Daily prosocial activities and well-being: Age moderation in two national studies.
Bibliographic record
Abstract
Prosocial activities, such as volunteering, predict better mental and physical health in late adulthood, but their proximal links to well-being in daily life are largely unknown. The current study examined day-to-day associations of prosocial activities with emotional and physical well-being, and whether these associations differ with age. We used daily diary data from the National Study of Daily Experiences (NSDE) II (n = 2,016; ages 33-84) and NSDE Refresher Study (n = 774; ages 25-75). Participants completed telephone interviews on 8 consecutive evenings regarding their prosocial activities (formal volunteering, providing unpaid assistance, providing emotional support), well-being (negative affect, stressors, positive events), and physical symptoms. On days when individuals participated in more formal volunteering or provided more unpaid assistance than usual, they experienced more stressors and positive events but no difference in the number of physical symptoms. Negative affect was reduced on volunteering days for older adults but increased for younger adults (NSDE Refresher). Providing emotional support was associated with higher same-day negative affect, more stressors, more positive events, and elevated physical symptoms. Compared to younger and middle-aged adults, older adults experienced less of an increase in stressors and positive events (NSDE II) and negative affect (NSDE Refresher) on days when they provided more emotional support than usual. These findings demonstrate that prosocial activities are associated with both costs (negative affect, stressors, physical symptoms) and benefits (positive events) for same-day well-being. Older age may protect against negative ramifications associated with prosocial activities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".