Age Discrimination During the COVID-19 Pandemic: Associations With Daily Well-Being
Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, ageist attitudes have been pervasive in public discourse, interpersonal relationships, and medical decision-making. For example, older adults have been portrayed as vulnerable while younger adults have been portrayed as reckless. The current study examined age discrimination during COVID-19 and associations with daily affect and physical symptoms. Positive events and age were examined as moderators. From March to August 2020, 1493 participants aged 18-91 (mean=40) in the U.S. and Canada completed surveys for seven consecutive evenings about discrimination, positive events, affect, and physical symptoms. Multilevel models controlled for age, race, income, education, sample (university students vs. community), and country of residence. Results indicated that individuals who reported more age discrimination had higher negative affect (b=36.44, SE=3.97), lower positive affect (b=-19.07, SE=4.10), and increased physical symptoms (b=3.85, SE=0.49; p<0.001 for all), compared to those with fewer reports of age discrimination. Within-persons, days with age discrimination were associated with higher negative affect (b=3.66, SE=1.36, p=0.008), lower positive affect (b=-2.60, SE=1.23, p=0.037), and increased physical symptoms (b=0.26, SE=0.11, p=0.02), compared to days on which age discrimination was not reported. Positive events moderated the between-person association of age discrimination with physical symptoms such that individuals with more age discrimination and more frequent positive events reported fewer daily physical symptoms than those with more age discrimination and less frequent positive events. Age did not moderate the associations. Age discrimination was associated with poorer daily well-being during the COVID-19 pandemic and may have long-term impacts on intergenerational solidarity and attitudes toward aging.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".