Examining Associations Between Distress Tolerance, Perceived COVID-19 Threat, and Psychological Outcomes: The Moderating Role of Social Support
Bibliographic record
Abstract
The COVID-19 pandemic has caused significant psychological distress worldwide. It is important to enhance our understanding of the interpersonal and intrapersonal processes that can be addressed to promote psychological well-being after experiencing an adverse event like a pandemic. Therefore, to understand the direct and indirect associations between distress tolerance and diverse psychological outcomes following the onset of the COVID-19 pandemic, we examined whether perceived COVID-19 threat mediates the association between distress tolerance and several psychological outcomes (i.e., psychological well-being, depression, anxiety, and stress). We also investigated whether social support moderates the indirect associations between distress tolerance and these psychological outcomes. We collected online survey data between April and July 2020 from individuals living in Canada (N = 139). Moderated mediation analyses indicated higher distress tolerance was associated with lower perceived COVID-19 threat which in turn was associated with higher psychological well-being, and lower depression and stress. Additionally, social support satisfaction enhanced the indirect association between distress tolerance and psychological well-being. Our findings may inform the design of interventions that promote psychological well-being after the onset of an adversity like the COVID-19 pandemic by presenting distress tolerance, perceived threat, and social support as targets for intervention. Future research should investigate the moderating role of different types of social support on the association between distress tolerance and psychological outcomes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".