Multidimensional correlates of psychological stress: Insights from traditional statistical approaches and machine learning using a nationally representative Canadian sample
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".