MétaCan
Menu
Back to cohort
Record W2154766191 · doi:10.1177/0956797614531799

Purpose in Life as a Predictor of Mortality Across Adulthood

2014· article· en· W2154766191 on OpenAlexaff
Patrick L. Hill, Nicholas A. Turiano

Bibliographic record

VenuePsychological Science · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsCarleton University
FundersNational Institute of Mental HealthNational Institute on Aging
KeywordsLongevityPsychologyLongitudinal studyGerontologyLongitudinal dataAdult developmentBaseline (sea)Successful agingWorkforceLongitudinal sampleDemographyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Having a purpose in life has been cited consistently as an indicator of healthy aging for several reasons, including its potential for reducing mortality risk. In the current study, we sought to extend previous findings by examining whether purpose in life promotes longevity across the adult years, using data from the longitudinal Midlife in the United States (MIDUS) sample. Proportional-hazards models demonstrated that purposeful individuals lived longer than their counterparts did during the 14 years after the baseline assessment, even when controlling for other markers of psychological and affective well-being. Moreover, these longevity benefits did not appear to be conditional on the participants' age, how long they lived during the follow-up period, or whether they had retired from the workforce. In other words, having a purpose in life appears to widely buffer against mortality risk across the adult years.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.422
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), 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

Citations500
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePsychological ScienceSame topicPsychological Well-being and Life SatisfactionFrench-language works237,207