Older adults’ perceptions of ageism before and during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: Studies assessing the effects of ageism on older adults during the COVID-19 pandemic suggest that perceiving ageism is associated with lower self-reported mental and physical health. Yet, it remains unknown whether these pandemic associations are distinct from pre-pandemic associations. The present study addressed this issue by controlling for pre-pandemic levels of ageism and mental and physical health in order to assess which pandemic-era experiences of ageism predict well-being in older people. METHOD: Both prior to and during the pandemic, 117 older adults completed measures of perceived ageism, self-perceptions of aging, subjective age, subjective health, and life satisfaction. RESULTS: During the pandemic, perceived ageism predicted lower subjective health and life satisfaction. However, when controlling for pre-pandemic measures, perceived ageism during the pandemic predicted only subjective health but not life satisfaction. Perceptions of continued growth positively predicted both measures across most analyses. CONCLUSION: The present findings suggest caution when interpreting the effects of ageism on well-being during the pandemic, as those associations may already have existed pre-pandemic. The finding that perceptions of continued growth positively predicted subjective health and life satisfaction suggests that promoting more positive self-perceptions of aging, along with combatting ageism in society, may represent important policy objectives.
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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 | 0.004 |
| 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 | 0.001 | 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".