SELF-PERCEPTIONS OF AGING AND DAILY SOCIAL INTERACTIONS
Bibliographic record
Abstract
Abstract Age stereotypes can become internalized across the lifespan to influence one’s self-perceptions of aging (SPA). Stereotype embodiment theory suggests behavioral and stress response pathways through which SPA influence downstream health outcomes. Previous research has shown negative SPA predict lower social engagement, but little is known about whether this mechanism is evident at the daily level. Thus, the current study uses ecological momentary assessment over 14 days to investigate whether more negative SPA are associated with fewer interpersonal stressors and positive social interactions, and whether SPA attenuate differences in positive and negative affect on moments with versus without these events. Participants were 224 adults from British Columbia, Canada (ages 25-89, M = 46, 71% women). Multiple regression and three-level multilevel models controlled for demographic factors, depressive symptoms, and health conditions. Results showed that SPA did not predict exposure to interpersonal stressors and positive social interactions. People with more negative SPA had larger differences in negative affect on moments with versus without interpersonal stressors (simple slope for -1 SD SPA: b =.99, SE =.13, p <.001), compared to those with less negative SPA (simple slope for +1 SD SPA: b =.60, SE =.14, p <.001). SPA did not moderate associations between interpersonal stressors and positive affect, or between positive social interactions and positive and negative affect. Findings indicate that people with more negative SPA do not show a lack of daily social engagement but display greater negative affective stress responses, which have implications for downstream health.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".