Hostname: page-component-89b8bd64d-72crv Total loading time: 0 Render date: 2026-05-07T16:13:18.652Z Has data issue: false hasContentIssue false

Characterizing experiential elements of early-life stress to inform resilience: Buffering effects of controllability and predictability and the importance of their timing

Published online by Cambridge University Press:  27 July 2023

Emily M. Cohodes*
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA
Lucinda M. Sisk
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA
Taylor J. Keding
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA Child Study Center, Yale School of Medicine, New Haven, CT, USA
Jeffrey D. Mandell
Affiliation:
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
Madeline E. Notti
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA
Dylan G. Gee*
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA
*
Corresponding authors: Emily M. Cohodes; Dylan G. Gee; Emails: emily.cohodes@yale.edu; dylan.gee@yale.edu
Corresponding authors: Emily M. Cohodes; Dylan G. Gee; Emails: emily.cohodes@yale.edu; dylan.gee@yale.edu
Rights & Permissions [Opens in a new window]

Abstract

Key theoretical frameworks have proposed that examining the impact of exposure to specific dimensions of stress at specific developmental periods is likely to yield important insight into processes of risk and resilience. Utilizing a sample of N = 549 young adults who provided a detailed retrospective history of their lifetime exposure to numerous dimensions of traumatic stress and ratings of their current trauma-related symptomatology via completion of an online survey, here we test whether an individual’s perception of their lifetime stress as either controllable or predictable buffered the impact of exposure on trauma-related symptomatology assessed in adulthood. Further, we tested whether this moderation effect differed when evaluated in the context of early childhood, middle childhood, adolescence, and young adulthood stress. Consistent with hypotheses, results highlight both stressor controllability and stressor predictability as buffering the impact of traumatic stress exposure on trauma-related symptomatology and suggest that the potency of this buffering effect varies across unique developmental periods. Leveraging dimensional ratings of lifetime stress exposure to probe heterogeneity in outcomes following stress – and, critically, considering interactions between dimensions of exposure and the developmental period when stress occurred – is likely to yield increased understanding of risk and resilience following traumatic stress.

Information

Type
Special Issue Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Table 1. Descriptive statistics for main study variables

Figure 1

Table 2. Zero-order correlations among all study variables

Figure 2

Figure 1. Lifetime stressor controllability and stressor predictability moderate the association between lifetime stress and trauma-related symptomatology. Respectively, interaction effects for Models 1, 2, and 3 are shown here. Figure produced using the interActive data visualization tool (McCabe et al., 2018).

Figure 3

Table 3. Coefficients for development-naïve models

Figure 4

Figure 2. Stressor controllability and stressor predictability differentially moderate the association between developmental stage-specific stress exposure and trauma-related symptoms according to developmental stage. Respectively, interaction effects for Models 4, 5, and 6 are shown here. Given that multiple interaction effects were significant for Models 4 and 5, a selected interaction effect is visualized for each model. Figure produced using the interActive data visualization tool (McCabe et al., 2018).

Figure 5

Table 4. Coefficients for development-informed models