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UBC Theses and Dissertations

The multidisciplinary correlates of chronic stress in Canadians Hives, Benjamin A.


Background: Nearly one-quarter of Canadians report high levels of daily stress. This is alarming as chronic stress has been associated with several co-morbidities and premature mortality. In order to create beneficial interventions and public policy, factors associated with stress must be identified. While a wealth of research has determined a myriad of correlates of stress, the majority of this work has used approaches that focus on a very limited number of correlates per study, often from within one field of study. Currently there are no studies that analyze large-scale data sets and test multiple variables simultaneously. Methods: This study analyzed data from the 2012 Canadian Community Health Survey - Mental Health, including 67 factors from a range of disciplines and over 23,000 participants. This study uses two approaches to test the associations between these factors and chronic stress including traditional statistics (i.e., simple linear regression and multiple linear regression) and machine learning algorithms (i.e., random forest analysis). Results: The simple linear regression analysis showed that negative social interaction, life satisfaction, and higher levels of insomnia have the largest effect size in their association with chronic stress. Random forest analyses found that, after accounting for variance from other factors and considering complex interactions, life satisfaction, negative social interactions, and age were the most important correlates of chronic stress. Conclusion: This study highlights that the important correlates of stress do not come from one field, but rather are a combination of psychological, social, and demographic factors. These novel findings highlight potential target pathways for devising new stress reduction interventions. However, as this study was exploratory and correlational, more research is needed regarding direction of effect and potential confounding variables.

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