Identifying patterns of multidimensional self-compassion in Chinese young adults: implications for longitudinal mental health outcomes during the pandemic

Abstract

Background: Self-compassion (SC), reflecting self-attitude and self-connectedness, has proven to be a modifiable factor in promoting mental health outcomes. Increasingly, SC is recognized as a multidimensional construct consisting of six dimensions, rather than a single dimension.

Objectives: First, this study adopted a person-centered approach to explore profiles of SC dimensions in Chinese young adults. Second, the study examined the predictive effects of SC profiles on mental health outcomes.

Methods: In February 2020, young adults (N = 1164) were invited to complete the 26-item Neff's Self-Compassion Scale online. Three months later, the same subjects (N = 1099) reported their levels of depressive symptoms, posttraumatic stress disorder (PTSD) symptoms, and posttraumatic growth (PTG).

Results: After controlling for retrospective ACEs, four classes best characterized the profiles: self-compassionate (26.7%, N = 294), self-uncompassionate (12.3%, N = 135), average (55.9%, N = 614), and detached groups (5.1%, N = 56). Young adults in the self-compassionate group adjusted the best (with the highest level of PTG and the lowest levels of depressive and PTSD symptoms). Adults in the self-uncompassionate group demonstrated the poorest mental health outcomes (with the lowest level of PTG and the highest levels of depressive and PTSD symptoms). Young adults in the average group obtained more PTG than adults in the detached group (p < .01), but did not differ significantly in depressive and PTSD symptoms (p > .05).

Conclusion: The compassionate profile is the most adaptable for young adults among all groups. This study highlights the limitations of representing the relative balance of SC with a composite score.

Keywords: latent profile analysis; mental health; multidimensional self-compassion; regression mixture analysis.