EVIDENCE FOR THE NEED TO DISRUPT AGING: IMPACT OF PERCEPTIONS OF AGING AND AGING STEREOTYPES
Bibliographic record
Abstract
This symposium explores the potential health effects of negative stereotypes of aging and self-perceptions on aging. Prior research has linked self-perceptions of aging to self-reported health, health care utilization, functional status and cognitive status. Menkin, Seeman and Sarkisian examine the association between expectations regarding aging and health and walking in four diverse minority communities in Los Angeles. Prior research suggests that under stereotype threat conditions, older adults tend to underperform on tasks that emphasize memory performance. Gallant and co-authors from Canada report on a meta-analysis that shows a significant and robust impact of the of stereotype threat effect on memory performance. The authors report on the ability of age and level of education to moderate in the impact of negative age stereotypes on memory performance in old age. Mejía and Gonzalez report on the extent to which couples in middle age and older adulthood share positive and negative self-perceptions of aging. Using data from the 2008 through 2014 waves of the Health and Retirement Study they report how these shared perceptions predict future functional limitations above and beyond individual. Finally Sun and Smith use data from the Health and Retirement Study and the Health Care Mail Survey to determine the association between self-perceptions of aging and the likelihood of delaying medical care and older adults’ decision-making processes when considering whether to seek medical care. Stevens concludes with an integrative discussion on AARP’s “disrupt aging” agenda that seeks to improve self-perceptions on aging and combat stereotypes on 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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".