Who Benefits From Helping? Moderators of the Association Between Informal Helping and Mortality
Bibliographic record
Abstract
BACKGROUND: While informal helping has been linked to a reduced risk of mortality, it remains unclear if this association persists across different levels of key social structural moderators. PURPOSE: To examine whether the longitudinal association between informal helping and all-cause mortality differs by specific social structural moderators (including age, gender, race/ethnicity, wealth, income, and education) in a large, prospective, national, and diverse sample of older U.S. adults. METHODS: We analyzed data from the Health and Retirement Study, a national sample of U.S. adults aged >50 (N = 9,662). Using multivariable Poisson regression, we assessed effect modification by six social structural moderators (age, gender, race/ethnicity, wealth, income, and education) for the informal helping (2006/2008) to mortality (2010-2016/2012-2018) association on the additive and multiplicative scales. RESULTS: Participants who reported ≥100 hr/year of informal helping (vs. 0 hr/year), had a lower mortality risk. Those who engaged in 1-49 hr/year most consistently displayed lower mortality risk across moderators, while those who engaged in 50-99 and ≥100 hr/year only showed decreased mortality risk across some moderators. When formally testing effect modification, there was evidence that the informal helping-mortality associations were stronger among women and the wealthiest. CONCLUSIONS: Informal helping is associated with decreased mortality. Yet, there appear to be key differences in who benefits from higher amounts of informal helping across social structural moderators. Further research is needed to evaluate how the associations between informal helping and health and well-being are patterned across key social structural moderators.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".