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Record W4386256783 · doi:10.1177/00914150231196098

Changes in Purpose in Life and Low-Grade Chronic Inflammation Across Older Adulthood

2023· article· en· W4386256783 on OpenAlexafffund
Irene Giannis, Carsten Wrosch, Heather Herriot, Jean‐Philippe Gouin

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchConcordia University
KeywordsPercentileMedicineInflammationGerontologySuccessful agingYoung adultStressorAgeingDemographyInternal medicineClinical psychology

Abstract

fetched live from OpenAlex

Background: Older adults often experience an increase in low-grade chronic inflammation. Purpose in life could act as a protective factor as it is associated with beneficial health outcomes. Purpose in life may exert part of its adaptive function by promoting persistence in goal pursuit. During older adulthood, however, when many individuals experience an increase in intractable stressors and declining resources, the adaptive function of purpose could become reduced. Purpose: We examined whether the association between inter- and intra-individual differences in purpose in life and chronic inflammation differed across older adulthood. Method: We assessed four waves of data among 129 older adults (63–91 years old) across 6 years. Results: Hierarchical linear modeling demonstrated that within-person increases in purpose in life predicted reduced levels of chronic inflammation in early old age (25th percentile or 73 years, coefficient = −.016, p < .01), but not in advanced old age (75th percentile or 81 years, coefficient = .002, p = .67). Between-person differences in purpose were not related to chronic inflammation. Conclusions: These results suggest that greater within-person increases in purpose may protect health processes particularly in early old age but become less effective in advanced old age.

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 score0.509
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.424
Teacher spread0.375 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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