Social interactions, wellbeing and health in the oldest old: what can we learn from daily life approaches?
Bibliographic record
Abstract
Canada, like many Western societies, has an aging population. Past research indicates that social interactions are meaningfully associated with physical and mental health. Unfortunately, very few studies have included individuals older than 85 years. This is important because the Oldest Old are the fastest growing segment of the Canadian population. This study is based on a subsample of the Berlin Aging Study (N = 83; Mean age = 81.1 years) who participated in an intensive, one-week time-sampling module. I examined how older adults regulate their social interactions by looking at two competing theories, the Convoy Model and the Socioemotional Selectivity Theory. I also examined how daily social interactions are associated with concurrent affective experiences and physical symptoms. This study found that even when controlling for cognition and overall health, age was associated with a decrease in social interactions in the present sample of older adults. Results showed that a more limited future time perspective in this sample was associated with a greater amount of time spent alone as well as a greater number of socially unpreferred situations. Furthermore, a greater amount of time spent alone was associated with lower levels of daily wellbeing. Results also showed that time spent alone was a risk factor for mortality. I discuss possible explanations as to why my findings complement past research by showing less favorable associations between social interactions and wellbeing in the Oldest Old.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".