Do negative views of aging influence memory and auditory performance through self-perceived abilities?
Bibliographic record
Abstract
Memory and hearing are critical domains that interact during older adults' daily communication and social encounters. To develop a more comprehensive picture of how aging influences performance in these domains, the roles of social variables such as views of aging and self-perceived abilities need greater examination. The present study investigates the linkages between views of aging, self-perceived abilities, and performance within and across the domains of memory and hearing, connections that have never been examined together within the same sample of older adults. For both domains, 301 older adults completed measures of their views of aging, their self-perceived abilities and behavioral tests. Using structural equation modeling, we tested a hypothesized model in which older adults' negative views of aging predicted their performance in the domains of memory and hearing through negatively affecting their self-perceived abilities in those domains. Although this model achieved adequate fit, an alternative model in which hearing performance predicted self-perceived hearing also was supported. Both models indicate that hearing influences memory with respect to both behavioral and self-perception measures and that negative views of aging influence self-perceptions in both domains. These results highlight the importance of views of aging and self-perceptions of abilities within and across these domains.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".