The utility of coping through emotional approach: A meta-analysis.

OBJECTIVE: A systematic review and meta-analysis was conducted to examine associations between attempts to cope with stressors through the two facets of emotional approach coping (EAC; i.e., processing and expressing stressor-related emotions) and indicators of physical and mental health. METHOD: EBSCO databases including MEDLINE, PsycINFO, and Cochrane Collections were searched from inception to November 2022. In all, 86 studies were included in a meta-analytic evaluation using a random-effects model and meta-regression analysis. RESULTS: EAC was associated with better overall health (r = .05; p = .04; 95% confidence interval = [.003, .10]). Emotional expression (EE) and emotional processing (EP) also were positively associated with better overall health, although these relationships were not statistically significant. In meta-regressions examining specific health domains, EAC was linked to better health in biological/physiological, physical, and resilience-related psychological adjustment domains, as well as to worse outcomes in the risk-related psychological adjustment and mental/emotional distress domains. Results for EE and EP mirrored this pattern; however, only EP was associated with more engagement in health-promoting behaviors. CONCLUSIONS: Coping with stressors through emotional approach appears to be associated with better mental and physical health, with some observed differences for EE and EP. The literature on EAC and health is marked by heterogeneity across study methodologies and measures. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effect sizes in ANCOVA and difference-in-differences designs.

It is common practice in both randomized and quasi-experiments to adjust for baseline characteristics when estimating the average effect of an intervention. The inclusion of a pre-test, for example, can reduce both the standard error of this estimate and-in non-randomized designs-its bias. At the same time, it is also standard to report the effect of an intervention in standardized effect size units, thereby making it comparable to other interventions and studies. Curiously, the estimation of this effect size, including covariate adjustment, has received little attention. In this article, we provide a framework for defining effect sizes in designs with a pre-test (e.g., difference-in-differences and analysis of covariance) and propose estimators of those effect sizes. The estimators and approximations to their sampling distributions are evaluated using a simulation study and then demonstrated using an example from published data.