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Record W4296445372 · doi:10.1016/j.ssmph.2022.101235

What makes life purposeful? Identifying the antecedents of a sense of purpose in life using a lagged exposure-wide approach

2022· article· en· W4296445372 on OpenAlexafffund
Julia S. Nakamura, Ying Chen, Tyler J. VanderWeele, Eric S. Kim

Bibliographic record

VenueSSM - Population Health · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of British Columbia
FundersUniversity of MichiganNational Institute on AgingMichael Smith Health Research BCU.S. Social Security Administration
KeywordsPsychosocialPsychological interventionGerontologyMedicineHealth and Retirement StudyPsychologyDepression (economics)Clinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.109
GPT teacher head0.387
Teacher spread0.278 · 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

Citations27
Published2022
Admission routes2
Has abstractyes

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