The Psychological Impact of the COVID-19 Pandemic and Rumination as an Overlooked Psychopathological Mechanism
Bibliographic record
Abstract
For over a year, the global coronavirus disease 2019 (COVID-19) pandemic has been humanity’s greatest public health issue. During this time, clinicians and researchers worldwide have reported on the negative psychological impact due to safety measures that were implemented to curb the spread of this deadly disease (i.e., closing businesses, working from home, social distancing, quarantine, etc.). However, most of the published research about this topic has focused on complications to instrumental functioning (e.g., job loss, reduced income, shortages of supplies, increased child-care burdens, etc.), and how they lead to increased distress and reduced well-being. In contrast, little research has investigated how pandemic life has changed how we think about ourselves, our circumstances, and our futures, or how these cognitive factors have led to worsened mental health. In this article, we reviewed the literature on the psychological impact of the COVID-19 pandemic, with a major focus on the overlooked cognitive process of rumination (i.e., repetitive thinking about oneself and one’s problems). We explained how rumination translated pandemic-related stress into psychopathological outcomes such as increased depression, anxiety, post-traumatic stress, and substance misuse. We also recommended strategies for mitigating the negative effects of pandemic-related rumination and provided recommendations for future directions regarding pandemic-related mental health research.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".