Daily Ageist Attitudes, Subjective Age, and Memory Failures: A Multi-Country Integrated Data Analysis
Bibliographic record
Abstract
Abstract Ageist attitudes reflect negative views about others’ aging and are negatively associated with health and well-being. However, the ways in which ageist attitudes may fluctuate on a daily basis, and how those fluctuations are coupled with everyday memory functioning, are underexplored. Applying a cross-cultural perspective and leveraging 14-day daily diary data from the USA, Switzerland, Türkiye, Germany, India, Ukraine, Israel, Austria, Czech Republic, and Canada, the goal of the current study was to identify within-person fluctuations in daily ageist attitudes and connect those fluctuations to everyday memory functioning. Multilevel models were conducted across a mixture of integrated data analysis (for data that could be shared) and coordinated data analysis (standardized models applied to locally controlled data) to maximize the inclusion of countries. Estimates of within-person variability in daily ageist attitudes ranged from 13% (Germany) to 44% (India). Conditional models indicated varying patterns across countries. For example, in the US and Germany, daily increases in ageist attitudes were associated with increases in memory failures (reflecting lower levels of everyday memory functioning). However, in Canada and Türkiye, daily ageist attitudes were not associated with memory failures. The current results suggest that ageist attitudes move to varying degrees on a daily basis, and that their importance for everyday memory functioning may depend on cultural context.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".