The Ups and Downs of Daily Life During COVID-19: Age Differences in Affect, Stress, and Positive Events
Bibliographic record
Abstract
OBJECTIVES: Past research has linked older age with greater emotional well-being and decreased reactivity to stressors, but it is unknown whether age-related advantages in emotional well-being are maintained in the wake of COVID-19. We examined age differences in exposure and affective reactivity to daily stressors and positive events in the first several weeks of the COVID-19 outbreak. METHODS: In March and April 2020, 776 adults from Canada and the United States aged 18-91 (mean age 45) years reported COVID-19 threats at baseline, then completed nightly surveys for 1 week about their daily stressors, positive events, and affect. RESULTS: Younger age predicted more concerns about the threat of COVID-19 across multiple domains, in addition to lower positive affect, higher negative affect, and less frequent positive events. Younger adults had more non-COVID-19 daily stressors and higher perceived control over stressors, but lower perceived coping efficacy than older adults. There were no age differences in the frequency of COVID-19 daily stressors nor perceived stressor severity. Younger adults had greater reductions in negative affect on days when more positive events occurred and greater increases in negative affect on days when non-COVID-19 stressors occurred. Age moderation was attenuated for negative affective reactivity to COVID-19 stressors. Age did not moderate positive affective reactivity to daily events. DISCUSSION: In the early weeks of the pandemic, older adults showed better emotional well-being and less reactivity to stressors but did not differ from younger adults in their exposure to COVID-19 stressors. Additionally, younger adults benefited more from positive events.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".