Identifying pathways to increased volunteering in older US adults
Bibliographic record
Abstract
Abstract While growing evidence documents strong associations between volunteering and improved health and well-being outcomes, less is known about the health and well-being factors that lead to increased volunteering. Using data from 13,771 participants in the Health and Retirement Study (HRS)—a diverse, longitudinal, and national sample of older adults in the United States—we evaluated a large range of candidate predictors of volunteering. Specifically, using generalized linear regression models with a lagged exposure-wide approach, we evaluated if changes in 61 predictors spanning physical health, health behaviors, and psychosocial well-being (over a 4-year follow-up between t 0 ; 2006/2008 and t 1 ; 2010/2012) were associated with volunteer activity four years later (t 2 ; 2014/2016). After adjusting for a rich set of covariates, certain changes in some health behaviors (e.g., physical activity ≥ 1x/week), physical health conditions (e.g., physical functioning limitations, cognitive impairment), and psychosocial factors (e.g., purpose in life, constraints, contact with friends, etc.) were associated with increased volunteering four years later. However, there was little evidence that other factors were associated with subsequent volunteering. Changes in several indicators of physical health, health behaviors, and psychosocial well-being may predict increased volunteering, and these factors may be novel targets for interventions and policies aiming to increase volunteering in older adults.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".