Ageism toward older adults during the COVID‐19 pandemic: Intergenerational conflict and support
Bibliographic record
Abstract
A cross-national representative survey in Canada and the U.S. examined ageism toward older individuals during the first year of the COVID-19 pandemic, including ageist consumption stereotypes and perceptions of older people's competence and warmth. We also investigated predictors of ageism, including economic and health threat, social dominance orientation, individualism and collectivism, social distancing beliefs, and demographics. In both countries, younger adults were more likely to hold ageist consumption stereotypes, demonstrating intergenerational conflict about the resources being used by older people. Similarly, young adults provided older people with the lowest competence and warmth scores, though adults of all ages rated older individuals as more warm than competent. Particularly among younger individuals, beliefs about group-based dominance hierarchies, the importance of competition, and the costs of social distancing predicted greater endorsement, whereas beliefs about interdependence and the importance of sacrificing for the collective good predicted lower endorsement of ageist consumption stereotypes. Support for group-based inequality predicted lower perceived competence and warmth of older individuals, whereas beliefs about interdependence and the importance of sacrificing for the collective good predicted higher perceived competence and warmth of older individuals. Implications for policies and practices to reduce intergenerational conflict and ageist perceptions of older individuals are discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".