From the COVID‐19 lockdown to the new normal: Two‐year changes in daily stress and positive event processes
Bibliographic record
Abstract
The emergence of the novel COVID-19 disease and the social distancing measures implemented to curb its spread affected most aspects of daily life. Past work suggests that during times of more severe stress, people respond to daily stressors with relatively higher negative affect. However, little is known about how people responded to daily stressors and positive events at different moments in time during the COVID-19 pandemic. Thus, we examined longitudinal changes in daily event-related affect (covariation of daily stressors or positive events with same-day affect) from Spring 2020 (wave 1) to 2022 (wave 2). The sample consisted of 324 adults aged 18-80 (mean = 52 years; 89% women) from the US and Canada who completed weeklong daily diaries at both waves. The results revealed improvements in affective well-being, stressor-related affect (i.e., smaller fluctuations in affect on stressor days vs. nonstressor days), and positive event-related affect (i.e., lower negative affect on days with vs. without positive events). Furthermore, as people gradually resumed their social activities from 2020 to 2022, people reported being exposed to an increased frequency of both stressors and positive events. This study highlights the potential influence of socio-historical phenomena, such as an ongoing pandemic, on the events that people encounter and how they emotionally respond to them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".