What makes life purposeful? Identifying the antecedents of a sense of purpose in life using a lagged exposure-wide approach
Bibliographic record
Abstract
Prior research documents strong associations between an increased sense of purpose in life and improved health and well-being outcomes. However, less is known about candidate antecedents that lead to more purpose among older adults. We used data from 13,771 participants in the Health and Retirement Study (HRS) — a diverse, national panel study of adults aged >50 in the United States, to evaluate a large number of candidate predictors of purpose. Specifically, using linear regression with a lagged exposure-wide approach, we evaluated if changes in 61 predictors spanning physical health, health behaviors, and psychosocial well-being (between t0;2006/2008 and t1;2010/2012) were associated with purpose four years later (t2;2014/2016) after adjustment for a rich set of baseline covariates. Some health behaviors (e.g., physical activity ≥1x/week [β = 0.14, 95% CI: 0.09, 0.19]), physical health conditions (e.g., stroke [β = −0.25, 95% CI: −0.40, −0.10]), and psychosocial factors (e.g., depression [β = −0.21, 95% CI: −0.27, −0.15]) were associated with subsequent purpose four years later. However, there was little evidence that other health behaviors, physical health conditions, and psychosocial factors such as smoking, drinking, or financial strain, were associated with subsequent purpose. Several of our candidate predictors such as volunteering, time with friends, and physical activity may be important targets for interventions and policies aiming to increase purpose among older adults. However, some effect sizes were modest and contrast with prior work on younger populations, suggesting purpose may be more easily formed earlier in life.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".