Emotional responses to a global stressor: Average patterns and individual differences
Bibliographic record
Abstract
Major stressors often challenge emotional well-being—increasing negative emotions and decreasing positive emotions. But how long do these emotional hits last? Prior theory and research contain conflicting views. Some research suggests that most individuals’ emotional well-being will return to, or even surpass, baseline levels relatively quickly. Others have challenged this view, arguing that this type of resilient response is uncommon. The present research provides a strong test of resilience theory by examining emotional trajectories over the first 6 months of the COVID-19 pandemic. In two pre-registered longitudinal studies (total N =1147), we examined average emotional trajectories and predictors of individual differences in emotional trajectories across 13 waves of data from February through September 2020. The pandemic had immediate detrimental effects on average emotional well-being. Across the next 6 months, average negative emotions returned to baseline levels with the greatest improvements occurring almost immediately. Yet, positive emotions remained depleted relative to baseline levels, illustrating the limits of typical resilience. Individuals differed substantially around these average emotional trajectories and these individual differences were predicted by socio-demographic characteristics and stressor exposure. We discuss theoretical implications of these findings that we hope will contribute to more nuanced approaches to studying, understanding, and improving emotional well-being following major stressors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".