Dispositional Mindfulness and Self-Compassion Buffer the Effects of COVID-19 Stress on Depression and Anxiety Symptoms
Bibliographic record
Abstract
Abstract Objectives The COVID-19 pandemic has been associated with a dramatic rise in symptoms of depression and anxiety. Dispositional mindfulness (DM) and self-compassion (SC) have consistently been associated with psychological disorder symptoms and appear to buffer the effects of stress on depression and anxiety. Methods Across two studies (n = 888), we examined direct and indirect (moderation) relationships of DM, SC, COVID-19-related stress, and symptoms of depression and anxiety. We also examined the differential effects of several DM measures (FFMQ-15; FFMQ-39; MAAS) in the relationships of COVID-19 stress and psychological disorder symptoms. We recruited participants (Study 1 n = 350; 42.2% cis women; Study 2 n = 538; 44.3% cis women) online (MTurk) and examined associations of DM, SC, and COVID-19 stress, and emotional impact, and the moderating effect of DM and SC in the relationships of COVID-19-related fears, stress, emotional impacts, and psychological disorder symptoms. Results DM and SC were moderately and negatively correlated with COVID-19 fears and stress (correlations ranging r = − .14 to r = − .42) across studies. Study 1 moderation analyses demonstrated SC, but not DM (FFMQ-15), significantly moderated relationships of COVID-19 fears and emotional impacts with symptoms. Study 2 analyses demonstrated the FFMQ-39, but not the MAAS, significantly moderated relationships of COVID-19 stress and psychological disorder symptoms. Conclusions These results support the potential protective roles of DM and SC in disrupting pathological trajectories related to naturally elevated pandemic stress. Results also demonstrate the differential associations of several DM measures with COVID-19 stress. Future research should replicate such findings with more diverse samples and using various measures of self-compassion and risk metrics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".