How is volunteering associated with reduced mortality? A mediator-wide approach.
Bibliographic record
Abstract
OBJECTIVE: Volunteering has been repeatedly associated with reduced mortality in older adults, yet research examining the mechanisms explaining this association remains limited. We evaluated potentially modifiable mediators, and combinations of mediators, that may underlie the volunteering-mortality association. METHOD: We used prospective data from 9,962 participants in the Health and Retirement Study (2006-2018), a national, diverse, and longitudinal cohort of U.S. adults aged >50. We evaluated associations between volunteering at baseline (2008/2010), mediators at Wave 2 (2010/2012), and mortality between Waves 3 and 4 (2010-2016 for Cohort A, 2012-2018 for Cohort B). RESULTS: After adjusting for demographic confounders and mediators in the prebaseline wave (2006/2008), we observed evidence of mediation for those who volunteered ≥100 hr/year (vs. 0 hr/year) through combined physical health factors (proportion mediated [PM] = 49.56%, p = .004) and social factors (PM = 90.76%, p = .017) as well as through increased contact with friends (PM = 25.34%, p = .015) and helping friends/neighbors/relatives (PM = 25.12%, p = .018). However, there was less evidence of mediation through other proposed mediators. CONCLUSIONS: With further research, these results inform basic science, interventions, and policies by identifying potential mechanisms, which might become modifiable features of the volunteering experience, to promote longevity in our rapidly aging population. (PsycInfo Database Record (c) 2025 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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".