Longitudinal predictors of depression, anxiety, and alcohol use following COVID‐19‐related stress
Bibliographic record
Abstract
The COVID-19 pandemic imposed profound effects on health and daily life, with widespread stress exposure and increases in psychiatric symptoms. Despite these challenges, pandemic research provides unique insights into individual differences in emotion and cognition that predict responses to stress, with general implications for understanding stress vulnerability. We examined predictors of responses to COVID-19-related stress in an online sample of 450 emerging adults recruited in May 2020 to complete questionnaires assessing baseline stress and psychiatric symptoms, rumination, cognitive reappraisal use and intolerance of uncertainty. Stress and symptoms were re-assessed 3 months later (N = 200). Greater pandemic-related stressful events were associated with increases in symptoms of depression, anxiety and alcohol use severity. Additionally, individual differences in emotional and cognitive styles emerged as longitudinal predictors of stress responses. Specifically, greater rumination predicted increased depression. Reduced cognitive reappraisal use interacted with stress to predict increases in alcohol use. An unexpected pattern emerged for intolerance of uncertainty, such that stress was associated with increases in depression for those high in intolerance of uncertainty but increases in alcohol use at relatively low levels of intolerance of uncertainty. These results highlight unique vulnerabilities that predict specific outcomes following stress exposure and offer potential prevention targets.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".