THE INTERPLAY OF SOCIAL INTERACTION, POSITIVE AFFECT, AND SUBJECTIVE AGE IN OLDER ADULTS
Bibliographic record
Abstract
Abstract How old people feel compared to their actual age, their so-called “subjective age” (SA), is a central predictor of health and well-being across the life span. Felt age can influence lifestyle choices such as attending a social gathering. On the other hand, spending time with other people can elicit feelings of engagement and positive emotions, which could in turn, make people feel younger. The current study aimed to examine the reciprocal association between everyday time spent in social interaction and subjective age in old age. For this purpose, a sample of 108 older adults aged 65–92 years took part in a daily diary study. Over 14 days, participants reported daily social interaction time, positive affect, and subjective age. Multi-level models showed that previous day social interaction time was related to next day subjective age, whereas previous-day subjective age was not related to next-day social interaction time. In addition, the same-day association between social interaction time and subjective age was fully mediated by positive affect. Findings suggest that social connections might foster a sense of feeling young in older adulthood. Further research in this area may offer valuable insights into developing interventions aimed at enhancing subjective well-being and quality of life among older populations.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".