Benjamin A Hives, Mark R Beauchamp, Yan Liu, Jordan Weiss, Eli Puterman
{"title":"心理压力的多维相关性:来自传统统计方法和机器学习的见解,使用具有全国代表性的加拿大样本。","authors":"Benjamin A Hives, Mark R Beauchamp, Yan Liu, Jordan Weiss, Eli Puterman","doi":"10.1371/journal.pone.0323197","DOIUrl":null,"url":null,"abstract":"<p><p>Approximately one-fifth of Canadians report high levels of psychological stress. This is alarming, as chronic stress is associated with non-communicable diseases and premature mortality. In order to create effective interventions and public policy for stress reduction, factors associated with stress must be identified and understood. We analyzed data from the 2012 'Canadian Community Health Survey - Mental Health' (CCHS-MH), including 66 potential correlates, drawn from a range of domains (e.g., psychological, physical, social, demographic factors). First, we used a random forest algorithm to determine the most important predictors of psychological stress, then we used linear regressions to quantify the linear associations between the important predictors and psychological stress. In total, 23,089 Canadian adults responded to the 2012 CCHS-MH, which was weighted to be nationally representative. Random forest analyses found that, after accounting for variance from other factors and considering complex interactions, life satisfaction (relative importance = 1.00), negative social interactions (0.99), primary stress source (0.85), and age (0.77) were the most important correlates of psychological stress. To a lesser extent, employment status (0.36), was also an important variable. Univariable linear regression suggested that these variables had effects ranging from small to medium-to-large. Multiple linear regression showed that lower life satisfaction, being younger, greater negative social interaction, reporting a primary stressor, and being employed were all found to be associated with greater psychological stress (beta range = 0.03 to 0.84, all p < 0.001, R2 = 0.264). Further, these factors accounted for 26% of the variance of psychological stress. This study highlights that the most important correlates of stress reflect diverse psychological, social, and demographic factors. These findings highlight that stress reduction interventions may require a multidisciplinary approach. However, further longitudinal and experimental studies are required.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0323197"},"PeriodicalIF":2.6000,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12074393/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multidimensional correlates of psychological stress: Insights from traditional statistical approaches and machine learning using a nationally representative Canadian sample.\",\"authors\":\"Benjamin A Hives, Mark R Beauchamp, Yan Liu, Jordan Weiss, Eli Puterman\",\"doi\":\"10.1371/journal.pone.0323197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Approximately one-fifth of Canadians report high levels of psychological stress. This is alarming, as chronic stress is associated with non-communicable diseases and premature mortality. In order to create effective interventions and public policy for stress reduction, factors associated with stress must be identified and understood. We analyzed data from the 2012 'Canadian Community Health Survey - Mental Health' (CCHS-MH), including 66 potential correlates, drawn from a range of domains (e.g., psychological, physical, social, demographic factors). First, we used a random forest algorithm to determine the most important predictors of psychological stress, then we used linear regressions to quantify the linear associations between the important predictors and psychological stress. In total, 23,089 Canadian adults responded to the 2012 CCHS-MH, which was weighted to be nationally representative. Random forest analyses found that, after accounting for variance from other factors and considering complex interactions, life satisfaction (relative importance = 1.00), negative social interactions (0.99), primary stress source (0.85), and age (0.77) were the most important correlates of psychological stress. To a lesser extent, employment status (0.36), was also an important variable. Univariable linear regression suggested that these variables had effects ranging from small to medium-to-large. Multiple linear regression showed that lower life satisfaction, being younger, greater negative social interaction, reporting a primary stressor, and being employed were all found to be associated with greater psychological stress (beta range = 0.03 to 0.84, all p < 0.001, R2 = 0.264). Further, these factors accounted for 26% of the variance of psychological stress. This study highlights that the most important correlates of stress reflect diverse psychological, social, and demographic factors. These findings highlight that stress reduction interventions may require a multidisciplinary approach. However, further longitudinal and experimental studies are required.</p>\",\"PeriodicalId\":20189,\"journal\":{\"name\":\"PLoS ONE\",\"volume\":\"20 5\",\"pages\":\"e0323197\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12074393/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PLoS ONE\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1371/journal.pone.0323197\",\"RegionNum\":3,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS ONE","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1371/journal.pone.0323197","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Multidimensional correlates of psychological stress: Insights from traditional statistical approaches and machine learning using a nationally representative Canadian sample.
Approximately one-fifth of Canadians report high levels of psychological stress. This is alarming, as chronic stress is associated with non-communicable diseases and premature mortality. In order to create effective interventions and public policy for stress reduction, factors associated with stress must be identified and understood. We analyzed data from the 2012 'Canadian Community Health Survey - Mental Health' (CCHS-MH), including 66 potential correlates, drawn from a range of domains (e.g., psychological, physical, social, demographic factors). First, we used a random forest algorithm to determine the most important predictors of psychological stress, then we used linear regressions to quantify the linear associations between the important predictors and psychological stress. In total, 23,089 Canadian adults responded to the 2012 CCHS-MH, which was weighted to be nationally representative. Random forest analyses found that, after accounting for variance from other factors and considering complex interactions, life satisfaction (relative importance = 1.00), negative social interactions (0.99), primary stress source (0.85), and age (0.77) were the most important correlates of psychological stress. To a lesser extent, employment status (0.36), was also an important variable. Univariable linear regression suggested that these variables had effects ranging from small to medium-to-large. Multiple linear regression showed that lower life satisfaction, being younger, greater negative social interaction, reporting a primary stressor, and being employed were all found to be associated with greater psychological stress (beta range = 0.03 to 0.84, all p < 0.001, R2 = 0.264). Further, these factors accounted for 26% of the variance of psychological stress. This study highlights that the most important correlates of stress reflect diverse psychological, social, and demographic factors. These findings highlight that stress reduction interventions may require a multidisciplinary approach. However, further longitudinal and experimental studies are required.
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