Date of Award

9-27-2023

Publication Type

Thesis

Degree Name

M.A.

Department

Psychology

Keywords

biopsychosocial;chronic illness;emerging adults;mixed-methods;post-traumatic growth;social support

Supervisor

Jessica Kichler

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

According to the Chronic Disease Prevention Alliance of Canada (2017), approximately 60% of Canadian adults suffer from a chronic medical condition. Managing a chronic medical illness provides an opportunity for post-traumatic growth (PTG). PTG is the positive psychological change that develops because of experiencing a trauma or highly stressful event. The current study evaluated a collection of biopsychosocial factors as potential predictors of PTG. Specifically, we hypothesized that physical pain, perceived social support, coping, pain self-efficacy, pain acceptance, and resilience would produce a model that significantly predicts PTG. Both quantitative and qualitative data from undergraduate students aged 18 to 25 was obtained to gain a comprehensive understanding of the factors that contribute to PTG and how those factors interact with the management of chronic illnesses. Five linear regression analyses were conducted, one for each predictor variable, with resilience as a mediator for PTG. Resilience significantly mediated the relationships between social support, pain intensity, and pain self-efficacy and PTG. Adaptive coping directly affected PTG whereas pain acceptance did not predict PTG in this sample. Furthermore, thematic analysis (Braun & Clark, 2021) was used to analyze the qualitative semi-structured interviews. Five themes were generated using thematic analysis from the qualitative data: 1) embracing the “silver-lining”, 2) integration of the condition, 3) things I wish I knew, 4) chronic illness changes social networks, and 5) the ripple effect. Future research needs a more advanced statistical approach (e.g., SEM) to evaluate how the various predictor variables may potentially interact, especially within different severity levels of chronic pain symptoms.

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