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Record W4409455603 · doi:10.1037/hea0001429

How is volunteering associated with reduced mortality? A mediator-wide approach.

2025· article· en· W4409455603 on OpenAlexafffund
Julia S. Nakamura, Baoyi Shi, Rachel Leong, Tyler J. VanderWeele, Eric Kim

Bibliographic record

VenueHealth Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMediatorPsychologySocial psychologyDevelopmental psychologyGerontologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.423
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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