping, however. The final model was generally invariant by sex and HCWs status. Additional analyses revealed that non-doctoral level HCWs had significantly higher depression and anxiety than doctoral-level HCWs on both occasions. CONCLUSIONS: HCWs differ in their susceptibility to distress imposed by COVID-19. Those who are particularly vulnerable may have characteristics that contribute to a lower sense of confidence and efficacy in stressful situations, and more likely to rely on ineffective, disengaged coping behaviors that can exacerbate stress levels. Individual interventions and institutional policies may be implemented to support HCWs at risk.

Parallel processing modeling in longitudinal designs: An example predicting trajectories of distress and life satisfaction.

PURPOSE: Parallel process modeling (PPM) can be used to analyze co-occurring relationships between health and psychological variables over time. A demonstration is provided using data obtained from the British Household Panel Survey (years 2005, 2006, 2007, and 2008), examining predictors of ongoing changes in their distress and life satisfaction of a subsample from the survey. RESEARCH METHOD: In the 2005 survey, data were available from 7,970 participants based on the following demographic variables: gender, age, ever registered as disabled, and ever experienced any strokes (before or at 2005). Time-varying variables included distress and life satisfaction collected yearly from 2005 to 2008. Time-invariant variables included age (65 or older), gender, disability condition, and stroke survivor status. RESULTS: Steps of fitting the PPM are presented. Four distinct distress trajectory groups-chronic, recovery, delayed, and resilient-were identified from the PPM estimates. Resilient and recovery groups showed a positive trend in life satisfaction. The delayed distress and chronic groups had a slight decrease in satisfaction. The time-invariant covariates only significantly predicted baseline levels of distress and satisfaction (i.e., their intercepts). CONCLUSIONS: PPM is a relatively simple and powerful tool for simultaneously studying relations between multiple processes. A step-by-step approach on decomposing the significant predictive relation from the change of distress to the change of satisfaction is presented. Properly decomposing any significant growth factor regressed on another growth factor is necessary to fully comprehend the intricate relationships within the results. Practical implications and additional methodological information about fitting PPM are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).